MANAGING

TECHNOLOGICAL

INNOVATION

MANAGING

TECHNOLOGICAL

INNOVATION

COMPETITIVE ADVANTAGE

FROM CHANGE

THIRD EDITION

FREDERICK BETZ

John Wiley & Sons, Inc.

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Library of Congress Cataloging-in-Publication Data:

Betz, Frederick, 1937–

Managing technological innovation : competitive advantage from change / Frederick Betz.—3rd ed.

p. cm.

Includes

index.

ISBN 978-0-470-54782-3 (hardback); 9780-4-70-92756-4 (ebk.); 978-0-470-92757-1 (ebk.); 978-0-470-92758-8 (ebk.); 978-047-0-92759-5 (ebk.); 978-0-470-95154-5 (ebk.); 978-047-0-95171-2

(ebk.)

1.

Technological

innovations—Management. 2.

Research,

Industrial—Management. I.

Title.

HD45.B443 2011

658.5'14—dc22

2010030985

Printed in the United States of America

10 9 8 7 6 5 4 3 2 1

This book is dedicated to my wife, Nancy, and my children, Sara, Fred, and David.

CONTENTS

Preface

xi

I. TECHNOLOGY COMPETITIVENESS—

BUSINESS BASE OF INNOVATION

1 Technological Innovation

3

Timeline of Science, Technology, and Industrialization 4

Innovation Process

12

Technology and Wealth

17

Technical Savvy and Financial Savvy

20

Technical Personnel and Business Personnel

25

2 Innovation and Economy

27

Economic Scale of Innovations

29

Economic Long Waves

31

Measuring the Economic Impact of Technological Innovation in an Economy 37

Core Technologies of an Industry

41

Industrial Life Cycle

42

Industrial Standards and First Movers in a New Industry 44

Commodity Industry

48

3 Innovation and National Systems

51

Science Bases for Technology

54

Computer Technology

58

National Innovation System

60

Radical Innovation at the Macro and Micro-Levels

66

Radical Innovation and Next-Generation Product Lines 71

Circular Interactions in a National Innovation System 72

Performance of National Innovation Systems

75

vii

viii

CONTENTS

4 Innovation Research

77

Logic of Research for Innovation

79

Logic of Radical Innovation—Technology Research

87

Logic of Radical Innovation—Engineering Research

89

Logic of Radical Innovation—Commercialization

91

Costs of Innovation

94

5 Innovation and Corporate R&D

97

Institutionalization of Scientifi c Technology

103

Objectives of Corporate Research

106

Planning Incremental Innovation

108

Two Cultures—Research Laboratory and Business Division 109

Evaluating Corporate R&D

112

6 Innovation and Markets

115

Business Model—Price and Value

118

Designing a Product for a Market

124

Completing High-Tech Products for Applications

127

Market Positioning

130

Proprietary Technology

132

Market Niches

134

7 Innovation and Product Development

137

Market-Pull or Technology-Push Innovation

139

Incremental Innovation in Product Development

142

Delays in a Product Development Process

145

Product Quality

152

8 Innovation and Strategy

153

Micro-level Innovation in a Business

156

Top-down and Bottom-up Strategy in Innovation

161

Top-level Innovation Strategy

164

Mid-level Innovation Strategy—New High-tech

Business Ventures

167

Lower Level of Innovation Strategy—Technical Programs 171

Bottom Level of Innovation Strategy—Technical Projects 172

II. TECHNOLOGY STRATEGY—

TECHNICAL BASE OF INNOVATION

9 Integrating Technology and Business Strategy 177

Industrial Value Chain

179

Technology Strategy

186

CONTENTS

ix

Technology and Application Systems

192

Product-Line Planning

196

Product-Line Platforms

197

10 Inventing

Technology

199

Technology and Potentials of Nature

206

Next-Generation Technologies

212

Innovation Process of Next-Generation-Technology Products 214

Types of Technologies

217

11 Technology

Systems

220

Technology System

224

Physical Technology S-Curve

228

Technology Performance Parameters

230

Software Technologies

232

Planning Technology Discontinuities

235

Technological Progress

236

Systems Analysis for Alternative Technology Confi gurations 238

12 Product

Systems

241

Sikorsky’s Helicopter

241

Hardware Product System

245

Systems Analysis in Hardware-Product Design

246

Specialized Languages in Science

249

Computer-Assisted Networks

252

Logic and Language in Software Design

255

Design Bugs

256

Comparing Software and Hardware Innovation

258

13 Service

Systems

260

Services Science

260

Logic of Service Transactions

263

Strategic Business Vision—A Hardware Business or a Service Business?

266

Services and E-Commerce

270

Modeling Service Systems

272

Services Performance and Innovation

276

14 Biotechnology

Systems

278

Science Bases for Technology

285

Profi ts in Biotechnology

288

Research and Complexity of Nature

290

Progress in Biotechnology

293

University Research Centers for Science and Technology 297

x

CONTENTS

15 Ethics

and

Technology

299

Technology in Ethics as “Means and Ends”

302

Ethics and Regulation

305

Ethical Imperative and Technology Imperative

307

Control Systems for Intrinsically Evil Technologies 313

III. INNOVATION HANDBOOK

16 Innovation

Practice

317

Introduction 317

How to Manage Innovation Processes

317

How to Manage Research and Development (R&D)

323

How to Manage Product Development

329

How to Manage High-Tech Marketing

332

How to Manage Service Innovation

334

How to Manage Biotechnology Innovation

335

Summary—Overview of Innovation Theory

337

Bibliography 339

Index 349

PREFACE

INNOVATION PRACTICE

Our focus is on practicing successful innovation, which will be summarized, at the end, as a kind of guide to practice—a handbook of innovation. We will describe the theory for six practical management procedures in innovation: 1. How to manage innovation processes

2. How to manage research and development

3. How to manage product development

4. How to manage high-tech marketing

5. How to manage service innovation

6. How to manage biotechnology innovation

We will build toward these useful procedures—by examining the details of innovation theory—to summarize these procedures in chapter.

CASES AND THEORY

To get to practice, one must review theory in cases of actual practice. What is different about this book is that it not only tells stories—stories about technology and business—but it also deepens these stories with a theory of innovation. This is different because grounding theory in research has not often been a typical practice in the business literature. Instead, the usual literary style is to use case studies of best practice—what some company did at some time—in other words, a business process. This has been called the Harvard Business School case method.

But case studies by themselves may not develop or validate theory. They may be not of much practical use, because how something worked in one company is not a complete story. It does not necessarily tell why. And it is not only the “how” of xi

xii

PREFACE

companies doing something (a best practice) but the “why” of it working (or failing). The “why” can only be found in research on such cases of real practice—but relevant to theory. It is research-grounded theory that is useful. What worked for one company may or may not work for another company—nor even work for the same company in the future. We describe all cases in this book in a theoretical framework—cases relevant to theory and theory in the context of cases.

For example, a famous case of best practice in product innovation was the use of “concurrent engineering design,” dramatically illustrated by the development of the Ford Taurus car in 1981. This development was extensively written about and studied in the business literature. Lew Veraldi, the Ford project leader wrote:

[T]he team sought out the best vehicles in the world and evaluated more than 400 characteristics on each of them to identity those vehicles that were the best in the world for particular items. These items ranged from door closing efforts, to the feel of the heater control, to the under-hood appear-ance. The cars identified included BMW, Mercedes, Toyota Cressida and Audi 5000. Once completed, the task of the Taurus Team was to implement design and/or processes that met or exceeded those “Best Objectives” (Veraldi 1988, 5).

The Taurus car saved the Ford Motor Company from bankruptcy in the early 1980s. Yet in 2008, Ford terminated the model, as it had not sold well for years and Ford had allowed it to become technically obsolete. Why? Why had Ford not improved Taurus over the years? It could have used that best practice of “concurrent engineering design” to continually improve the car into a brand-recognized, quality product. And a competitive Taurus might have saved Ford from its near bankruptcy in 2007–2008, when Ford desperately needed a brand-recognized quality compact car—stylish, fuel efficient, high performance. It is the whys in the business world that constitute theory. Why is concurrent engineering design insufficient as a business process to maintain continual product improvement in a product and its technologies? This is one of many questions we address in our cases and theory of innovation.

Briefly, the “why” of the Taurus story is that subsequent CEOs at Ford were not committed to innovation as a competitive strategy, only to business acquisitions.

After the success of Taurus in the 1980s, in the 1990s Ford bought other brands of cars, such as Volvo and Jaguar. But later in the 2000s, facing bankruptcy, Ford sold these brands off—after losing a great deal of money on them. In 2009, Jaguar, once a British car and then an American Ford car, became an Indian car. In 2010, Volvo, once a Swedish car and then an American Ford car, became a Chinese car. This is an example of a failure of proper innovation strategy—a lack of proper top-down and bottom-up technology implementation at Ford—and theory that we will discuss in Chapter 8, “Innovation and Strategy . ”

Grounded theory is what counts—both the how and why. Here we will use stories of real practice—some “best practice” and some “worst practice”—to ground innovation theory and/or raise challenges to theory—both the how and why of successful innovation.

PREFACE

xiii

THE FIELD OF ENGINEERING MANAGEMENT AND TECHNOLOGY

Technological innovation is a complicated story and theory, because it has both a business side and a technical side. The business side focuses on using technological progress to design, produce, and market new high-tech products/services/processes.

The technical side focuses on inventing new technology and developing its performance sufficiently to embed in high-tech products/services/processes. Personnel with business educations normally perform the business side of innovation, whereas personnel with engineering or science or computer science or mathematics educations normally perform the technical side of innovation. As business processes can be complicated and managing them can be challenging, so, too, can technical processes be complicated, and managing them challenging.

The study of managing technical processes began with the field of engineering management (EM)—so named since engineers were predominantly the technical personnel who develop new products and production processes. However, as information technology expanded in the second half of the twentieth century, other kinds of technical personnel exceeded the numbers of engineers in a business, and these were personnel in the computer fields: programmers, computer scientists, mathematicians. So the field of engineering management was broadened to include all kinds of technical personnel involved in all kinds of technologies. Accordingly, the field was renamed management of engineering and technology—or management of technology (MOT), for short. The idea that is central to MOT is that technology strategy and business strategy should be integrated for technology to provide a competitive edge to business. MOT can be divided into two classifications: 1.

Empirical—EM/MOT

is

descriptive, describing actual historical patterns of change in science, technology, and economy.

2. Theoretical—EM/MOT is also prescriptive, developing useful concepts, techniques, and tools for managing future change in science, technology, and economy.

For the first half of the twentieth century, technological progress was primarily driven by the invention and production of physical goods. But as the second half of twentieth century evolved, dramatic new progress in information technology and in molecular biology fostered economic progress in industries of information, services, and biotechnology. This third edition of this book continues to broaden innovation study on a proper breadth across all the kinds of technologies—material, power, biological, and informational technologies.

ORGANIZATION OF THE BOOK

The questions we will pose and answer include the following: 1. How is innovation organized as a process?

2. What is technology?

xiv

PREFACE

3. What kinds of technologies are there?

4. Why is progress in any technology eventually finite?

5. How does technological progress impact a nation?

6. How can innovation strategy be formulated for a nation?

7. How does technological progress impact a business?

8. How can a manager identify technologies relevant to the future of a business?

9. How should high-tech research and development projects be managed?

10. How should innovation strategy be formulated in a business?

11. How does the innovation differ in hardware, software, sciences, and biotechnology?

12. What is the ethical context of technology?

These questions cross both the technical and business aspects of a business system. Any business must be run as a system: a business system developing and designing products/services, producing products/services, and selling these into a market. The technical functions of a business system emphasize the “upstream part of the operations,” doing the research and development of products and production.

The business functions of a business system emphasize the “downstream part of the operations,” doing the financial, sales, and marketing activities.

Accordingly, this book was written to cover the concepts that bridge and connect the technical and business aspects of a business system. The chapters of the text are so divided between the two sides:

1. Part I, Technology Competitiveness (Chapters 1 to 8), covers the business side of innovation.

2. Part II, Technology Strategy (Chapter 9 to 15), covers the technical side of innovation.

Part III is the Innovation Handbook, covered in Chapter 16 as Innovation Practice.

MBA AND EM/MOT DEGREES

The two aspects of business and technology provide different foci for graduate management programs—either in engineering schools (offering EM or MOT degrees) or in business schools offering MBA degrees. This book can be used to provide an overview of innovation in either kind of program.

For example, as sketched in Figure 0.1, Nguyen Hoang Chi Duc has compared the two approaches: (1) in an MBA program, focusing on the business aspects of a business system, or (2) in an EM/MOT program, focusing on the technical aspects of a business system (Nguyen 2010).

In any academic degree program, the study of processes in business should include both business and technical aspects. However, because of differing intellectual

PREFACE

xv

1. INNOVATION

2. RESEARCH

3. DEVELOPMENT

ENGINEERING

4. DESIGN

SCHOOL

5. PROJECTS

6. MANUFACTURE

1. INNOVATION

A

EXTENDING

7. PATENTS

2. RESEARCH

MOT/MB

8. CUSTOMER SERVICE

3. DEVELOPMENT

9. PROJECTS

4. DESIGN

10. INFORMATION SYSTEM

5. MANUFACTURE

11. STRATEGY

6. PATENTS

12. FINANCE

7. CUSTOMER SERVICE

13. MARKETING

8. PROJECTS

EM/MO

14. SALES

9. INFORMATION SYSTEM

T

USINESS SYSTEMB 15. DISTRIBUTION

FOCUSING

16. HUMAN RESOURCES

17. ORGANIZATION

1. INNOVATION

18. LEADERSHIP

2. RESEARCH

19. ENTREPRENEURSHIP

3. DEVELOPMENT

4. DESIGN

EXTENDING

5. PROJECTS

6. MANUFACTURE

MANAGEMENT

7. PATENTS

TECHNO-MB

SCHOOL

8. CUSTOMER SERVICE

9. PROJECTS

A

1. CUSTOMER SERVICE

10. INFORMATION SYSTEM

2 . INFORMATION SYSTEM

11. STRATEGY

3. STRATEGY

12. FINANCE

4. FINANCE

13. MARKETING

5. MARKETING

14. SALES

USINESS SYSTEM

6. SALES

15. DISTRIBUTION

B

MBA

7. DISTRIBUTION

16. HUMAN RESOURCES

8. HUMAN RESOURCES

17. ORGANIZATION

18. LEADERSHIP

FOCUSING

9. ORGANIZATION

10. LEADERSHIP

19. ENTREPRENEURSHIP

11. ENTREPRENEURSHIP

Figure 0.1 MBA and EM/MOT programs

priorities, MBA programs in business schools tend to emphasize the business aspects: customer service, information system, strategy, finance, marketing, sales, distribution, human resources, organization, leadership, entrepreneurship. Conversely, MOT/EM programs (in departments of industrial engineering or in engineering management) in engineering schools tend to emphasize the technical aspects: innovation, research, development, design, manufacture, patents, customer service, information system, strategy. The purpose of an overview course on innovation in either program is to assist in extending either program toward a more complete view of the business system (business + technical).

ACKNOWLEDGMENTS

Finally, I especially acknowledge the help and intellectual ideas of many of my friends in this field: John Aje, Robert Argenteri, David Bennet, Elias Carayannis, Ritchie Herink, Yassar Hosni, Nguyen Hoang Chi Duc, Dundar Kocaoglu, Tarek Khalil, Jay Shattuck, Rias Van Wyk, and many others. I am also indebted to the other scholars and practitioners who have contributed to building this important academic field for understanding the connections of science to technology to economy, in order to better manage innovation.

Frederick Betz

I.

TECHNOLOGY

COMPETITIVENESS—

BUSINESS BASE OF

INNOVATION

1

TECHNOLOGICAL

INNOVATION

INTRODUCTION

Technological innovation is, without doubt, the major force for change in modern society

—

a force of knowledge. There are two basic issues about knowledge: (1)

creating knowledge and (2) applying knowledge. The first is the domain of science and the second is the domain of technology. This book focuses on the second domain, technology — the application of knowledge.

But there is a difference between technology and scientific technology. The world has had technology since the dawn of the Stone Age — when humanity ’ s predecessors, the hominoids, chipped stones into tools. In fact, the history of humanity may be classified into ages of technologies — the Stone Age, the Bronze Age, the Iron Age.

But what age shall we call our age, the modern age? As a reflection of its influence on society, a most descriptive term would be the age of science and technology.

In historical fact, the transition from antiquity to modern arose from the origin of science and from thence all the technologies derived from science — scientific technology. Technologies are the “ how ” to do something; science is the “ why ” of something. So scientific technologies are both the how and why something can be done in nature. Science understands nature. Scientific technology manipulates nature. And this is good or bad — depending what we do to nature.

The basis for our modern age, characterized by so many new technologies and rapid technological progress, is the science base of modern technologies —

scientific technology.

These are the modern connections — from science to technology to economy.

Scientific technologies provide the basis for new high - tech products, services, and processes of modern economic development. The study of these connections is 3

4

TECHNOLOGICAL

INNOVATION

the focus of the topic of technological innovation . The field of management of technology (MOT) studies the principles of innovation, which describe the general patterns and principles in technological progress — the theory of innovation . As in any social theory, the context of the application of the theory affects the generality and validity of theory. So, too, with innovation theory, successful innovation is context dependent, and that theory needs to be illustrated and bounded by the contexts of actual historical examples of innovation. The first cases we will examine are the innovations of the Internet, Google, Xerography, and the Altos PC.

There is a “ big picture ” of innovation — science and technology and economy —

and the historical industrialization of the world. There is also a “ smaller picture ” of innovation — businesses and competition and high - tech products/services. Innovation operates at two levels: macro and micro. We begin by looking at the macro level by asking the following questions:

• How does innovation create wealth?

• How does innovation transform scientific nature into economic utility?

• Who makes innovation?

TIMELINE OF SCIENCE, TECHNOLOGY, AND INDUSTRIALIZATION

Historically, the grand theme of innovation has been the invention of major new technologies and their dramatic impacts — changing all of a society and all societies. This story of the modern world has been both dramatic and ruthless. The drama has been the total transformation of societies in the world from feudal and tribal to industrial.

The ruthlessness in technological change has been its force, which no society could resist and which has been called a technology imperative . Technological change has been irresistible — in military conflict, in business competition, and in societal transformations. (The latest of these imperatives is the globalization of the world, driven by the Internet. For example in 2010, the government of China decided that it would control Google in China or Google would have to leave China.) Going back to the 1300s and 1400s in Europe, there were two technological innovations that provided the technical basis for the beginning of our modern era: the gun and the printing press. They were not scientific technologies, but only technologies; as scientific technologies were to begin later in the 1700s with the steam engine and the Bessemer steel process. The technologies of the gun and printing press had been invented in China, but were innovated in Europe. This is an important distinction between invention only and innovation as both invention and commercialization. The gun was improved and commercialized in Europe, and it was so potent a weapon that the gun ended the ancient dominance of the feudal warrior — a military technology imperative. In parallel, the improvement and commercialization of the printing press made books relatively inexpensive and fostered the secularization of knowledge. This combination of the rising societal dominance of a mercantile class ( capitalist ) and the deepening secularization of knowledge ( science ) are hallmarks of a modern society. After the fifteenth century, the political TIMELINE OF SCIENCE, TECHNOLOGY, AND INDUSTRIALIZATION 5

histories of the world became stories of the struggles between nations and peoples, wherein the determining factor has been the military and economic superiorities made possible by new scientific technologies .

When and how did scientific technologies begin? Figure 1.1 summarizes the major historical milestones of changes in science, technology, and economy.

Science began in European civilization in the seventeenth century, when Isaac Newton combined new ideas of physics (from Copernicus, Brahe, Kepler, and Galileo) with new ideas in mathematics (from Descartes and others) to develop the mathematical theory of space, time, and forces, the Newtonian paradigm of physics. In the next eighteenth century, these new ideas were further developed into the new scientific disciplines of physics, chemistry, and mathematics. The nineteenth and twentieth centuries had dramatic advances in these disciplines, along with the founding of the scientific discipline of biology By the end of that twentieth century the physics of the small parts of matter and the largest spaces of matter was established, the chemistry of inanimate and animate matter was established, the 1600s

1700s

1800s

1900s

2000s

1600 – Future

Internationalization of science

Scientific disciplines

1800 To 1950

of physics, chemistry,

National

biology, and mathematics

Industrializations

Industrializations of

Europe, North America,

1950 to Future

Japan … and engineering

Globalization of

disciplines of civil,

industrialization

mechanical, electrical,

chemical engineering

Industrializations of Asia

1750 to 2000

National progress in technology

2000 to Future

Technologies of steam power,

Globalization of

steel, textiles, railroads,

steamships, chemicals, telegraph,

innovation

telephone, radio, automobiles,

airplanes, etc.

Technologies of IC chip

electronics, computers

biotechnology

Figure 1.1 Timelines of science, technology, and economy 6

TECHNOLOGICAL

INNOVATION

molecular biology of the inheritance of life was established, and the computational science of mind and communication was being extended. All this began in and took place in an international context from its very beginnings, so that one can see the four hundred years of the origin and development of science as a period of the internationalization of science as well.

In contrast to this international context of science, the economic and technological developments occurred within purely national contexts. Each nation industrialized on a national basis and in competition with other nations. From about 1765 to 1865, the principal industrialization occurred in the European nations of England, France, and Germany. From 1865 to about 1965 (the second hundred years) other European nations began industrializing, but the principal industrialization shifted to North America.

By the 1940s, the industrial capacity in the United States alone was so large and innovative as to be a determining factor in the conclusion of the Second World War.

For the second half of the twentieth century, U.S. industrial prowess continued, and European nations rebuilt their industrial capabilities that had been destroyed by that war. From 1950 to the end of the twentieth century, several Asian countries began emerging as globally competitive industrial nations: Japan, Taiwan, South Korea, and Singapore.

After the economic reforms in China by Deng Xiaoping, China began to rapidly industrialize, quickly becoming a major manufacturing nation in the world in the twenty - first century. India also, throwing off decades of socialism, began to further industrialize, particularly in the information technologies. All other Asian countries were also moving toward globally competitive capability: Vietnam, Thailand, Philippines, Malaysia, and Indonesia. (Note that historically, Asian industrialization actually begun in Japan in 1865 — but was diverted principally to a military - dominated society. After the Second World War, a reindustrialization of Japan occurred.)

In summary, we see a pattern of three hundred years of world industrialization in which different regions of the world began to develop globally competitive industrial industries:

• First hundred years

(1765 – 1865) — Europe

• Second hundred years (1865 – 1965) — North America

• Third hundred years (1965 – 2065) — Asia

As with industry, the patterns of developing technological progress was also on a national basis, with technology viewed as a national asset. However, the pace at which modern technology was transferred around the world increased in the second half of the twentieth century, so that when the twenty - first century began, a new pattern of change in the modern world emerged, the beginning of the globalization of technological innovation .

Thus, by the time the twentieth century ended, there was worldwide appreciation that science and technology were critical to international economic competitiveness.

TIMELINE OF SCIENCE, TECHNOLOGY, AND INDUSTRIALIZATION 7

World markets and industrial production had become global affairs. In 1980, global trade had already accounted for about 17 percent of total economic activity, increasing by 2000 to 26 percent, worldwide (Kahn 2001). The economic mechanism of the global trade were multi - national firms: “ Global trade increased rapidly throughout the 1990s, as multinational companies shipped products through a global supply chain that minimized costs and maximized efficiency with little regard for national borders ( Kahn 2001, p. A4).

But while the entire world was industrializing, it is important to make clear the difference between globally effective and ineffective industrialization. For example, Michael Porter identified several factors in effective national competitive structures: political forms, national and industrial infrastructures, domestic markets, and firm strategies. Also, an effective national research infrastructure was necessary for effective industrialization. Elements of necessary national infrastructure include educational systems, police and judicial systems, public health and medical systems, energy systems, transportation systems, and communication systems.

Economic development of all nations in this global context remains an important problem. Technological progress has enabled some but not yet all nations to develop economically.

One important research feature for national competitiveness lies in proper strategic interactions between universities and high - tech companies in the nation. For example, Peter Gwynne described some of the science and technology parks developed in Singapore, South Korea, and Taiwan to build their science and technology infrastructure for high - tech industries (Gwynne 1993). The model for such science and technology parks was the Silicon Valley in northern California in the United States for the building of the chip industry and personal computer industry. Stanford University and the University of California at Berkeley both played an important role in the rise of Silicon Valley, along with venture capital firms in growing high - tech industries there (e.g., computer chips, computers, and multimedia).

CASE STUDY:

Innovation of the Internet

Let look at our first case, the innovation of the Internet, a major technological innovation at the end of the twentieth century. The Internet is both an idea of a technology and an implementation of the technology as a connected set of businesses, as sketched in Figure 1.2 . The Internet is constructed of many, many units that continually are connecting into or out of the network at different time — either as businesses directly connecting to the Internet or as home

-

based customers

connecting to the Internet through connection services. The operations of this functional system enable users (as businesses or as consumers) to log onto the Internet through their respective personal computers or Web servers, and thereby communicate from computer to computer.

8

TECHNOLOGICAL

INNOVATION

Implementation of the

Internet as an economic

Business 1

functional system

Home 1

PC

PC

PC

Intel

Server

Microsoft

Modem

Modem

PC

Cisco

Internet

R

I

R

o

service

n

o

u

provider

t

u

t

e

t

e

e

r

TCP/IP

Addresses

r

Server

LAN

r

n

Standards

e

t

Home 2

Business 2

PC

PC

B

a

c

Server

PC

Modem

Modem

k

b

PC

o

Internet

R

n

service

o

R

e

provider

u

o

t

u

e

t

Server

r

e

LAN

r

Figure 1.2 Architecture of the Internet

The technological innovation of the Internet was commercialized by a set of business:

• Sale of personal computers (e.g., Dell, Mac), containing a microprocessor (e.g.,

•

Intel CPU), an operating system (e.g., Microsoft Windows), and a modem An Internet service provider (e.g., AOL, Vodaphone, Comcast, etc.)

• A server and router (e.g., Cisco, Dell, IBM)

•

A local

-

area network or wide

-

area network in a business (e.g., Cisco,

Erickson)

•

An Internet backbone communications system (e.g., AT

&

T, Sprint,

Vodaphone)

• Internet search services (e.g., Google, Yahoo)

The invention of Internet technology can be traced to an earlier computer network then called ARPAnet. ARPAnet

’

s origin, in turn, can be traced to

Dr. J. C. R. Licklider. In 1962 Licklider was serving in the Advanced Research Projects Agency (ARPA), a government agency funding military research projects for the U.S. Department of Defense. At ARPA he headed research into how to use TIMELINE OF SCIENCE, TECHNOLOGY, AND INDUSTRIALIZATION 9

computers for military command and control (Hauben 1993). As an ARPA research program officer, Licklider began funding projects from ARPA on networking computers. He wrote a series of memos on his thoughts about networking computers, which were to influence the computer science research community.

About the same time, a key idea in computer networking was derived from research of Leonard Kleinrock. Kleinrock had the idea of sending information in packaged groups, or packet switching . He published the first paper on packet switching in 1962 and a second in 1964. Packet switching enabled computers to send messages swiftly in bursts of information — without tying up communication lines very long and thus vastly increasing communication capacities of network lines.

In 1965, Lawrence Roberts at the Massachusetts Institute of Technology (MIT) connected a computer at MIT to one in California through a telephone line. In 1966, Roberts submitted a proposal to ARPA to develop a computer network for a military need (defense) for protection of U.S. military communications in the event of a nuclear attack. This was called the Advanced Research Projects Administration Network, or ARPAnet, and was to develop, eventually, into the Internet.

Robert W. Taylor had replaced Licklider as program officer of ARPA

’

s

Information Processing Techniques Office. Taylor had read Licklider ’ s memos and was also thinking about the importance of computer networks; and he also approved the funding of projects from ARPA on computer networks: “ The Internet has many fathers, but few deserve the label more than Robert W. Taylor. In 1966 . . .

(At ARPA), Taylor funded the project with the idea for Internet ’ s precursor, the ARPAnet ” (Markoff 1999).

Earlier, Taylor had been a systems engineer at the Martin Company and next a research manager at the National Aeronautics and Space Administration (NASA).

There he had approved projects funded by NASA for advances in computer knowledge. Then he went to ARPA and became interested in the possibility of communications between computers. In his office, there were three terminals, connected to time - sharing computers in three different research programs that ARPA was supporting. He watched communities of people build up around each of the time -

sharing computers: “ As these three time - sharing projects came alive, they collected users around their respective campuses . . . [but] . . . the only users . . . had to be local users because there was no network . . . . The thing that really struck me about this evolution was how these three systems caused communities to get built.

People who didn ’ t know one another previously would now find themselves using the same system ” (Markoff 1999, C38).

Taylor was also struck by the fact that each time - sharing computer system had its own commands: “ There was one other trigger that turned me to the ARPAnet.

For each of these three terminals, I had three different sets of user commands . . . .

I said . . . It obvious what to do: If you have these three terminals, there ought to be one terminal that goes anywhere you want to go where you have interactive computing. That idea is the ARPAnet ” (Markoff 2000).

In 1965, Taylor proposed to the head of ARPA, Charlie Herzfeld, the idea for a communications computer network, using standard protocols. Next in 1967, a

10

TECHNOLOGICAL

INNOVATION

meeting was held by ARPA to discuss and reach a consensus on the technical specifications for a standard protocol for sending messages between computers.

The packet - switching node used to connect the computer network was called the Interface Messaging Processor (IMP).

Using these to design messaging software, the first node on the new ARPAnet was installed on a computer on the campus of the University of California at Los Angeles. The second node was installed at the Stanford Research Institute, and the ARPAnet began to grow from one computer research setting to another. By 1969, ARPAnet was up and running. Taylor left ARPA to work at Xerox ’ s Palo Alto Research Center.

J.C.R. Licklider

Leonard Kleinrock

Robert W. Taylor

( http://en.wikipedia.org . 2009)

As the ARPAnet grew, there was the need for control of the system. It was decided to control it through another protocol, called Network Control Protocol (NCP); and this was begun in December 1970 by a private committee of researchers called the Network Working Group.

The ARPAnet grew as interconnected independent multiple sets of smaller networks. In 1972, a new program officer at ARPA, Robert Kahn, proposed an advance of the protocols for communication, as an open architecture accessible to anyone.

It was formulated as the Transmission Control Protocol/Internet Protocol (TCP/IP), and became the standard upon which the Internet would later be based.

While the ARPAnet was being expanded in the 1970s, other computer networks were being constructed by other government agencies and universities. In 1981, the National Science Foundation (NSF) established a supercomputer centers program. The program funded computer centers at universities, which purchased supercomputers and allowed researchers to run their programs on these supercomputers. Therefore, researchers throughout the United States needed to be able to connect to the five NSF - funded supercomputer centers to conduct their research.

NSF and ARPA began sharing communication between the networks, and the possibility of a truly national Internet was envisioned. In 1988, a committee of the National Research Council was formed to explore the idea of an open, commercialized Internet. They sponsored a series of public conferences at Harvard ’ s Kennedy School of Government on the “ Commercialization and Privatization of the Internet. ”

TIMELINE OF SCIENCE, TECHNOLOGY, AND INDUSTRIALIZATION 11

In April 1995, NSF stopped supporting its own NSFnet “ backbone ” of leased communication lines, and the Internet was privatized. The Internet grew to connect more than 50,000 networks all over the world. On October 24, 1995, the Federal Network Council defined the Internet as follows:

• Logically linked together by a globally unique address space based on the Internet Protocol (IP)

• Able to support communications using the Transmission Control Protocol/

Internet Protocol (TCP/IP) standards

One can see in this case that the innovation of the Internet occurred at a macro - level of a nation — motivated by researchers seeking ways to have computers communicate with each other. This was a new kind of functional capability in computation. The invention of the computer networks required the creation of nine technical ideas, and together these constitute the technology of the Internet:

1. Computer - to - computer communications

. Computers would be electroni-

cally connected to each other.

2. Packet - switching . Computer messages should be transmitted in brief, small bursts of electronic digital signals, rather than a continuous connection used in the preceding human voice telephone system.

3. Standards . Formatting of the digital messages between computers needed to be standardized to send message packets. These open standards became the Internet ’ s (TCP/IP) standards.

4. Routing . A universal address repository would provide addresses so computers could know where to send messages to one another.

5. HTML . Web pages would be written in a language that allowed computers to link to other sites.

6. www

. World Wide Web registration of directory of Web sites would allow sites to be connected through the Internet.

7. Browser . Software on computers would allow users to link to the World Wide Web (www) and find sites.

8. Search engine . Software would allow users to search for relevant sites and link to them.

9. Web page publication . Software facilitates the preparation and publication of sites on the Internet.

A technology consists of the technical ideas that together enable a functional transformation. The functional transformation of the Internet technology provides communication between and through computers.

12

TECHNOLOGICAL

INNOVATION

All these technical ideas together enabled the new Internet technology. Next commercialization of the new technology occurred when NSF transferred network management from the government to private companies. Thus, the innovation of the Internet did occur in a common pattern of technological innovation — first the invention of new technical ideas (as ARPAnet) and second the commercialization of new products and services embodying these new ideas (in the privatization of the Internet).

Technological innovation consists of both the invention and commercialization of a new technology.

INNOVATION PROCESS

How should we think about the process of innovation? In the “ big picture, ” we start with the nature and them turn to transforming knowledge of nature into economic utility. The term nature is the scientific term for the entire observable world in which we exist. All technologies involve manipulating nature to create products and services useful in an economy.

For example, in the Internet innovation, a government agency, ARPA, funded university researchers, who used the nature of electronics (electrical signal propaga-tion), the nature of information (communication standards), and the nature of logical computation (computers) in order to invent computer - to - computer communication technology. If one examines any technology, one will see that some kind of nature (material, biological, or social) is being used (manipulated). Accordingly, we can describe the innovation process as the way knowledge of nature (science) can be connected to technology (manipulation of nature), which then can be connected to use of nature (economy). This is sketched in Figure 1.3 .

1. Research . In technological innovation, one begins with nature. Knowledge about nature — what it is (discovery) and how it operates (explanation) — is gained by science

through act of research. Scientists are the principal kinds of people who as researchers study the knowledge of nature.

Research

Invent

Commercialize

Market

Nature

Science

Technology

Economy

Utility

Exist

Discover

Manipulate

Embed

Use

naturally

nature

nature

nature

nature

The world as material

The world as money

Figure 1.3 Innovation process

INNOVATION

PROCESS

13

2. Invent . Scientific knowledge of nature is used as a knowledge base by technologists to create new technologies (manipulations of nature) through the act of invention . Technologists are usually scientists or engineers or other technical personnel.

3. Commercialize . Technical knowledge is embedded within a product/service/

software through the act of design . In a business, engineers use technological knowledge to develop and design new high - tech products or services or processes. Commercialization is the act of connecting (embodying) technology into the products/services/processes. In the product/service development procedures of a business, technical and business personnel work together in innovation teams.

4. Market

. A business competes by selling high

-

tech products/services in a

marketplace, earning income — which become profits when the sales prices exceed the costs of producing products/services.

For this representation of the innovation process, we should formalize the defini-tions of the key term. We can do this in the following way, by carefully defining each term with regard to the idea of nature:

Basic Definitions for Innovation

1. Nature is the totality of the essential qualities of the observable phenomena of the universe.

In the communities of scientists and engineers, the term nature is commonly used to indicate essential qualities of things that can be observed in the entire universe.

2. Science is the discovery and explanation of nature.

The derivation of the term science comes from the Latin term scien-tia, meaning “ knowledge. ” However, the modern concept of scientific research has come to indicate a specific approach toward knowledge, which results in discovery and explanations of nature.

3. Technology is the knowledge of the manipulation of nature for human purpose.

The technical side of the idea of technological innovation

—

invention — derives, of course, from the idea of technology. The historical derivation of the term technical comes from the Greek word, technikos, meaning “ of art, skillful, practical. ” The portion of the suffix ology indicates a “ knowledge of ” or a “ systematic treatment of. ” Thus, the derivation of the term technology is literally “ knowledge of the skillful and practical. ” This meaning of technology is a common definition of the term — but too vague for expressing exactly the interactions (continued)

14

TECHNOLOGICAL

INNOVATION

between

science,

technology, and economy. The

“

knowledge of the

skillful and practical ” is a knowledge of manipulation of the natural world. Technology is a useful knowledge — a knowledge of a functional capability. In all technologies there is nature being manipulated.

4. Scientific technology is technology invented upon a science base of knowledge that explains why the technology works.

Not all technology has been invented upon a base of scientific knowledge. In fact, until science began in the world in the 1600s, all previous technologies

—

fire, stone weapons, agriculture, boats, writing, boats, bronze, iron, guns — were invented before science. Consequently, technical knowledge of these understood how to make the technologies work but not why the technologies worked . What science does for technology is explain why technologies work. After science, all the important technologies in the world have been invented upon a knowledge base of science.

5. Engineering is the design of economic artifacts embodying technology.

Technologies are implemented in products and services by designing the technical knowledge into the operation of the products/services, and engineers do this design. Engineering designs enable businesses to use nature in adding economic value through its activities. What engineers design in the commercialization phase of technological innovation are new products or services or processes that embody the technical principles of a new technology.

6. Economy is the social process of the human use of nature as utility.

The products/services provide utility to customers who purchase them.

Through products/services, the concept of utility provides the functional relationship of a technology to human purpose. Thus, economic utility is created by a product or service sold in a market and that provides a functional relationship for its customer. For example, xerography products provided the functional relationship of copying (duplicating) the contents of printed papers, which is useful to the customer. Since in a society its technology connects nature to its economy, we will use a meaning of the term economy that indicates this. The common usage of the term economy is to indicate the administration or management or operations of the resources and productivity of a household, business, nation, or society.

But we will use the term to mean the use of nature as utility.

7. Management is the form of leadership in economic processes.

Business organizations provide the social forms for economic activities. The leadership in an economic organization is provided by the management staff of the business.

8. High - tech products/services/processes

are commercial artifacts that

operate on the principles of a new technology.

INNOVATION

PROCESS

15

CASE STUDY:

Google Inc.

In our second case, we turn from the macro to the micro - level of innovation, how a new business develops and uses a new technology to compete and to create wealth for its entrepreneurs. A good example of this is the firm Google — which used the macro - level technology of the Internet to begin a new business in the micro - level technology of a search engine to find Web sites on the Internet. Google did not invent the search engine but improved it and learned how to make money from it. In the first decade of the 2000s, Google earned enormous revenue through advertising. Google incorporated in 1998 and by 2006 generated annual revenue of $ 7.4 billion, with a net income of $ 2.05 billion. It became the most - used search engine and one of the largest companies in the world.

Sandra Siber and Josep Valor summarized Google ’ s early years (Sieber and Valor 2007). The founders of Google were Sergey Brin and Larry Page, and they met in 1995 as two PhD candidate graduate students in Stanford University Computer Science Department — working on a Stanford University digital library project. In 1996, Brin and Page began developing their own search engine, which they called Back Rub. It analyzed not only the content of pages in terms of key words but also counted the number of other links that pointed to these pages.

They assumed that the importance of a page could be measured by the number of links pointing to it. They hosted the software on Stanford University servers, and students and professors tried it out.

In 1997, they renamed the search engine as Google (from the term googol, used in mathematics for quantities raised to the power, 10 100 ) and registered the google.com domain and informed the Office of Technology Licensing at Stanford of their technology. (As part of the U.S. national innovation system, normally the intellectual property rights of all research performed at a university are first invested in the university.)

Some offers were received to buy the technology, but Brin and Page decided to start their own company, licensing the technology from Stanford. In 1998, they were introduced to Andy Echtosheim, who had co - founded Sun Microsystems, Inc.

and was then a vice - president of Cisco Systems. He invested $ 100,000. Google was established in a rented garage in which telephone lines and cable Internet access and DSL lines were installed.

In 1998, PC Magazine listed Google as one of its top 100 Web sites. By 1999, Google was handling 500,000 queries per day and moved to a new office in Palo Alto. Also in 1999, Google obtained its first revenues from license fees for its software from RedHat. Google continued to sell more licenses, and in 2002, America Online (AOL) adopted Google as its default search engine. From all these contracts, by 2003 Google had revenues of $ 961.8 million U.S. dollars with a profit of $ 105.6 million. Then in 2004, Google made an initial public offering (IPO) — offering shares at $ 85 per share — but with no voting rights on these shares.

They raised $ 1.2 billion in the offering.

16

TECHNOLOGICAL

INNOVATION

But if Google was free to use on the Internet, how was Google too earn

“ googol ” amounts of money? Not just from licensing fees — increasingly from advertising. By 2005, advertising revenues at Google, at $ 6 billion, comprised 98.8 percent of its revenue.

While Google started out as a technology company earning money by licensing its software, it became an in effect an advertising company — to make lots of money.

Google built its search engine with four criteria: accuracy, speed, ease of use, and objectivity. Yahoo! gained advertising revenue by listing its advertisers at the top of research results. Google always used objective relevance to order results. Although Yahoo! was the first search engine, increasingly users turned away from nonobjective ranking of results (listed by paid advertisers). And they turned to Google for objectivity. By 2001, Google was the most widely used search engine, with over 100 million queries daily. (This was 10 8 — far from a googol at 10 100 — but heading upward.) Google ’ s business challenge was to build advertising revenue but not to compromise objectivity. To do this, Google built two separate columns for its search -

results page. The first column presented its objective ranking of relevant Web sites for the search. The second column presented a list of relevant advertisers to the research results. In that second advertisers ’ column, Google would charge an advertiser, but only if the user actually clicked through to its Web page, called click - through . The two marketing ideas were that (1) Google would maintain brand integrity for its search users, while (2) producing a higher probability of sales for an advertiser through click - through pricing.

Google maintained objectivity in the presentation of the ranking of relevance to sites, and Google also charged advertisers not on view but only on click - throughs.

The Google search - users were not Google ’ s customers. Google provided a free and objective service to them. That free service was paid for by advertising.

Google ’

s customers were the companies who paid Google to list as relevant to the search on the advertising column. Therefore, Google had to provide two kinds of value: (1) search value to its users, as Google ’ s market base, and (2) sales value to its advertisers, as Google ’ s customers. This was called Google ’ s business model (Sieber and Valor 2007).

Google continued to refine its business model as an advertising company.

Google added services to its advertising customers to try to increase their utility from Google ’ s services — as Google ’ s Checkout program. Google understood that the real value from its advertising prices came to its customers when users not only clicked - through to the advertiser ’ s Web site but also actually purchased from this Web site. To make this purchase an easier experience for the search user, Google added to their customer ’ s Web site the image of a shopping basket (such as that used shopping in a supermarket) — Google Checkout. A user can buy a product from a company advertising on Google and purchase it by simply clicking on Google ’ s payment technology, symbolized by its “ shopping basket. ” TECHNOLOGY AND WEALTH 17

TECHNOLOGY AND WEALTH

With Google, wealth was created by developing new technology (a search engine) and providing it in a business (Google Inc.) as a service (browsing) — making money (wealth) from advertising in the service. The combination of invention and commercialization is the way that technological progress has become the major historical factor in enabling economic development in the modern world — technology creating wealth. Tarek Khalil expressed the relationship between technological innovation and wealth in Figure 1.4 (Khalil, 2000).

The process of technological innovation is generated by the science and technology infrastructure of a nation. This infrastructure provides new technical knowledge to the economic and financial infrastructure of the nation. All three infrastructures provide the bases for national wealth creation. Technological innovation is commercialized in economic systems to add value to markets and to international trade.

Technological innovation provides a competitive advantage for exports and for the businesses in a nation, thus contributing to wealth creation.

Thus, to create wealth, two stages are necessary in innovation: (1) inventing new technology and (2) commercializing new technology in high - tech products or services. The development of the ideas for a new technology is called the invention of the technology. The embodiment of the new technology into high - tech products or services is called commercialization of the technology. These two parts of invention and commercialization are different processes and present different management challenges.

National economic competitiveness

National

wealth

creation

Science and

Economic and

Market and

technology

financial

trade

system

system

system

Technological innovation

Figure 1.4 Technology and wealth creation 18

TECHNOLOGICAL

INNOVATION

CASE STUDY:

Innovation of Xerography

To see the different management challenges in innovation, we next look at xerography. It was another and earlier case of a radical innovation at the micro - level that also launched a new business — Xerox. Here we will see that the two different roles — one of a technical talent and the other of a business talent — were both important. The inventor of xerography was a technical person, Chester F. Carlson, and the commercialization of xerography was accomplished by a businessperson, Joseph Wilson.

Dennis Hall and Rita Hall have summarized Carlson ’ s life (Hall and Hall 2000).

Chester F. Carlson was born in Seattle, Washington, on February 8, 1906. His father had tuberculosis and arthritis and for health reasons moved the family from Seattle to California. During high school in San Bernardino, California, Carlson worked in part - time jobs in a newspaper office and in a small printing business.

He became interested in “ the difficult problem of getting words onto paper or into print ” (Hall and Hall 2000, p 15). After graduating from high school, Carlson attended the two - year Riverside Junior College and then the California Institute of Technology in Pasadena, California, graduating in 1930 with a bachelor ’ s degree in physics.

While at Caltech, Carlson began to think of himself as an inventor: “ I had read of Edison and other successful inventors and the idea of making an invention appealed to me as one of the few means to accomplish a change in one ’ s economic status, while at the same time bringing to focus my interest in technical things and making it possible to make a contribution to society as well ” (Hall and Hall 2000, p. 17).

Carlson found a job with the Bell Telephone Company in New York City, working in their patent department to assist Bell ’ s patent attorneys. It was the time of the Great Depression in the United States, and Carson was laid off from Bell in 1933. He found a similar job in a New York law firm, and then a year later, another one in P.R. Mallory and Company. While at Mallory, Carlson entered and graduated from New York Law School. He became head of the Mallory ’ s patent department.

Carlson had technical backgrounds in physics and in the chemistry of carbon (the powder that he was to use in his invention). As a patent lawyer, he understood a market need for copying. He had been frustrated by the errors in copying a patent for public dissemination and with the trouble with making large numbers of copies whose quality continually decreased with number.

In 1935, Carlson began experimenting in the evenings and weekends with ways to create a new copying process. His idea was (1) to project the image of a typed paper onto a blank sheet of paper coated with dry carbon, (2) to hold temporarily the carbon on spaces of letters by static electrical charges induced by light, and (3) finally, by baking, to melt the ink onto the paper in the patterns TECHNOLOGY AND WEALTH 19

of the projected letters. This would produce a quick, dry reproduction of a typed page, which he would call xerography.

In the fall of 1938, Carlson moved his apparatus from his kitchen in his apart-ment to a one - room laboratory in Astoria on Long Island, New York. He hired Otto Kornie, a recent Austrian immigrant and a physicist, to help him in the invention. On October 22, 1938, they used static electricity and a photoconduc-tive, sulfur - coated zinc plate to transfer a written phrase, “ 10 – 22 – 38 ASTORIA, ” from a glass plate to paper. It was the first demonstration of what would later become xerography . It was a crude image, but it reduced his idea to practice, and he filed for a patent.

Yet like all new inventions, it was still not commercially efficient, cost - effective, or easily usable. It required research and development. The development of a new technology usually costs a great deal of money, takes time, and requires skilled resources. All inventors face similar problems — first conceiving the invention, reducing it to practice, obtaining a patent, and then obtaining support for its development and commercialization.

From 1939 to 1944, Carlson went from company to company seeking support.

He was turned down, again and again, by twenty major companies. Each company that turned him down missed one of the great commercial opportunities of that decade. (That story you may have heard about how the world will beat a path to the door of the inventor of a better mousetrap — not true! A newly invented mousetrap that uses new technology is seldom capable of catching a real mouse until after much costly research and development.) Finally in 1944, Carlson ’ s patent work for Mallory brought him into contact with Russell Dayton, who worked at Battelle Memorial Institute in Ohio. Some researchers in Battelle found Carlson ’ s idea interesting and signed a development agreement with Carlson on October 6, 1944, in return for a share in royalties from the invention. Battelle Memorial was a nonprofit R & D organization, and proceeded to make several improvements in the technical process of the invention.

That same year of 1944, John Dessauer, director of research at Haloid Company, read an article about Carlson ’ s patent. Dessauer told the President of Haloid, Joseph Wilson, about the invention. Wilson was looking for new technology for his company for new products. At the time, Wilson ’ s main customer was the giant Kodak, who could at any time eliminate his small business if it chose. Wilson watched Battelle ’ s research progress, and in 1947 signed a license agreement with Battelle and Carlson.

Finally, all the innovative pieces for Carlson had fallen in place — inventions, patents, R & D, commercialization. Wilson funded Battelle for the rest of the development and then commercialized the first copiers, which Wilson called Xerox.

Wilson subsequently changed the name of his company to Xerox, and the rest became commercial history in the second half of the twentieth century. Xerox created a new industry in office copying and was one of the fastest - growing companies in the world for the decades of the 1950s and 1960s.

20

TECHNOLOGICAL

INNOVATION

For technological innovation, two roles are always required: (1) an inventor (invention) and (2) an entrepreneur (commercialization).

Joseph Wilson (1909 – 1971) graduated from the University of Rochester in New York. He worked for his father ’ s firm, Haloid Company, which made photographic paper for Kodak. Wilson became very wealthy. Carlson derived substantial income from Xerox from royalties, as did Battelle.

Chester Carlson

Joseph Wilson

( http://en.wikipedia.org , 2009)

TECHNICAL SAVVY AND FINANCIAL SAVVY

In the history of innovation, the hard fact is that relatively few managers have really successfully envisioned and run with a radical innovation. Managers with both technical vision and financial strategy have been rare. They are the exceptional managers who are savvy (clever) about both technical and financial issues. Joseph Wilson had both technical and financial savvy. Thus, interesting questions that can be asked about such cases of innovation:

• Why do many companies who are presented with a vision of a new technology not see its strategic importance?

• Why can some organizations with research capabilities have good technological vision but not be able to commercialize things themselves?

•

What kind of leadership qualities do innovative, risk

-

taking managers

possess?

• What was the power of the innovation that it enabled the newly large firm of Xerox to create and dominate an industry for fifty years?

TECHNICAL SAVVY AND FINANCIAL SAVVY 21

These are some the kinds of questions we will pursue in studying technological innovation. We will learn that technical people, like Carlson, invent technology from a skilled base of knowledge in science and engineering. We will learn that a technological entrepreneur, like Joseph Wilson, is a manager who understands a technical opportunity and financially runs with it. What made Carlson an outstanding inventor was that he had two kinds of skills — an understanding of a technical need and the scientific background to invent a process to accomplish the technical goal. What made Wilson an outstanding business leader was that he had two kinds of skills — technical savvy and business savvy. A technically savvy manager, a clever manager, needs to know how to manage the business process of innovation — planning and financing and assembling a good technical team for innovation strategy.

A technically savvy manager may not fully understand the details of a given technology but (1) does appreciate technology and (2) can effectively organize technically skilled personnel for innovation.

CASE STUDY:

Xerox Invents the Altos PC System

Now it is useful to look at another case of radical invention in Xerox — but a problematic case in which Xerox succeeded at invention but failed in commercialization. This is the dramatic case of a second great invention made later by Xerox — but that created no wealth for Xerox. This is the case of the Xerox Altos Personal Computer System.

It was in the late 1970s and early 1980s when Xerox made the radical invention of the next

-

generation technology of personal computers. It was in the form in which today we know as the PC — with windows, icons, mouse, object -

oriented operating system, networked, and laser

-

printing. But Xerox failed to

commercialize — never making a dime from the invention.

In the 1960s, Xerox had acquired a mainframe computer company to enter the computer business, but its acquisition failed in competition against IBM. In response, Xerox turned back to innovation and established a new research laboratory for computer invention — the Palo Alto Research Center (PARC). The PARC

laboratory was to pioneer new computational ideas for the Xerox ’ s strategic technical vision of a “ paperless office. ”

Xerox hired George Pake, a physicist, to head the new PARC laboratory. He located it next to Stanford University, which was strong both in electrical engineering and the then new discipline of computer science. Pake hired many bright young researchers, and an important one was Alan Kay. In the late 1960s, Kay as a student had been influenced by the ideas of an MIT professor, J. Licklider (and we recall that Licklider had once served in ARPA). Licklider envisioned an 22

TECHNOLOGICAL

INNOVATION

easy - to - use computer, portable and about the size of a book (Bartimo 1984). At Palo Alto, Kay and his colleagues further developed these ideas into the vision of the future computer system — personal computers linked in a communications network with laser printers, and operated with icons, windows, and a mouse. They called this the Altos system and built it in 1979 as an experimental computer test bed and prototype.

What did Altos look like then? It looked much like what we would see over a decade later, when others successfully commercialized the earlier Altos vision —

offices with local area networks of Macintosh computers, hooked together with ethernet coupling, using icons, desktops, and mouse and object - oriented programming software. Altos was ahead of its time.

But did Xerox itself make wealth from its visionary investment in computer technology? No. Instead of producing the Altos system, Xerox instead produced a workstation that looked like a Wang word processor — which then was the technical vision of Xerox Office Products Division. The Wang word processor was a minicomputer programmed for the writing task — word processing. Wang produced it from 1978 to 1983. Then, after 1984, the personal computer took over the word processing application. The manager of the Office Products Division was looking backward at technology — in contrast to the forward - looking, technology vision of the Xerox PARC researchers. In 1980, he thought the Wang word processor was the latest thing in technology. But it wasn ’ t. The PC was the future of computer technology.

Instead of an Altos PC, Xerox ’ s Office Products Division put out a Wang look - alike product using only some of PARCs inventions. It was called the Star workstation, and was a commercial failure. This poorly conceived product cost Xerox its whole investment in personal computers. Xerox lost its opportunity to capture the then - new emerging personal computer market. Xerox might have become the future Microsoft and Intel combined!

This was a failure of a manager to properly use his researchers ’ capabilities for technology foresight. He was too much a short - term, money - oriented fellow and failed to appreciate the correct long - term technology vision of his technical personnel and likely emerging market.

Xerox ’ s Office Products Division failed to properly integrate strategies of matter (technology) with strategies of money (markets).

In 1983, Bro Uttal commented: “ On a golden hillside in the sight of Stanford University nestles Xerox ’ s Palo Alto Research — and an embarrassment. For the $ 150 million it has lavished on PARC in 14 years, Xerox has reaped far less than it expected. Yet upstart companies have turned the ideas born there into a crop of promising products. Confides George Pake, Xerox ’ s scholarly research vice president: ‘ My friends tease me by calling PARC a national resource ’ ” (Uttal 1983, p. 97).

TECHNICAL SAVVY AND FINANCIAL SAVVY 23

Eventually all of PARC ’ s Altos system inventions were innovated — but by companies other than Xerox. In the early 1980s, Steve Jobs of Apple visited PARC and saw Altos. Rushing back to Apple, Jobs used PARC ’ s vision to design Macintosh personal computers; and this Mac saved Apple in the 1980s. PARC ’ s research had given Apple its technology lead in personal computers from 1985 to 1995. So it happened that in 1981, Xerox invented the personal computer that the world would not see for another decade. Xerox invented the ethernet - connected personal computer, along with the graphic interface and mouse and object - oriented operating system. Xerox ’ s research was ten years ahead of Apple ’ s Mac and twenty years ahead of Microsoft ’ s Windows software. But then, Xerox never produced PCs and lost its future as a commercially dominant company.

It was in the late 1980s that Xerox management continued to look backward, trying to protect the Xerox market. It completely missed the evolving personal computer industry and next the Internet revolution of the 1990s. These later Xerox managers had failed in technical savvy. So by 2002, Xerox was deeply in debt and on the edge of bankruptcy. (Xerox then even offered PARC for sale. Also, a Xerox CEO was then under investigation by the ERC for fraudulent reporting of sales in the late 1990s.) As a historical note — emphasizing the long - term impact of that failure of innovation strategy in Xerox in the1980s — even three decades later, when the 2010s began, Xerox was still struggling for its future. Then a new CEO, Ursula M. Burns, had just completed a major business acquisition of Affiliated Computer Services for $ 6.4 billion. Despite Xerox ’ s research prowess of the 1970s to 1980s, Xerox had never been able to grow again by innovation but resorted to acquisitions of different businesses. Adam Bryant of the New York Times summarized: “ For many years, Xerox dominated the copier market, helped by an unparalleled direct sales force, and was known as a technology innovator. But growing competition from low - cost computer printers, a failure to capitalize on its innovations and management missteps rocked the company a decade ago ” (Brian 2010, p. B9).

The sequence of events in the two decades of the 1990s and 2000s unfolded as follows:

1.

In 1997, Paul A. Allaire, Xerox ’ s chief executive, hired G. Richard Thoman from IBM as president in hopes that he would help with new digital products and technologies and generally invigorate a stodgy culture.

2. Mr. Thoman, as chief executive, pared 14,000 jobs in two years and realigned the sales force twice. Then, in October 1999, warnings began that profits would tumble.

3. In May 2000, Mr. Thoman was ousted as C.E.O. Anne M. Mulcahy, a four -

year Xerox veteran, was named president.

4. In June 2000, accounting issues were uncovered that later led to big restatements and an SEC fine.

5.

With a steady hand, Ms. Mulcahy turned around Xerox, strengthening products and making it more competitive on cost. Ursula M. Burns, also a longtime Xerox veteran, succeeded her as chief executive in July 2009.

24

TECHNOLOGICAL

INNOVATION

6. Xerox urgently needed to build revenue (sales dropped 14 percent to $ 15.2

billion in 2009) and perk up its stock price, which remained below $ 10 a share after spending a long stretch of the last decade (between $ 20 and $ 10

dollars a share) (Bryan 2010 p. B9).

This case is a cautionary tale of a great company, Xerox, which had been built on a radical invention, xerography. Xerox went on to strategically prepare its future by a second radical invention, the Altos PC system. But Xerox failed to properly commercialize its second radical invention. Thus, Xerox was a “ once great ” company — which became a “ not - so great ” company — continually needing fixing for three decades.

The long - term challenge of innovative high - tech companies is to keep innovative strategy going in a corporation through generations of leadership — in successive CEOs.

The failure of a big company by inventing but not commercializing, such as in the case of Xerox, turns out to be not infrequent in the history of innovation. Another dramatic example of such a failure can be found in the history of AT & T. AT & T had been established in the early twentieth century and granted a monopoly for phone service in the United States. AT & T established a research lab, Bell Laboratories (much earlier, but inventive like Xerox ’ s PARC). Bell Labs made major inventions, including transistor in the 1940s. In the 1960s it invented the basic concepts of cellular phone systems. But then, AT & T did not commercialize and start a cellular phone business. AT & T top management was looking backward — at how to maximize profits from pricing local and long distance phone services. Managers got into legal arguments about AT & T ’ s phone monopoly with the U.S. Department of Justice. Consequently, AT & T agreed to divide its fixed - line phone business between one long - distance phone company (AT & T) and several regional local - phone services. The regional phone services thrived but long - distance AT & T shrank. Eventually, AT & T went bankrupt. But two of the local phone services looked ahead and established cellular phone services as Verizon and Cingular. Then Cingular bought out the old AT & T and changed its name to AT & T. Reincarnated, the new AT & T is a cellular phone service.

It is one of the great ironies of big business that management has often looked backward to yesterday ’ s technologies and focused only on maximiz-ing short - term profits from these old technologies.

Even while their researchers might be envisioning and creating a new technical future, sometimes top executives have failed to look forward toward a strategic future of innovation.

TECHNICAL PERSONNEL AND BUSINESS PERSONNEL 25

TECHNICAL PERSONNEL AND BUSINESS PERSONNEL

How can one account for this backward - looking versus strategic forward - looking that has occurred at the executive levels in some very big companies at various times? To the point — if technological innovation is so important, why hasn ’ t everyone done it successfully?

The reason lies in the differences in ways of thinking between technical and business personnel. As we saw in the case of Xerox ’ s Altos invention, one difficulty in succeeding in innovation has often arisen in the differences between the two activities of innovation — invention and commercialization. Invention is principally a technical process (performing research) and secondarily a business process (funding research). Commercialization is a principally a business process (investing in product development) and secondarily a technical process (solving engineering problems).

Thus, to understand the whole of innovation, one needs to understand: (1) how engineers and scientists think and (2) how marketing, production, and financial managers think. And these groups think differently. Technical personnel primarily focus on technical effectiveness and business personnel primarily focus on financial effectiveness. For a successful high - tech business — a technology innovative business — scientists/engineers and business/managers must understand each other and work together as an innovative team.

Forming and leading integrated technical/business teams for successful innovation is one focus of management of technology.

But making effective teams is not easy to do in practice. This is because engineering and business personnel live in the two different worlds — worlds of matter or of money. The world of matter depends on the sciences of physics, chemistry, biology, and mathematics. The world of money depends on the social systems of economy and markets and on the practices of management. Their principles and theory and practice of the two worlds are very different — one dependent on physical nature and the other on social nature.

Inventors and designers of technology (scientists and engineers) are trained in a conceptually technical world — that one of physical and biological nature.

They see the world as a material, physical world — a world of matter and energy and material life. In contrast, business people are principally trained in the financial world

—

focused on management, leadership, economics, and sales and markets. They see the world as principally driven by financial forces — an economic world of production, trade, and competition for goods and services and wealth.

26

TECHNOLOGICAL

INNOVATION

Which is it? Is the world made of matter or of money?

Both, of course! Technology provides the capability to do things but not the wherewithal. Finance provides the wherewithal of action but not the technical capability.

Properly managing innovation, in both invention and commercialization (matter and money), continues to be a major long - term strategic challenge in large businesses.

LOOKING AHEAD

Innovation theory is complicated because of these two fundamental parts of innovation — invention and commercialization. We have emphasized how these are, respectively, principally technical or business processes. To clarify such complication, we will divide and explore innovation in the two groupings of ideas — a business side of innovation (commercialization) and a technical side of innovation (invention).

But one of the rewards for our facing this complexity is the really good stories that can be told about innovation — stories like the Internet, Google, Xerox, and so on. In the cases of innovation, one can see some of the most dramatic stories of modern times — the successes and failures of innovation — wealth creation and the technology imperative!

KEY IDEAS

In the case studies of:

• Invention of the Internet

• Google

• Invention of

•

Xerography

Invention of the Altos Personal Computer System

We have examined the key ideas of:

• Technology and wealth creation

• Innovation process

• Definitions of science, technology, and economy

• Technical savvy and financial savvy

• Two worlds of innovation — matter and money 2

INNOVATION AND ECONOMY

INTRODUCTION

We first look at the macro - level of innovation. How has innovation impacted a whole economy? We noted that global industrialization began when (1) science created the knowledge base; (2) the new knowledge base enabled continuing technological progress; and (3) new technologies provided the engineering base for new products and new industries. We called this a radical innovation process — transforming knowledge of nature into economic utility. What is the evidence that this process actually occurred? In the economic literature, this was first documented as a historical pattern in modern economic development and called economic long waves .

We ask the following questions:

• How do innovations impact long - term economic development — dynamics of an economy?

• How does an innovation impact a given industry in an economy — dynamics of an industry?

As illustrated in Figure 2.1 , these issues explain how an economy is impacted by technological innovation.

Impact of innovation

upon an economy

Research

Invent

Commercialize

Market

Nature

Science

Technology

Economy

Utility

Exist

Discover

Manipulate

Embed

Use

naturally

nature

nature

nature

nature

The world as material

The world as money

Figure 2.1 Innovation process

27

28 INNOVATION AND ECONOMY

CASE STUDY:

U.S. Dot.com Stock Bubble of the 1990s

A recent dramatic example of economic growth and wealth creation due to a radical innovation can be seen in the early days of the Internet. As the twentieth century ended, there was a financial boom from 1995, which burst in 2000 — the famous “ dot.com ” financial bubble of the U.S. stock market, charted in Figure 2.2 .

Joseph Nocera and Tim Carvell nicely summarized that time: “ The Internet decade has seen the unscrupulous rewarded, the dimwitted suckered, the ill

-

qualified enriched at a pace greater than at any other time in history. The Internet has been a gift to charlatans, hypemeisters, and merchants of vapor . . . . and despite all that, it still changes everything ” (Nocera and Carvell 2000, p. 137).

In the year 2000, the U.S. NASDAQ stock market peaked at a then - historic high of 6000 (see Figure 2.2 ).

At the time of the commercialization of the Internet in 1995, there also occurred, coincidentally, a U.S. government deregulation of the U.S. telecommunications business. It was in both the deregulation and the new Internet that many entrepreneurs saw new business opportunities. They launched both new telecommunication businesses and many new businesses in electronic commerce, “ dot.com businesses. ”

In the 1990s, the rapid growth of the Internet was exciting. For example, in 1996 in the United States, 14 percent of the population used the Internet, which jumped to 22 percent in 1997, 31 percent in 1998, 38 percent in 1999, and 44

percent in 2000 (Elliott and Rutenberg 2000). In 2000, the average monthly hours NAS/NMS compsite (Nasdaq stock

as of 22-May-2009)

6000

4000

3000

2000

1000

54

1975

6.0

1980

1985

1990

1995

2000

2005

4.0

2.0

Billions

0.0

Copyright 2009 Yahoo! Inc.

http://finance.yahoo.com/

Figure 2.2 U.S. stock market “ dot.com ” financial bubble ECONOMIC SCALE OF INNOVATIONS 29

spent online per user was 19 hours. U.S. consumer spending online grew from a few million in 1996 to $ 3 billion in 1997, $ 7 billion in 1998, $ 19 billion in 1999, and $ 36 billion in 2000. Of the $ 36 billion spent in 2000, $ 11.0 billion was for travel, $ 7.7 billion for PCs, $ 2.4 billion for clothes, $ 13.4 billion for books, and $ 13.4 billion for other merchandise. The Internet stimulated an economic expansion in the U.S. economy.

Venture capital funds poured huge amounts of capital into hundreds of ideas for new dot.com businesses, and immediately took many of these public as IPOs (initial public offerings). From 1997 to 2000, the excitement over the Internet and the new dot.com businesses drove U.S. stock markets to new highs. Business pundits then wrote that the Internet was creating a “ new economy, ” for which one need not need worry about profitability of a new business but market growth.

But in the year 2000 this stock bubble burst. The “ new economy ” fell back to the principles of the old economy, which require profitable businesses. The many new unprofitable dot.com businesses collapsed. The NASDAQ average dropped 65 percent early in 2000. Following the Internet stock bubble bursting in 2000, there was a rash of major corporate failures — due to the greed and misdeeds stimulated by the bubble. Some of the failed companies included Enron, WorldCom, Global Crossing, Andersen, and so on.

Historically, the Internet stock bubble was just one of many examples of excessive financial enthusiasm about technological innovation. (An earlier example in the late 1800s in the U.S. was a financial bubble in railroad stocks.) There are important economic lessons one can learn about radical innovation: 1. Scientific research provides the knowledge base for the invention of new basic technologies.

2. New basic technologies create new economic opportunities.

3. The path to wealth in economic opportunities is often rocky.

4. However, rocky the entrepreneurial path, economic development from technological innovation does become a permanent feature of societal structures —

for example, despite that stock bubble, the Internet and electronic commerce was there to stay.

5. Technological innovation and economic development is neither simple nor inevitable.

6. If progress in technology is to occur and be successfully implemented as a business opportunity, then technological innovation needs to be carefully managed.

ECONOMIC SCALE OF INNOVATIONS

The first relationship we will look at between technology and economy is the scale of impact that a technology has on industry in an economy — and innovations have had varying degrees of impact. The idea of classifying technological innovations by the size of their impact on economic completion was introduced by Donald Marquis 30 INNOVATION AND ECONOMY

(Marquis 1969). He distinguished three different scales of innovation impact: radical , incremental , and systems . However, what Marquis called a systems innovation has not proven to be a useful distinction, since all technologies are systems. A more useful distinction is that of a next - generation technology (Betz 1993). Accordingly, we now distinguish three scales of technological innovation: 1. Radical innovation — a basic technological innovation that establishes a new functionality (e.g., Internet, steam engine, or steamboat) 2. Incremental innovation — a change in an existing technology system that does not alter functionality but incrementally improves performance or lowers cost (e.g., governor on a steam engine)

3. Next - generation technology innovation — a change in an existing technology system that does not alter functionality but dramatically improves performance, features, safety, or quality, or lowers cost — to open new applications (e.g., substitution of jet propulsion for propellers on airplanes) For example, the invention of the triode electron tube was a basic innovation.

It enabled many new inventions to occur in electronics — such as radio, television, computers — each of which also was a basic innovation. Incremental innovations in the electron vacuum tubes included adding more grids to improve electronic control, improving materials in the tube, improving vacuum in the tube, and reducing the size of the tube. After forty years, from 1909 to 1949, incremental improvements had attained a technological performance that was hard to improve. Then another radical innovation, the next - generation technology of the transistor, replaced the electron vacuum tube.

As an example of radical innovation we saw that of the Internet. Previously the electronic computer was invented as a basic new technology in the 1940s and commercialized in the 1950s. The 1960s saw another radical innovation when the above - mentioned next - generation technology of transistors replaced electron vacuum tubes in computers. In the early 1970s, another radical innovation occurred as yet another next -

generation technology — integrated circuit (IC) chips — in turn replaced transistors in computers — launching the mini - computer product line. In the late 1970s, another radical innovation occurred when the logic circuits of computers were inscribed on a single chip as a central processing unit (CPU) — launching the personal computer product line.

As another example, International Business Machines (IBM) commercialized the first mainframe computers in 1950. They dominated the market in the 1960s, with their innovation of the next - generation technology of transistorized computers. But IBM failed to innovate the mini - computer product line in the early 1970s and the personal computer product lines in the late 1970s. The next - generation technology of personal computers of the 1980s propelled Microsoft and Intel into giant companies. By 1990, the old mainframe computer business of IBM was dying. IBM

only recovered in the middle of the 1990s when it converted its mainframes from computers to servers, as the radical new innovation, the Internet, grew.

ECONOMIC LONG WAVES 31

Radical innovations occur as (1) basic new technologies or (2) next - generation technology innovations. Incremental innovations occur as small but significant improvements in an existing technology. The processes are different for managing these two different scales: (1) radical innovation process and (2) incremental innovation process.

Another important aspect about the scales of innovation is that their impacts upon business competition differ — as discontinuous and continuous impacts. Radical innovations (basic and next

-

generation technologies) have been called

“

discon-

tinuous

”

innovations because their impacts disrupt existing business structures.

Incremental innovations have been called “ continuous ” innovations because their impacts strengthen existing business structures.

Continuous (incremental) technological innovation enables an existing firm to defend against competitors and to grow its markets.

Discontinuous technological innovation provides the competitive conditions for displacing older businesses — by beginning new businesses and growing a new industry.

ECONOMIC LONG WAVES

The boom - and - bust pattern of the U.S. dot.com financial bubble in 2000 has occurred before. It is an example of the impact of radical innovation (basic new technologies) on an economy. The dot.com bubble was based on financial estimates of the impacts of the discontinuous innovation of the Internet — a radical innovation. This pattern of radical innovation stimulating financial investments and economic growth is a basic pattern in the history of modern economies. Technological innovation and subsequent economic cycles have happened since the beginning of the industrial revolution in England.

The first economist to empirically document this pattern was t he Russian economist Nikolai Kondratiev (1892 – 1938), who in the 1920s studied the economic development of the English economy of the late 1800s and early 1900s. He was born near Moscow and studied at the University of St. Petersburg. After the abdication of the Czar Alexander, a government was formed under Alexander Kerensky, and in 1917

Kondratiev was appointed as minister of supply. After the Bolshevik revolution in 1919, Kondratiev was appointed to a teaching position at the Agricultural Academy.

Then in 1920, he founded the Institute of Conjuncture for the study of business cycles. From 1923 to 1925, he worked on the Soviet five - year plan for agriculture.

In 1924, he published his book detailing his theory of major economic cycles. That same year he also traveled widely in Europe and the United States.

After the death of Lenin in 1924 and the rise of Stalin in 1928, Kondratiev was removed from his institute directorship. In 1930, he was sentenced to eight years in prison. In a prison at Suzdal, near Moscow, he continued his research and prepared

32 INNOVATION AND ECONOMY

new books. But then in 1938, in one of Stalin ’ s Great Purge trials, Kondratiev was sentenced to an additional ten years but was executed by a firing squad that same day. Kondratiev was imprisoned and executed because he had violated a cardinal rule of dictatorships — instead of reiterating party dogma, he told the truth. (In dictatorships, you get shot when you tell the truth.)

Nikolai Kondratiev

( http://en.wikipedia.org2007 )

Kondratiev did his pioneering studies in Russia under the communist dictators Lenin and Stalin. A key tenet in the communist doctrine of Marxism is that modern history is a struggle between capitalists and labor, and when capitalists dominated, they would starve labor. This starvation would lead to the inevitable decline and collapse of capitalism. Marxists assumed that capitalists would never allow labor to have sufficient wages to create a viable internal market that could sustain capitalism over the long term. Kondratiev measured the long - term economic activity of the capitalist country of England to test Marx ’ s prediction. But instead of Marx ’ s prediction of the inevitable decline of capitalism, he found that there actually had been recurrent cycles of economic expansion and contraction in England ’ s economy. Moreover, the net result was increasing economic activity in England, not decreasing.

Kondratiev asked the question: How was capitalism in England periodically renewed and overall expanding? Kondratiev ’ s answer was technology . He plotted a correlation between times of basic innovation to times of economic expansion.

Overall, the capitalistic economies were expanding rather than contracting because of periodic innovations of new technologies.

This economic idea is now called a

long economic cycle,

or a

Kondratiev

wave . The idea is that long periods of economic expansion in modern economies are stimulated by the invention of a new basic technology — such as the airplane or radio or computers or biotechnology. New businesses begin to economically exploit the new technology, and new markets emerge to purchase the new - technology (or ECONOMIC LONG WAVES 33

high - technology) products and services. This expansion of businesses and markets drives the economy over a long time during the rising part of a Kondratiev wave.

While Kondratiev was imprisoned in Russia in the 1930s, another economist was working in Austria, Joseph Schumpeter. Schumpeter (1883 – 1950) was born in Moravia (now part of the Czech Republic). He studied law at the University of Vienna, obtaining his PhD in 1906. In 1909, he became a professor of economics at the University of Czernowitz and in 1911 moved to the University of Graz. In 1919 he served as the Austrian minister of finance. In 1920, he became head of the Biederman Bank until it collapsed in 1924 (due to the postwar inflation in Germany and Austria). From 1925 through 1932, he was a professor at the University of Bonn.

But in 1932 with the rise of the Nazis, Schumpeter fled to the United States, where he became a professor at Harvard. Schumpeter fled the Nazi dictatorship not because of his ideas but of his ethnicity.read and shared Kondratiev ’ s idea — that innovation was important to economic growth: “ . . . the work of Schumpeter . . . put emphasis on innovation and on the subsequent burst of entrepreneurial investment activity as the engine in the upswing of the long cycle, a la Kondratiev . . . ” (Ray 1980, pp. 79 – 80). In his book Business Cycles, Schumpeter said economic innovation could be of five kinds:

1. The introduction of a new good (product or service) to a consumer (or new quality in a good)

2. The introduction of a new method of production of a good 3. The opening of a new market to the sales of a good 4. The obtaining of a new source of supply (inventing or finding or developing new material or energy sources)

5. The implementation of a new form of organization in a business that provides a competitive advantage to the business (Schumpeter 1939).

What technological progress can do for these kinds of innovation is (1) provide a new good or improve the quality of a good (e.g., telephone, radio, airplanes, automobiles, plastics, computers, etc.), (2) improve the quality of production and/or lower the cost (e.g., new semiconductor chip production processes), (3) access a new market by providing a new functional capability to the market (e.g., e - commerce by means of the Internet), and (4) locate new sources of resources of provide improved means for obtaining and processing the resources (e.g., seismic techniques for discovering oil deposits or horizontal drilling techniques). Thus, Schumpeter saw the idea of “ economic innovation ” as any means that (1) creates new markets or market expansion and/or (2) provides a competitive advantage to a business serving these markets.

This is the fundamental idea for connecting science to society, science to economy

—

technological innovation. In particular, innovation based on new

scientific technologies has been a major force in modern economic developments.

34 INNOVATION AND ECONOMY

In his book, Capitalism, Socialism and Democracy , Schumpeter expanded on the impacts of radical innovation as an economic process using the term creative destruction . He argued that radical technological innovations destroyed existing industries and created new industries. This was a process of economic renewal, destroying the old to create the new (Schumpeter 1942). In 1995, the term became popular in business literature, when Richard L. Nolan and David C. Carson published their book also called Creative Destruction (Nolan and Carson 1995).

Joseph Schumpeter

( http://en.wikipedia.org2007 )

In 1975 in Germany, Gerhard Mensch again revived Kondratiev ’ s ideas (Mensch 1979), followed by Jay Forrester, Alan Graham, and Peter Senge in the United States (Graham and Senge 1980). They all argued that basic inventions and innovations underlay the beginnings of economic long cycles. In 1990, Robert Ayers brought more up - to - date data to Kondratiev ’ s earlier empirical correlation — between European industrial expansion and contraction and the development of new technology - based industries (Ayers 1990).

New - Technology Industries and Early Economic Expansion in Industrialization

1770 – 1800

The beginning of the Industrial Revolution in England and next in Europe was based on the new technologies of steam - engine power, coke - fueled steel production, and textile machinery factories.

ECONOMIC LONG WAVES 35

1830 – 1850

A second acceleration of the European Industrial Revolution was based on the innovation of the new technologies of railroads, steamships, telegraph, and coal - gas lighting.

1870 – 1895

Contributions to a third wave of economic expansion were made by innovations of new technologies in electrical light and power, telephone, and chemical dyes and petroleum production.

1895 – 1930

A fourth wave of economic expansion in both Europe and North America was based on innovations of new technologies in automobiles, airplanes, radio, and plastic materials.

Periods of Expansion — Science Base

1770 – 1800 — The invention of the steam - powered engine required the science base of the physics of gases and liquids. This new scientific discipline of physics provided the knowledge base for Thomas Newcomen and James Watt ’ s inventions of the steam engine. Coal - fired steel required knowledge of chemical elements from the new science base of chemistry, which was to be developed in the middle of the 1700s. The new disciplines of physics and chemistry were necessary for the technological bases of the industries of the first wave of economic expansion from the beginning of the Industrial Revolution in England.

1830 – 1850 —

The second economic expansion, to which the telegraph contributed, was based on the new discoveries in electricity and magnetism in the late 1700s and early 1800s.

1870 – 1895 — In the third long economic expansion, it was again the new physics of electricity and magnetism that provided the science bases for inventions of electrical light and power and the telephone. Also, advances in the new discipline of chemistry provided the science base for the invention of the chemical dyes. Artificial dyes were in great economic demand because of the expansion of the new textile industry. Producing these dyes added a new business to the firms producing gunpowder, and provided the technical basis for that industry ’ s expansion into the modern chemical industry.

1895 – 1930 — The fourth long economic expansion was fueled by the invention of the automobile, which depended on the earlier invention of the internal combustion engine. This internal - combustion engine invention 36 INNOVATION AND ECONOMY

required knowledge bases from chemistry and physics. Radio was another new invention based on the advancing science in physics of electricity and magnetism. Chemical plastics were invented from further experimentation in the advancing scientific discipline of chemistry.

Expansions of economic activities due to the innovation of new technologies were followed by contractions of economic activity. Why should economies first expand and then later contract after technological innovation? Why didn ’ t the English or other European economies that Kondratiev followed continue to grow smoothly as the new technologies created new industries in the late 1800s and early 1900s?

Kondratiev had also observed the successive economic contractions in England. The answer is in the interaction between technology and finance.

Economic Cycles in England from 1792 – 1913

The English economy expanded from 1792 – 1825 but then contracted from 1825 – 1847. Kondratiev argued that temporary excess production capacity in iron production, steam engine production, and textile production increased economic competition and drove prices down for an economic recession.

After the second economic expansion in England from 1847 – 1873, there followed another economic contraction from 1873 – 1893 due to excessive investments in the new industries of railroads, steamships, and telegraph industries.

The third economic expansion from 1893 – 1913 was interrupted by the First World War in Europe. Then the economic expansion was renewed in North America and Japan, only to end in a worldwide depression — due, in part, to a collapse in the stock market in the United States and excessive inflation in Germany. The global depression did not end in the different several countries until military production in their economies restarted their industries — for weapons production.

This long -

term interaction between technology and finance can be partly described in a “ model ” of the Kondratiev long wave (Betz 1987). At first, technological innovation attracts capital investment, which produces returns on investment as new high - technology products and services are sold into an expanding market. But this business expansion attracts competition. Further financial investment launches competing businesses in the new market for high - technology products and services. And even while the new high - technology based industry markets grow, industrial production can grow even faster. This leads to a temporary excess production - capacity, which results in price competition and some business failures. Business expansion can be followed by business contraction when financial investment creates temporary excess supply to demand. The nine stages in this Kondratiev long wave are as follows: 1. Science discovers phenomena that can provide for a new manipulation by technological invention of nature.

2. New basic technology provides business opportunities for new industries.

MEASURING THE ECONOMIC IMPACT OF TECHNOLOGICAL INNOVATION 37

3. A new high - tech industry provides rapid market expansion and economic growth.

4.

As the new industry continues to improve the technology, products are improved and prices decline and the market grows toward large volume.

5. Competitors enter the growing large market, investing in more production capacity.

6. As the technology begins to mature, production capacity begins to exceed market demand, triggering price cuts.

7. Excess capacity and lower prices cut margins and increase business failures and raise unemployment.

8. Turmoil in financial markets may turn recession into depression.

9. New science and new basic technologies may provide the basis for a new economic expansion.

History is not deterministic. There is no historical inevitability in Kondratiev ’ s long - wave pattern. It can begin (1) only after scientists discover new nature and (2) after technologists invent new basic technologies. Then basic technological innovation can provide business opportunities for economic expansion. But there is no guarantee that new science and new technologies will always be invented.

It is the innovative interaction between science and technology and economy that provides the basis for long - term economic expansion. Yet also within the competitive financial processes of industry, there is a second pattern — called a short - term economic cycle. In the normal business cycle, after periods of economic expansion, excess production capability often develops, which lowers prices as any new technology - based industry matures. The important point about the long - wave pattern is that one should expect eventual overproduction, even in a new high - tech industry, as technology matures and many competitors enter the new market. This will always cut profit margins, even in a relatively new industry. High - tech industry will never be a high - profit -

margin industry for long, because competition will intensify after the technology begins maturing.

Neither technological progress nor economic development is smooth and continuous. Both involve discontinuous (technology) and cyclic (economic) processes.

MEASURING THE ECONOMIC IMPACT OF TECHNOLOGICAL

INNOVATION IN AN ECONOMY

How can one measure the impact of technological innovation upon an economy?

Wassily Leontief

’

s economic input

-

output model of a national economy pro-

vides a means to do this. Leontief described the total production ( P i ) from an 38 INNOVATION AND ECONOMY

economic sector (such as manufacturing or agriculture) and traces that quantity of production P i as it is distributed into the economy for consumers ( C i ) or for other industrial sectors ( I j ) or exported to other countries ( E j ). Then a Leontief input

-

output equation describing the economy as sectors can be written as: P i ϭ SUM J ( C i ϩ X ij ϩ E ij ).

This is read as the quantity of production P i in the i th economic sector is distributed to a summation of all (a) the consumers of the i th products and (b) the industrial consumption and (c) the exports. The summation (SUM J ) is taken over all other j th economic sectors and all the other j th countries. (In mathematical notation, the quantities of P and C are vectors and the quantities of X and E are matrices.)

This is a system model of an economy — with inputs to the economy by production ( P i ) in i th sectors of the economy and outputs from the production into the economic sectors of consumer consumption ( C i ) and the other j th sectors of industrial consumption ( X ij ) and exports ( E ij ) to other j th nations. This is an input - output, open -

systems model of a national economy, and one can use this to measure economic impacts of technological innovation upon an industrial sector ( i ), as sketched in Figure 2.3 .

Wassily Leontief (1905 – 1999) was born in St. Petersburg, Russia. He entered the University of Leningrad in 1921 and earned a master ’ s degree in economics.

In 1925, he left Russia for political reasons. In Germany, he entered the University of Berlin and obtained a doctorate in economics in 1925. In 1931, he went to the United States, and began teaching at Harvard University in 1932. In 1949, Leontieff modeled data on the U.S. economy, which divided the economy into 500 sectors.

In 1973, he won a Nobel Prize in Economics, and in 1975, he joined New York University.

Leontief’s input-output systems model

P ϭ Production I

of national economic sectors

C ϭ Consumption of I

X ϭ Intersector I to J

P

E ϭ Exports of I to

I ϭ SUMJK(CI ϩ XIJ ϩ EIK)

Nation K

Technology innovation

investment in sector I

at time T1

PI(T1) ϭ SUMJK(CI ϩ XIJ ϩ EIK)

PI(T2) ϭ SUMJK(CI ϩ XIJ ϩ EIK)

Economic impact

Export impact from I to

in sector I at time T2

nation K at time T2

Figure 2.3 Measuring the direct impact of R & D funding MEASURING THE ECONOMIC IMPACT OF TECHNOLOGICAL INNOVATION 39

CASE STUDY:

United States Auto Industry

The Kondratiev long - economic wave describes the dynamical impact of technological innovation on a whole economy. But precisely how does this happen in an industry in an economy? We next look at this industrial dynamics due to technological change. This case is the origin and growth of the automobile industry in the United States.

The auto industry was created by the radical innovation of putting an engine onto the bicycle technology. The bicycle had been invented in the middle of the nineteenth century, taking technical advantage of new high - strength, lightweight, and low - cost steels produced in quantity by the then - new steel industry, using Bessemer ’ s invention. Toward the end of that century, some bicycle manufacturers were further innovating by adding engines onto a four - wheeled bicycle frame.

William Abernathy wrote:

Alan Nevins observes that the (automobile) industry was born from the consumer ’ s desire for a light personal transportation vehicle, a desire stimulated by the bicycle boom of the 1890s . . . Men with experience in the bicycle industry were the first to see the possibilities of the automobile as a means of personal transportation. Their technological orientation led them to improve the automobile ’ s performance through lightweight designs, high - strength materials, and low - friction ball bearings rather than increased motor power (Abernathy 1978, p 12).

The year 1896 can mark the beginning of the U.S. automobile industry because then more than one auto was produced from the same plan: J. Frank Duryea made and sold thirteen identical cars in Springfield, Massachusetts. During the next few years, many new automobile firms were founded and a variety of auto configurations were offered (Abernathy 1978). Races were held between the three principal configurations of automobiles in steam, electric, or gasoline power. In 1902, a gasoline - powered car defeated electric and steam cars at a racetrack in Chicago, establishing the dominance of the gasoline engine. Thereafter, this engine was to become the core technology for the automobile. Also in 1902, Olds Motor Works constructed and sold 2,500 small two - cylinder gasoline cars priced at $ 650. The next six years in the United States saw the growth of many small automobile firms selling different versions of the gasoline engine auto.

Other configurational choices were made. In 1903, Buick relocated the engine from the rear to the front of the car (as in most of current designs). The bicycle -

like drive chain was replaced by a direct drive shaft, connecting the front - placed engine to rear wheels. In 1904, Packard patented the four - position transmission with positions in the shape of an H, which subsequently became the standard for manual transmissions (with later alternative choices of three or five speed).

Ford redesigned the earlier one - piece designed block - and - head engines in two separate pieces for ease of casting and machining. So by 1907, the automobile 40 INNOVATION AND ECONOMY

system began to look more like modern designs than like the early carriage/

bicycles with rear - mounted engine.

The next key event in the history of the U.S. auto industry was Henry Ford ’ s introduction of his famous Model T (Nevins and Hill 1953). When Henry Ford was producing automobiles, he was racing his cars to establish a reputation for performance. His cars were expensive, as were all other cars — principally for the well - to - do.

But Ford had in mind a large, untapped market — a car for people living on farms.

Around 1900, half of Americans still lived on the farm. Ford wanted to build a practical, high - quality automobile priced at $ 450. His commercial strategy was price and his technical strategy was durability. The rural application required an inexpensive, reliable, and durable car, which also had a high clearance for dirt roads and easy maintainability by mechanically minded farmers. The key to his technical innovation would be in the weight and strength of the chassis of the automobile structure.

Material costs in the early automobile were a very large part of its cost. If Ford could reduce the weight of the model T by at least one - half of competing designs, that technology would produce an enormous competitive advantage for his grand strategy of a “ car for the people. ” Ford ’ s innovation for decreasing the weight of the automobile would be to use high - strength steel for the chassis, made of the element vanadium as an alloy. Henry Ford learned of this new steel when attending an automobile race. In one of the unfortunate accidents that day, a racing car imported from France was wrecked:

In 1905 [Ford] saw a French automobile wrecked in a smash - up. Looking over the wreck, he picked up a valve stem, very light and tough . . . it proved to be a French steel with vanadium alloy. Ford found that none [in the U.S.] could duplicate the metal. [Ford] found a small steel company in Canton, Ohio [and]

offered to guarantee them against loss. The first heat was a failure . . . the second time the steel came through. Until then [he] had been forced to be satisfied with steel running between 60,000 and 70,000 pounds tensile strength. With vanadium steel, the strength went to 170,000 pounds (Nevins and Hill 1953, p 349). Making the chassis of this steel meant that he could reduce the weight of the chassis by nearly a third and get the same strength. It was a technological breakthrough that allowed Ford to imagine an innovative new product design. Ford told Charles Sorensen, who helped him design the Model T, “ Charlie, this means entirely new design requirements and we can get a better, lighter and cheaper car as a result of it ” (Sorensen 1956, p. 98).

Ford used the new vanadium steel to fabricate the chassis of the automobile, which reduced the overall weight of the model T to about half that of then existing automobiles. In addition, Ford innovated the design by mounting the motor to this chassis with a three - point suspension. The prior practice had been to bolt the engine directly to the frame, and often even the cylinder blocks of those engines were twisted in half by the enormous strain that occurred when the automobile bounced over a hole or rut.

Ford also designed the Model T to be a “ best of breed. ” He used the other best ideas in other contemporary automobiles. For example, he replaced the then - traditional dry - cell batteries for starting the car with a magnet - powered ignition (one cranked the Model T to start it). The Model T became a “ design standard ” for CORE TECHNOLOGIES OF AN INDUSTRY 41

automobile technology, long after its time: “ For eighteen years the design of the Model T chassis was not significantly changed. During this period the industry ’ s production of passenger cars increased nearly sixty fold, from 64,500 cars annually to 3,700,000 . . . Ford maintained about a 50 percent market share through 1924 ” (Abernathy 1978, p 18). Ford ’ s Model T was the right product at the right time for the right market at the right price. Performance, timing, market, price — these are the four factors for commercial success in innovation. Ford captured the auto market from 1908 through 1923, selling the majority of automobiles in the United States in those years.

CORE TECHNOLOGIES OF AN INDUSTRY

As we see in this example of the early U.S. auto industry, Kondratiev economic long waves occur in a nation when a new technology is invented and applied in the production of new high - tech products/services. The idea that some technologies in a product are more central, or core, to the product has been expressed as a core technology . Core technologies in an industry provide the basic functional transformations for products and production processes. The gasoline/diesel engine is a core technology for the automobile. The materials for the chassis and body and their shaping into parts are also core technologies. The assembly - line manufacturing procedures, which Ford introduced, were also core production technology.

The invention of a core technology is essential to the beginning of a new industry, as the industry could not provide its product lines before a core technology was invented. Improvement in the technical performance of the industry depends on progress in its core technologies. Core technologies developed and held in - house for competitiveness are proprietary core technologies of the firm. As such, proprietary core technologies can be in products, production, distribution, or information.

If a core technology is supplied from outside the firm, that core technology cannot provide a competitive advantage to the firm. Thus, outsourcing of parts and supplies or technical progress cannot provide a competitive advantage to a company.

The core technologies of an industry provide the essential functional transformations in the industry ’ s products and production.

Lagging in technical progress in a core technology will put a business at a serious competitive disadvantage, while leading in technical progress in a core technology will defend against competitor ’ s encroachments.

Some core or supportive technologies will usually be changing at a much faster rate than others, and these may be called the strategic technologies for the industry.

The strategic technologies, which are also core technologies for the industry, can be called pacing technologies, for competition in the industrial structure will be technically paced by these.

42 INNOVATION AND ECONOMY

INDUSTRIAL LIFE CYCLE

The pacing of technological progress affects the dynamics of growth and stability of the whole industry. Abernathy and Clark first suggested the idea of a dynamic pattern in industrial growth due to technical progress — which they called an industrial life cycle (Abernathy and Clark 1985). Later, David Ford and Chris Ryan proposed that a chart of market volume over time for an industry would reflect the underlying maturation of the core technologies of its product. They also called this chart an industrial life cycle (Ford and Ryan 1981). Figure 2.4 shows the general pattern of growth for a new industry begun on a basic technological innovation. Market volume does not begin to grow until the application launch phase of the new core technology begins with an innovative product.

Figure 2.5 shows the industrial life cycle chart for domestic production of automobiles in the United States. (In the case of the U.S. automobile that was 1896, when Duryea made and sold the first thirteen cars from the same design.) One can see that the early shape of the curve is in the form of an S - curve that levels off around 1941

to 1985. The economic impacts of the Great Depression of the 1930s and of World War II interrupted automobile production during their times. After the Second War U.S. domestic automobile product begins again at a saturated market level of about 8 million cars produced per year. Then a dramatic event happens to the industry in 1990, when the Cold War between the United States and the Soviet Union ends as the Soviet government collapses in Russia. Then the U.S. industrial energies turn away from the expense of government funding of U.S. military expenses for the Cold War. And automobile consumption doubles in the U.S. market from 1991 to 1999. This can be seen as an indirect measure on the economy of the cost of the Cold War to the American people.

However, as a footnote to this history, it is interesting to look into the details of auto company market shares during this doubling period. The Japanese - owned automobile High-tech

Commodity

industry

industry

y

olume

ket V

of Industr

Mar

Time

Application

Product

Mature

Core

launch of

becomes

industry

technology

new high-tech

a commodity

becomes

product

type product

obsolete

Figure 2.4 Industrial life cycle

97

94

91

88

C

o

l

d

w

a

r

e

n

d

s

85

82

79

76

73

70

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64

61

58

55

52

ear

49

Y

W

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r

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46

43

U.S. automobile sales (domestic production)

40

2.5

37

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G

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a

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p

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e

s

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Figur

31

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147

8

6

4

2

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16

14

12

10

Millions

43

44 INNOVATION AND ECONOMY

companies expanded their share, obtaining almost the whole doubling in the market.

Finally by 2008, Toyota overtook Ford and General Motors to become the largest automobile producer in the United States. And in 2008, General Motors and Chrysler went bankrupt, and Ford remained as the last solvent U.S. – owned automobile producer.

The first technological phase of an industry will be one of rapid development of the new product during the applications growth phase. (For the automobile, this lasted from 1889 to 1902 as experiments in steam, electric, and gasoline powered cars were tried.) When a standard design for the product occurs, rapid growth of the market continues. (For the automobile, this occurred with Ford ’ s introduction of the Model T design.) Industrial standards ensure minimal performance, system compatibility, safety, reparability, etc. Sometimes these standards are set through an industrial consortium and/or government assistance (e.g., safety standards). But usually in a new technology, a performance standard emerges from a market leader.

William Abernathy pointed out that the pattern of early innovations in a new -

technology - based industry usually begins with product innovations (improving the performance and safety of the product) and then later shifts to production process innovations, making the product cheaper and with better production quality (Abernathy 1978). Figure 2.6 plots the rate of product innovations over time and rate of process innovations underpinning an industrial life cycle.

Therein one sees that the rate of product innovation peaks about the time of the introduction of a design standard for the new - technology product. Thereafter, the rate of innovations to improve the product declines, and the rate of innovations to improve production increases. This occurs because until the product design has been standardized, a manufacturer cannot focus on improving the production processes that will produce such a design. After the key technologies of the industry mature, the market for the industry will eventually saturate. This level of market for the industry will continue, unless the key technologies for the industry become obsolete by technology substitution. Then the market volume of the industry based on the older key - t echnology product will decline to zero or to a market niche.

INDUSTRIAL ST

ANDARDS AND FIRST MOVERS

IN A NEW INDUSTRY

The case of Ford ’ s Model T also provided a product - design standard for the new U.S. automobile industry. Industrial product - design standards are critical for the growth of a large volume market. Sometimes these emerge from a design standard set by an innovative product of a competitor, such as Ford.

In a new industry, a product - design standard defines the standard features, configuration, and performance of a product line in a new industry.

A product - design standard is necessary for the high - volume growth of a new market around that product.

INDUSTRIAL STANDARDS AND FIRST MOVERS 45

Market saturation

Number of firms

of industry

in industry

Market volume

Launch

Application

Mature phase

growth

Time

Rate of product

innovations

Rate of production

Rate of

innovations

innovations

Standard design

Time

Plateau of product performance

Improvement in

product performance

Product technology

Time

Figure 2.6 Dynamics of new industries based on technological innovation Sometimes design standards arise not from an innovative product but from a deliberate act of cooperation between industry and government to set safety standards. Then the process for establishing industrial product standards can be complex. Reaching consensus agreement between industrial firms and government about industrial standards is never simple or easy.

For example, Antonio Bailetti and John R. Callahan examined how public standards in communications were managed by industrial firms (Bailetti and Callahan 1995). They pointed out that managing industrial standards was a difficult problem for several reasons, such as the need for collaboration among competitors in setting standards, the problems of complexity and uncertainty in standards when the technology is rapidly changing, the need for international cooperation in setting standards, and so on. They suggested that a systematic procedure facilitated the establishment of standards. This procedure would be built on three kinds of groups in the standards 46 INNOVATION AND ECONOMY

process: information management (focusing on information formulation); commercial exploitation (focusing on product standards setting); and standard development (focusing on maintenance and evolution of standards).

The illustration of Ford (innovating both the Model T and assembly line production) was an example of an innovative firm in a new industry that moved first to establish competencies to dominate the market. Alfred Chandler used the term first movers in an industry to indicate the firms in a new industry that move first to dominate the industry: “ Those who first made . . . large investments — the companies I call ‘ first movers ’ — quickly dominated their industries and continued to do so for decades. Those who failed to make these investments rarely became competitive at home or in international markets; nor did the industries in which they operated ” (Chandler 1990, p. 132).

The necessary investments for a firm to become a first mover in a new industry are:

• Investments in advancing technology

• Investments in large - scale production capacity

• Investments in national (and international) distribution capability

• Investments in developing management talent to grow the new industry In a new - technology industry, a first - mover firm is the first to complete its national business system to gain economic advantages of scale and scope.

CASE STUDY:

Numbers of U.S. Auto Firms Over Time

After the introduction of the Model T, the improvements in automobile - product technology were incremental — for a long time. For example, in 1921, Hudson marketed a car with an enclosed steel panel body that had steel panel over a wooden body frame. This technical change made automobiles more durable to weather.

Later Studebaker shaped the steel panel for stiffness and eliminated the wooden body frame. Chrysler streamlined the steel body shape for greater air - flow efficiency. Just before the Second World War, General Motors innovated the automatic transmission. As innovation slowed in the automobile product, competition focused on style, price and quality of the product.

After the war, the American automobile industry entered a period of stable technology. Management focus from 1950 turned to refining existing technology, automating production techniques, styling and reducing numbers of parts. (Not until the 1980s did technology change in the automobile system, beginning with the introduction of electronics into the control systems of the automobile.) INDUSTRIAL STANDARDS AND FIRST MOVERS 47

In 1909 in the new U.S. auto industry, there were sixty - nine auto firms, but only half of these survived the seven years to 1916 (Abernathy 1978). In 1918, Ford ’ s new model T began putting many of these out of business, as the new design standard for automobiles captured the majority of the auto market. Competitors had to quickly redesign their product offerings to meet the quality of the Model T and its price. By 1923, only eight firms had succeeded in doing this and survived

—

General Motors (GM), Ford, Chrysler, American Motors, Studebaker, Hudson, Packard, and Nash. Twenty - six other firms failed over the five years from 1918 to 1923.

As we saw in the industry life - cycle chart, the economic depression of the 1930s and the Second World War interfered with the normal growth of the auto industry. But after that war, the market growth of the U.S. auto industry resumed and leveled off as a mature industry. The average annual sales of cars in the U.S.

peaked around 1955 at about 55 million units sold per year. By then, General Motors had attained close to 50 percent market share. In 1960, the number of domestic auto firms remaining were four: General Motors, Ford, Chrysler, and American Motors.

After GM overcame Ford ’ s innovative lead in the late 1920s, it continued to dominate the U.S. automobile market until late in the twentieth century. In the 1950s and 1960s, GM ’ s market share grew to about 54 percent of the automobile market, but then began to slip, due to poor design and engineering, losing market share to the rebuilding world auto industry, particularly in Japan and Europe.

By 1995, GM ’ s share had declined to 34 percent and continued declining to 29

percent by the year 2000.

Additionally, the 1970s saw the beginnings of significant U.S. market share loss to foreign auto producers. During that decade, gasoline prices jumped due to the formation of a global oil cartel. American automobile producers did not meet the demand for fuel - efficient cars that came about as a result of the subsequent increase in gasoline prices. In 1980, U.S. auto producers faced a desperate time, with obsolete models, high production costs, and low production quality.

During the 1980s, the foreign share of the U.S. market climbed to one third, and there were only three remaining U.S. - based auto firms: General Motors, Ford, and Chrysler. Chrysler was bought in 1988 by the German firm of Daimler - Benz.

In 2007 Chryslter was sold again, this time to a private equity firm, and in 2009

filed for Chapter 11 reorganization. That same year, the Fiat Group acquired a partial stake in the company.

The rapid decline of GM ’ s market share was due to the expansion of Japanese auto sales in the United States from the late 1970s. By the year 2000, foreign -

based automobile firms (i.e., Japanese and European) had captured over a third of the U.S. market — primarily because of GM ’ s lower quality.

In 2008, after a global meltdown in the world ’ s financial markets, General Motors entered bankruptcy. It took three decades and several CEOs at GM (from 1979 through 2009) to bankrupt the company. GM had a severe technology management problem. Its CEOs had maximized short - term profits and neglected to develop the fuel - efficient cars and trucks that would have saved the company.

48 INNOVATION AND ECONOMY

This is an example of what we earlier would have called a not - savvy - technology leadership style — or just plain technology stupid!

In 2009, Bill Vlasic wrote about the recognition of ‘ bad management ’ in GM: When GM collapsed last year (2008) and turned to the government for an emergency bailout, its century - old way of conducting business was laid bare, with all its flaws in plain sight . . . For all its financial troubles and shortcomings as an automaker, no aspect of GM has confounded its critics as much as its hidebound, command - and - control corporate culture . . . .

In the old GM, any changes to a product program would be reviewed by as many as 70 executives, often taking two months for a decision to wind its way through regional forums, then to a global committee, and finally to the all - powerful automotive products board . . . . In the past, GM

rarely held back a product to add the extra touches that would improve its chances in a fiercely competitive market . . . . Even as it labors to change its culture, GM must convince consumers that it is building better cars . . . (Vlasic 2009, p. B1).

How a company manages innovation and its product development process is critical to its competitiveness — and to its long - term survival.

This pattern of an industrial life cycle is a general pattern, one not limited to the automobile industry. James M. Utterback and Fernando F. Suarez charted over time a number of competitors in several industries: autos, televisions, vacuum tubes, typewriters, transistors, supercomputers, calculators, and IC chips (Utterback and Suarez 1993). Each of these industries showed the same pattern — a large number of competitors entering the field as the new technology grew, and then numbers peaking and dramatically declining due to intense competition as the technology progressed and matured. For a technology - mature industry, market levels may remain relatively constant, since without new technology, market level is determined only by replacement rates and demographics.

COMMODITY INDUSTRY

Eventually, all high - tech products become commodities, when progress mostly ends in their core technologies. High - tech products/services are products/services that are rapidly improving in technical performance, due to continuing technical progress in their core technologies. But as the core technologies in an industry mature in performance, products become relatively undifferentiated technically.

Then price and quality of the product ion become the primary competitive factors. In contrast to high - tech products, performance - undifferentiated products are called “ commodity ” type products — since they are all technically alike. Industries COMMODITY INDUSTRY 49

are called high - tech industries when the core technologies in their products and production are rapidly changing. Industries are called mature, or commodity

industries , when the technologies in their products and production are no longer changing rapidly or much at all.

Because over time core technologies mature, all high - tech industries eventually become commodity industries.

The automobile industry had become a mature - technology industry, a commodity product business in the second half of the twentieth century. And this also happened to other earlier high - tech industries of the early twentieth century.

Another example of this was the chemical industry, after technological innovation in many areas of chemistry had slowed by the 1970s. Then European and U.S.

chemical firms were seeing a future chemical industry with low

-

profitability in

commodity - type products and excess world production capacity, where earlier they had been used to innovative and high - profit - margin products. As one newspaper at that time summarized:

It happened in steel, it happened in copper, and now it is starting to happen to basic petrochemicals . . . . A once - thriving domestic industry reaches maturity . . . . Then upstart producers in developing countries, which often have lower costs for raw materials and labor, build spanking new plants.

This floods the world with excess capacity and forces many manufacturers in the developed countries to shutter their higher - cost operations. This oft -

repeated trend is under way in the petrochemical industry. It has sparked a shake - out among the nations ’ manufacturers of basic petrochemicals such as methane and ethylene, used as build blocks for more sophisticated chemicals ( New York Times 1984).

As an example, late in 2002, DuPont sold its nylon business, which in the late 1930s had been the innovation that powered the growth of the company.

Businesses in high - tech industries can compete based on superior technical performance of product, whereas businesses in commodity industries can only compete on price and quality.

The profit margin for any firm ’ s product lines are determined by two conditions, internal and external. The internal conditions consist of the efficiency of the firm and the strategy of management. The external conditions are the balance of supply to demand in the industry. Those external factors include the number of competitors, how rapidly technology is changing, and how rapidly the market is growing.

50 INNOVATION AND ECONOMY

When an industry reaches technological maturity and products are commodity -

type, then the profitability of any firm in the industry is bounded by the external conditions of the number of competitors and production capacity of the industry.

High - tech products can provide high gross margins because of the limited competition in the technical performance that high - tech products provide. But the profit margins of commodity products are set by industrial competition only on price. At first, all businesses in a new industry are high - tech businesses, but over time, all businesses in that industry become commodity businesses. As an industry matures from high - tech toward a commodity industry, the survivors of the industrial shakeout are those companies who are low - cost, high - quality producers.

For all industries, there are two different business strategies over time, due to technological innovation: first a high - tech product strategy and later a commodity product strategy.

KEY IDEAS

In the case studies of:

• U.S. dot.com stock bubble

• U.S. auto industry

• Numbers of U.S. auto firms over time

We have examined the key ideas of:

• Scales of innovations

• Economic long waves

• Core technologies

• Industrial life cycle

• Industrial standards

• Industrial movers

• Commodity industries

3

INNOVATION AND

NATIONAL SYSTEMS

INTRODUCTION

In the pattern of an industrial life cycle, we saw that new industries begin and grow due to radical innovation. But how does radical innovation occur? The issues we will address include the following:

• What kind of a knowledge structure in a nation facilitates the process of radical innovation?

• How does radical innovation as a next - generation technology create discontinuities in markets?

We will examine the interconnection of science and technology (Figure 3.1 ), and look at the details of how they are connected through the act of invention of a radically new technology — radical innovation.

Invention of a radically

new technology

Research

Invent

Commercialize

Market

Nature

Science

Technology

Economy

Utility

Exist

Discover

Manipulate

Embed

Use

naturally

nature

nature

nature

nature

The world as material

The world as money

Figure 3.1 Innovation process

51

52 INNOVATION AND NATIONAL SYSTEMS

CASE STUDY:

Invention of the Computer — Science Knowledge Base

To illustrate a radical innovation, we review the invention of the electronic stored -

program computer. The computer invention is one of those great technologies that completely altered human society. T. A. Heppenheimer noted the several individuals who contributed to the invention: “ Fascination with the power of technology has long been a part of human culture. The computer changed the mythic power of humanity to acquire and process information. Central to its invention were the humans — Von Neumann, Goedel, Turing, Mauchly, and Eckert — and tragically, each did suffer a Promethean end ” (Heppenheimer 1990).

The scientific knowledge on which the stored - program computer was invented was provided by mathematician John Von Neumann.

John Von Neumann was born in Hungary in 1903, the son of a Budapest banker.

He was precocious. At the age of six, he could divide eight - digit numbers in his head and talk with his father in ancient Greek. At eight years old he began learning calculus. He had a photographic memory. He could take a page of the Budapest phone directory, read it, and recite it from memory. When it was time for university training, he went to study in Germany under the great mathematician David Hilbert.

Von Neumann met John Mauchly and Presper Eckert just when they were working on the first computer. Mauchly was a physicist and Presper an engineer. They were doing technology research on a government - funded project to construct the first electronic computer. What Von Neumann, as a mathematician, added to their research was the idea of mathematics computation as an algorithm. This provided a science base for knowledge — underpinning the technology research of Mauchly and Eckert.

John Von Neumann

( http://en.wikipedia.org 2008)

David Hilbert believed that all the diverse topics in mathematics could be established on self - consistent and self - contained intellectual foundations. Hilbert, in a famous address in 1900, expressed his position: “ Every mathematical problem INTRODUCTION

53

can be solved. We are all convinced of that. After all, one of the things that attracts us most when we apply ourselves to a mathematical problem is precisely, that within us we always hear the call: here is the problem, search for the solution; one can find it by pure thought, for in mathematics there is no ignorabimus (we will not know) ” (Heppenheimer 1990, p. 8).

As a graduate student, Von Neumann had worked on the problem of mathematical foundations. But in 1931, Kurt Goedel ’ s famous papers were published, arguing that no foundation of mathematics could be constructed that was wholly self - contained.

If one tried to provide a self

-

contained foundation, one could always devise

mathematical statements that were formally unproveable within that foundation (incapable of being proved or disproved purely within the foundational framework).

This disturbed Von Neumann, as it did all other mathematicians of the time.

But also Goedel introduced an interesting new notation, by which any mathematical proof or calculation could be expressed as a series of steps, each step numbered as a mathematical instruction. This notation would later turn out to be a central idea for Von Neumann ’ s idea of a stored program computer. All mathematical statements, logical expressions as well as data, could be expressed as series of numerically encoded instructions. However, the first person to take up Goedel ’ s “ instruction ” idea was not Von Neumann but Alan Turing.

In 1937, Turing was a twenty - five - year - old graduate student at Cambridge University when he published his seminal paper: “ On Computable Numbers. ” Turing used Goedel ’ s idea for expressing a series of mathematical statements in sequential numbering. Turing proposed an idealized machine that could do mathematical computations. A series of mathematical steps could be expressed in the form of coded instructions on a long paper tape. The machine would execute these instructions in sequence as the paper tape was read. His idea was later to be called a Turing machine . Turing had described the key idea in what would later become a general - purpose programmable computer. Although many people had thought of and devised calculating devices, these had to be externally instructed or could solve only a specific type of problem. A machine that could be generally instructed to solve any kind of mathematical problem had not yet been built.

Earlier in 1930 after finishing his graduate studies in Germany, Von Neumann had emigrated to America to escape Nazi persecution in Germany. He joined the faculty of Princeton University. Later in 1936, Turing ’ s and Von Neumann ’ s paths temporarily crossed. Turing came to Princeton to do his graduate work. He was thinking about the problem of his idealized machine, and he worked with Von Neumann, exposing Von Neumann to his ideas. Von Neumann offered Turing a position as an assistant after Turing received his doctorate. But Turing chose to return to Cambridge. There he published his famous paper. This is how Turing ’ s ideas were in the back of Von Neumann ’ s mind.

But war was to intervene, beginning with the German invasion of Poland in 1939. In the United States in 1942, Von Neumann became involved in the U.S.

war effort — in the Manhattan Project to create the atomic bomb. Simultaneously in that war, Turing was involved in England ’ s secret code - breaking project and helped construct a large electronic computer to break enemy codes. It was called 54 INNOVATION AND NATIONAL SYSTEMS

the Colossus and began operating in 1943. The Colossus was in effect a single -

purpose computational machine (Zorpette 1987).

In this illustration of the invention of the electronic computer, we see that first there were some scientific ideas

—

which would later provide knowledge

(science base of knowledge) toward the invention of the computer. These scientific ideas were of mathematical computation in the form of a series of operational instructions — Goedel ’ s notation and Turing ’ s idea of a computational scheme (now called a Turing machine).

SCIENCE BASES FOR TECHNOLOGY

We have seen that the modern process of radical innovation begins in the interaction between science and technology. Technologies that are invented and improved on a knowledge base of science can be called scientific technologies . Table 3.1 lists some of the historically important innovations. Note how rare technological innovations were until the 1700s — tools, pottery, bronze, writing, iron, guns, printing, telescopes.

Note, too, how many technological innovations began after the 1700s. The enormous numbers of innovations after 1700 are due to the creation of science.

TABLE 3.1 Historically important innovations

Innovation

Function

Date

Tools

Technology

Pre - history

Pottery

Materials

Stone Age

Bronze

Materials

2500s BC

Writing

Literacy

2500s BC

Iron

Materials

1500s BC

Gun

Weapons

1300s AD

Printing

Literacy

1400s

Telescope

Optics

1500s

Microscope

Science

1700s

Steam Engine

Power

1700s

Powered Machinery

Production

1700s

Railroads

Transportation

1830s

Telegraph

Communications

1850s

Chemicals

Materials

1850s

Steam Ships

Transportation

1860s

Cameras

Images

1860s

Telephone

Communications

1880s

Electric Lighting

Illumination

1880s

Electrical Power

Power

1880s

Bicycles

Transportation

1880s

Automobiles

Transportation

1890s

Airplanes

Transportation

1900s

Plastics

Materials

1900s

Movies

Communications

1910s

Electron Tubes

Electronics

1910s

SCIENCE BASES FOR TECHNOLOGY 55

Innovation

Function

Date

Radio

Communications

1920s

Radar

Sensing

1930s

Space Rockets

Transportation

1930s

Nuclear Fission

Weapons

1930s

Television

Communications

1930s

Computers

Computation

1940s

Transistors

Electronics

1940s

Satellites

Transportation

1950s

Integrated Circuits

Electronics

1950s

Computer Networks

Communications

1970s

CASE STUDY (CONTINUED):

Invention of the Computer: ENIAC

Two kinds of knowledge were involved in inventing the computer. There was the mathematical knowledge about computation from Turing and Von Neumann and there was engineering knowledge about computation from Atanasoft and Mauchly — which we will next see.

In 1942, John Mauchly (physicist) and Presper Eckert (engineer) were both at the University of Pennsylvania and invented the first electronic computer, ENIAC.

This was funded as a research project by the U.S. Army during the Second World War. Herman Goldstine was the program officer in the military research organization who had decided to fund Mauchly ’ s and Eckert ’ s research proposal.

Mauchly ’ s idea for a computer had occurred in 1941, when he learned of some computational electronics invented by a physicist, John Atanasoff, at Iowa State University. Mauchly visited Atanasoff, who showed him an experimental electronic adder. The heart of Altanasoff ’ s adder was an electronic flip - flop circuit. The flip -

flop circuit connected two vacuum tubes together — so that when an electronic signal arrived at the grid of one tube, it turned its state to conducting (on) while at the same time the other connected tube was turned to nonconducting (off).

Mauchly saw he could construct a binary number system with Altanasoff ’ s flip - flop circuits (Brittain 1984). A signal applied to the grid of a tube could turn on the tube, and thereafter it would stay on and keep conducting (state of 1).

Another signal might then be applied to the tube, and it would turn off (state of 0).

In either state, the flip - flop circuit would be stable, until a new signal arrived to flip it. Mauchly outlined in the memorandum how one could use a set of flip - flop circuits to express a number system, binary numbers and so construct a reconfigurable calculating machine.

John Mauchly was born in Cincinnati, Ohio, in 1907. In 1932, he received a PhD in physics from Johns Hopkins University. While teaching physics at Ursinus College (from 1933 to 1941), Mauchly had begun to experiment with electronic counters as part of his research on metrology. In 1941, he attended a

56 INNOVATION AND NATIONAL SYSTEMS

summer course in electronics in the Moore School of Electrical Engineering at the University of Pennsylvania. He was then invited to join the faculty.

In the fall of 1942, Mauchly wrote a proposal for building a computer and submitted it to the U.S. Army Ordinance Department. Colonel Paul N. Gillon, Colonel Leslie E. Simon and Major H. H. Goldstine were in charge of funding research for future military applications. They approved the idea and gave an R & D contract to the University of Pennsylvania to build the machine, which Mauchly would call the ENIAC. Mauchly would be the research engineer and Eckert the chief engineer. J. G. Brainerd was project supervisor; and several others made up the team, including: Arthur Burks, Joseph Chedaker, Chuan Chu, James Cummings Leland Cunningham, John Davis, Harry Gail, Robert Michael, Frank Mural, and Robert Shaw. It was a large undertaking, and required a large research team (Brainerd and Sharpless 1984).

Eckert and Mauchly

( http://en.wikipedia.org , John Mauchly 2008)

When constructed, the ENIAC took up the whole of a large air - conditioned room, whose four walls were covered by cabinets containing electron - tube circuits. The circuit cabinets weighted 30 tons and drew 174 kilowatts of power. The major problem was tube failure. Mauchly and Eckert had calculated that of the 17,468 tubes they were using in ENIAC were likely to have on the average one failure every eight minutes. This would have made ENIAC useless, not able to compute any thing that took eight minutes or longer. Cleverly, they decided to run the tubes at less than one half their rated voltage and one fourth of their rated - current — which reduced the failure rate to one tube about every two days.

Eckert and Mauchly had also invented a memory unit consisting of an electro - acoustic delay line in a longitudinal tube filled with mercury. Their idea was to store a string of bits of ones and zeros as successive acoustic pulses in a long mercury tube. An electrical transducer at the start end of the tube would pulse acoustic waves into the tube corresponding to a one bit or no pulse corresponding to a zero bit. As the one - bit pulses and the zero - bit spaces between them traveled the length of the tube, the word they represented would be temporarily SCIENCE BASES FOR TECHNOLOGY 57

stored. When the string of pulses began to reach the end of the mercury tube, the acoustic pulse would be re - transduced back into electrical pulses for reading by the computer system. If it were not to be read it would then be reinserted electrically back into the front end of the tube as a renewed string of pulses and spaces expressing the stored word. This storage could repeat in cycle again until the word was “ read ” and a new “ word ” temporarily stored in the mercury tube. This was the weak point of the technology; the active memory was small because of the physical lengths of cable required (this Forrester would solve later with the SAGE computer).

ENIAC was not easily programmable. To set up a new problem for calculation, one had to physically connect circuits with patch cords between jacks, and all the cabling ran a total of 80 feet in length. The patching task itself could take at least two days. Mauchly and Eckert ’ s concepts for the ENIAC had not included the scientific idea of a stored - program type of computer.

This is when Von Neumann entered the scene. In August 1944, Von Neumann met the Army program manager, Major H. H. Goldstine, while both men were waiting for a train in Aberdeen, Maryland. Von Neumann was working on the U.S. Manhattan project and had gone to the Army ’ s Aberdeen Proving Ground.

Major Goldstine was in charge of the Army ’ s ENIAC project, and had also been to the Proving Ground and was also waiting for the train. Before the war, Major Goldstine had taught mathematics at the University of Michigan, and he recognized the famous mathematician. Goldstine introduced himself and found Von Neumann a warm, pleasant man. Von Neumann chatted amiably with Goldstine, asking him about his work. Later, Goldstine said of the meeting: “ When it became clear to Von Neumann that I was concerned with the development of an electronic computer capable of 333 multiplications per second, the whole atmosphere changed from one of relaxed good humor to one more like the oral examination for a doctor ’ s degree in mathematics ” (Heppenheimer 1990, p. 13).

The ENIAC was already working, and Eckert and Mauchly were already thinking about a better successor, which they intended to call EDVAC (Electronic Discrete Variable Automatic Computer). At Goldstine ’ s urging, the Army was considering awarding the University of Pennsylvania $

105,600 to build the

EDVAC: “ Into this stimulating environment stepped Von Neumann. He joined the ENIAC group as a consultant, with special interest in ideas for EDVAC.

He helped secure the EDVAC contract and spent long hours in discussions with Mauchly and Eckert ” (Heppenheirmer 1990, p. 13).

The contract was granted in October 1944, and Von Neumann completed his famous paper in June 1945: “ First Draft of a Report on the EDVAC. ” This was one of the most influential papers in what was to become computer science — science underlying the technology of the computer . Goldstine circulated the draft with only Von Neumann ’ s name on the title page: “ In a later patent dispute, Von Neumann declined to share credit for his ideas with Mauchly, Eckert, or anyone else. So the ‘ First Draft ’ spawned the legend that Von Neumann invented the stored - program computer. He did not, though he made contributions of great importance ” (Heppenheimer 1990, p. 13).

58 INNOVATION AND NATIONAL SYSTEMS

COMPUTER TECHNOLOGY

Mauchly - Eckert - Von Neumann ’ s concept of the stored program computer used the basic technical idea that a binary number system could be directly mapped to the two physical states of a flip - flop electronic circuit. In this circuit, the logical concept of the binary unit “ 1 ” could be interpreted as the on (or conducting state) and the binary unit “ 0 ” could be interpreted as the off (or not conducting state) of the electric circuit. In this way the functional concept of numbers (written on the binary base) could be directly mapped into the physical states (physical morphology) of a set of electronic flip - flop circuits. The number of these circuits together would express how large a number could be represented. This is what is meant by word length in the digital computer. Binary numbers must not only encode data but also the instructions that perform the computational operations on the data. One of the points of progress in computer technology has been how long a word length could be built into a computer.

The design of the early computer used a hierarchy of logical operations. The low-est level of logic was the mapping of a set of bi - stable flip - flop circuits to a binary number system. A next step up had circuits mapped to a Boolean logic (AND, OR, NOT circuits). A next step up had these Boolean logic circuits connected together for arithmetic operations (such as add and subtract, multiply and divide). Computational instructions were then encoded as sequences of Boolean logic operations and/or arithmetic operations.

Finally, at the highest logic level, Von Neumann ’ s stored program concept was expressed as a clocked cycle of fetching and performing computational instructions on data. This is now known as a Von Neumann computer architecture — sequential instruction operated as a calculation cycle, timed to an internal clock.

1. Initiate running the program.

2.

Fetch the first instruction from main memory to the program register.

3. Read the instruction and set the appropriate control signals for the various internal units of the computer to execute the instruction.

4. Fetch the data to be operated on by the instruction from main memory to the data register.

5. Execute the first instruction on the data and store the results in a storage register.

6.

Fetch the second instruction from the main memory the program register.

7. Read the instruction and set the appropriate control signals for the various internal units of the computer to execute the instruction.

8. Execute the second instruction on the recently processed data whose result is in the storage register and store the new result in the storage register.

COMPUTER TECHNOLOGY 59

9. Proceed to fetch, read, set, and execute the sequence of program instructions, storing the most recent result in the storage register until the complete program has been executed.

10. Transfer the final calculated result from the storage register to the main memory and/or to the output of the computer.

The modern computer has four hierarchical levels of schematic logics mapped to physical morphologies (forms and processes) of transistor circuits: 1. Binary numbers mapped to bi - stable electronic circuits 2. Boolean logic operations mapped to electronic circuits of bi - stable circuits 3. Mathematical basic - operations mapped (through Boolean constructions) to electronic circuits

4. Program instructions mapped sequentially into temporary electronic circuits (of Boolean and/or arithmetic instructions)

CASE STUDY (CONTINUED):

Invention of the Computer — Technology, ENIAC

Eckert and Mauchly did not build stored - program EDVAC. The ENIAC group broke up. The University of Pennsylvania hired a new director of research, Irvin Travis, who quarreled with Eckert and Mauchly over patent rights. Eckert and Mauchly had a letter from the university ’ s president, stating that they could hold the patents on ENIAC. But Travis told them that they must sign patent releases to the university.

Mauchly and Eckert refused and resigned from the university in 1946. They formed the Electronic Control Company, which became the Eckert

-

Mauchly Computer

Corporation in 1947. They obtained a $ 75,000 contract from the Census Bureau to develop a computer and used this contract to start their new company. Subsequently, they received a $ 300,000 contract from the Bureau to build the first commercial mainframe computer, which they called the Universal Automatic Computer (UNIVAC).

However, even this amount of money was not sufficient for the development costs. Eckert and Mauchly were forced to solve their financial problems by selling their company to Remington Rand (a typewriter manufacturer) on February 1, 1950. (Later Remington Rand sold the computer company to Sperry, which even later was put out of the computer business by the great commercial success of IBM ’ s mainframe computers.)

After the war, Von Neumann returned to the Institute for Advanced Study in Princeton. He decided to build a computer, and Goldstine joined him, along with Julian Bigelow, who had worked on radar - guided anti - aircraft gun control during 60 INNOVATION AND NATIONAL SYSTEMS

the war. Goldstine and Bigelow built a 23,000 - vacuum - tube computer for Von Neumann in the boiler room of the main building at the Institute. This was the first fully automatic stored - program computer.

A patent on this basic invention would have been valuable. Somewhere between Turing, Von Neuman, Mauchly, and Eckert the novel idea arose. But Von Neumann had published the idea before Mauchly and Eckert had filed for a patent. In U.S.

patent law, this prevented anyone else from filing for a patent. So it happened that there was no basic patent on the stored program computer.

Mauchly was bitter that Von Neumann never acknowledged him as a co - inventor of the stored - program concept. Adding injury to insult, Von Neumann testified against Mauchly in an important trial that challenged Mauchly and Eckert ’ s patent claim from the ENIAC. The judge ruled that Mauchly and Eckert were not the true inventors of the electronic computer. This ruling contributed to Mauchly ’ s final penury: “ Lawyers keep making money, ” said Mauchly toward the end. “ We ’ ve got down to the point where maybe we can buy some hot dogs with our Social Security ” (Heppenheimer 1990, p. 16).

There were many tragic endings in this grand Promethean tale of the invention of the computer. There was a terrible mythic aspect to Mauchly ’ s sad end — like the terrible fate of Prometheus, who gave fire from the gods to mortals. Mauchly died in 1980 from a genetic disease that had terribly disfigured him.

Terrible also was fate of Von Neumann: “ For Von Neumann, it was even worse.

In the summer of 1955, he was diagnosed with bone cancer, which soon brought on excruciating pain. In the words of his friend Edward Teller, ‘ I think that Von Neumann suffered more when his mind would no longer function than I have ever seen any human being suffer. ’ Toward the end there was panic and uncontrolled terror ” (Heppenheimer 1990, p. 16).

Fate turned out terrible for Allen Turing: “ Convicted in England of soliciting sexual favors from a teen - age boy, he was given a choice of prison or hormone treatments. He chose the hormones and soon found his breasts growing. Driven to despair, he made up a batch of cyanide in a home laboratory and died an apparent suicide ”

(Heppenheimer 1990, p. 16).

Even the famous logician, Kurt Goedel had a sad end: “ . . . it was his own personal demons that would drive him to death . . . . After his wife underwent surgery and was placed in a nursing home in 1977, Goedel refused to take any food. He starved himself to death ” (Heppenheimer 1990, p. 16).

NATIONAL INNOVATION SYSTEM

As we see in the invention of the computer, modern science has provided knowledge bases to facilitate the invention of technology — scientific technology. We can view this as a series of eight steps:

1. Scientists pursue research that asks very basic and universal questions about what things exist and how things work. For example, Turing was asking a basic question about mathematical computation, using Goedel ’ s notation.

NATIONAL INNOVATION SYSTEM 61

2. To answer such questions, scientists require new instrumentation to discover and study things. For example, Mauchly visited him, and Atanasoff, Mauchly, Goldstein, and Von Neumann all saw the importance of an electronic computers as a new instrument.

3. Studies are often by different disciplinary groups of scientists and engineers.

For example, Turing, Von Neumann, and Goldstein were mathematicians.

Atanasoff and Mauchly were physicists. Eckert was an engineer.

4. Major advances in science and technology occur when sufficient parts of the puzzling object are put together. For example, Goedel ’ s and Turing ’ s ideas came together in Von Neumann and Mauchly ’ s idea of stored program computing. Atanasoff ’ s flip - flop circuits and Mauchly ’ s binary numbers came together as the electronic mathematical operations.

5.

Scientific and technological progress takes time, patience, continuity, and expense. The U.S. Army funded the ENIAC computer project. The U.S. Air Force would next fund the SAGE computer project and buy them from IBM.

6. From an economic perspective, science and technology can be viewed as a form of societal investment in possibilities of future technologies.

7. Once science has created a new knowledge base, inventions for new technologies often occur.

8. When the new technology is pervasive across several industries (as genetic engineering is across medicine, agriculture, forestry, marine biology, materials, etc.), the technological revolution may fuel a new economic expansion.

Because of the importance of science and technology in innovation, an institutional infrastructure for innovation must include at least two kinds of organizations: (1) universities for science creation and technology invention, and (2) industry for technology invention and commercialization. The research sectors of universities and government provide a macro - level of innovation policy in a nation. High - tech businesses in industrial sectors provide a micro - level of innovation policies.

To understand how all this works as an infrastructure of organizations performing activities, we depict radical innovation as a knowledge - to - utility transformation in a national innovation system, shown in Figure 3.2 .

The macro - level consists of three institutional R & D sectors — universities, industries, and governments — each of which provides activities toward radical inventions.

Below that, a micro - level consists of the set of high - tech businesses that produce new products/services that embody newly invented technologies. The national innovation process consists of the procedures by means of which the macro - and micro - level organizations interact to create radically new technological innovation. It is in the design of high - tech products/services that new knowledge is transformed into new economic value, as the end of the knowledge - to - value transformation.

We have used Venn diagrams to indicate the interactions between the research sectors and businesses. A Venn diagram is a symbolic way of depicting interaction through the overlapping areas of logical sets of things — to show logical interrelatedness — as joint set membership. The overlaps in this set of Venn diagrams indicate the important 62 INNOVATION AND NATIONAL SYSTEMS

National knowledge infrastructure

University

Nature

Government

research sector

research sector

Industrial

Macro-level

research sector

Transforming

knowledge to utility

Micro-Level

Market

High-tech

Business

product/service

Customer

Utility

Application

system

Figure 3.2 Transforming knowledge to utility interrelationships that are necessary for a process of technological innovation to occur as participation in the process by different organizations.

The process of radical innovation consists of many interactions between (1) national research sectors of university and government and industry and between (2) high - tech firms and markets of customers.

The first group of sectors (university, government, and industrial research) constitutes a macro - level of innovation research in a national system.

The focus of university research is science, discovering and understanding nature — experiment and theory. Government research funding is the principle financial support for funding science. Research performed in government laboratories focuses primarily on technology and secondarily on science. Industrial research in different companies focuses primarily on technology. Research toward scientific and technological progress connects industry, university, and governmental sectors to high - tech business. A high - tech business uses research and technology in the design and in the production of its high - tech products.

The second group of high - tech businesses and markets constitute a micro - level of innovation in a nation.

NATIONAL INNOVATION SYSTEM 63

The design and development of new high - tech products and services occurs in the research and engineering departments of a specific business. It is here where technical knowledge becomes translated into product and service designs. It is important to note that the business and the customer are connected through the products of the business. The customer directly experiences the product and the application of the product; and yet the business does not directly experience the customer ’ s application, except indirectly through its product.

These connections are important for several reasons. First, the technological sophistication of a high - tech business is bounded by the research capability of the industrial, university, and governmental R & D infrastructure in which a business exists. The designs of new high - tech products/services use science and engineering knowledge created not only in a business but also in the university and governmental research sectors.

Second, the research and technological capability of a business is known to a customer only through the business ’ s products. In a high - tech business, research and technical capability that do not directly contribute to product performance, quality, or price are not valuable to the business because they are not seen by a customer.

Therefore, businesses must be very selective about what research and technologies engage their interests and investments.

Third, since the satisfaction of a customer with a product depends on its performance in an application (and since a business does not directly experience the application), it is the application that is the greatest source of uncertainty about commercial success in the design of a product. The direct connection between business and customer is through the product/service used in an application.

Both macro - and micro - levels of innovation are essential in any national innovation system. The macro - level of research creates research progress in generic knowledge (science and technology) that provides knowledge bases for new high -

tech products/services. The micro - level of research in particular businesses commercializes new technology into high - tech products/services for sale to markets. The transformation by knowledge of nature (science) into knowledge of utility (innovation) occurs in both the macro and micro levels of innovation.

CASE STUDY (CONTINUED):

Invention of the Computer — Technology: Sage

After the Mauchly – Eckert computer project (sponsored by the U.S. Army), there was a second important innovative computer project that fostered the innovation of commercial electronic computers — Jay Forrester ’ s SAGE project (sponsored by the U.S. Air Force). Mauchly and Eckert had invented the first stored - program electronic computer but had not succeeded in commercialization. The technical problem was in Mauchly and Eckert ’ s use of acoustic delay - lines for the computer ’ s active memory. This gave insufficient memory capacity for business computing tasks. It took a technical improvement as a ferrite - core active memory, which Jay Forrester invented, to make the computer really useful.

64 INNOVATION AND NATIONAL SYSTEMS

Jay Forrester graduated in engineering at the University of Nebraska in 1939. He went to MIT as a graduate student in electrical engineering, obtaining a doctorate.

This occurred during the Second World War, and he worked in military research at the Servomechanisms Laboratory at MIT. During the war in 1944, Forrester participated in studies for an aircraft analyzer and led the Aircraft Stability and Control Analyzer project. Emerson Pugh wrote:

Jay Forrester was described by people in the project as brilliant as well as cool and distant and personally remote in a way that kept him in control without ever diminishing our loyalty and pride in the project. He insisted on finding and hiring the best people according to his own definition, people with originality and genius who were not bound by the traditional approach (Pugh 1984, p. 63In August 1945, the Servomechanisms Laboratory received a feasibility study contract from the Naval Office of Research and Invention for the aircraft analyzer. Two months later, Forrester attended a conference on Advanced Computation Techniques. He wanted to learn about the ENIAC, which Mauchly and Eckert had built. He was interested in using digital electronic computation in his Aircraft Stability and Control Analyzer project. When he saw the digital circuit technologies developed for ENIAC, he thought he could adapt these techniques.

With this in mind, Forrester decided to redirect the analyzer project. In January 1946, he went back to the Navy with a new project proposal to design a digital computer and adapt the computer to the aircraft analyzer. The crux of the problem was the main memory subsystem for the computer. The need for response in real - time for control in the aircraft simulator made the use of both the mercury delay line and the rotating magnetic drum technology too slow for this application.

But then international events resulted in a reorientation of the project. After the Soviet Union exploded an atomic bomb, the United States government decided to build an early warning air defense system. An Air Defense System Engineering Committee was created in January 1950 under the chairmanship of George E.

Vally of MIT to make technical recommendations for such a system. At Vally ’ s suggestion, Forrester ’

s project had been nicknamed “ Whirlwind ” but then was redirected to the new objective of air defense.

Forrester had an idea for a kind of magnetic memory. In April of 1949 he saw an advertisement for a new material called Deltamax, and he had the idea of using it for a novel magnetic memory array. Deltamax was made of 50 percent nickel and 50 percent iron rolled very thinly, and had originally been developed by the Germans during the Second World War. After the war, U.S. naval scientists brought samples of the material back to the United States, and one of the machines required to make it. They engaged an American firm, Arnold Engineering (a subsidiary of Allegheny - Ludlum), to manufacture it as a kind of metal tape and coined the name “ Deltamax. ” Its important property was its sharp threshold for magnetization reversal when an external magnetic field was applied to it.

Forrester ’ s inventive idea was that he could use the magnetic direction of the metal to store information in binary mathematical form either as a one (in one direction of the magnetization in the material) or as a zero (in the reverse direction NATIONAL INNOVATION SYSTEM 65

of the magnetization in the material). He constructed a rectangular set of magnetic loops as an two dimensional array for storing data. These loops were small magnetic toroids, constructed of Deltamax. The tape was wrapped around each loop and connected with two sets of electric wires (to carry signals respectively for writing and reading). One wire could be electrically charged with a “ write ” signal to magnetize the tape in one direction. The other wire was used to sense a “ read ” signal of the direction of magnetization. If Forrester ’ s computer wanted to store a “ one data bit ” in a Deltamax tape loop, the computer would send a write signal to the loop to magnetize it in the proper “ one ” direction. This magnetic direction would stay stored in the loop until the computer wished to read the data bit. It could sense the direction of magnetization in the tape through the read wire. If the computer wished to change the data bit stored on that particular loop, all it had to do was to send a new write signal to reverse the direction of magnetization in the loop (which the computer would interpret as a “ zero ” rather than a “ one ” data bit). Each of Forrester ’ s tiny little ferromagnetic tape loops could store a one or zero as a date bit. Taken together a lot of these storage loops could provide an active memory for an electronic computer. And assembled in three dimensional arrays, these tiny toroids would function as a main memory of the first successful mainframe commercial computers. These ferrite - core memory arrays made the first computers practical.

When Forrester completed the project, he had built the first computer to use ferrite - core arrays for main memory, with an array of 16 X16. (We no longer use

ferrite core memories in computers but instead transistors in IC memory chips, storing millions of data bits in a single chip, and one can see the vast progress in computer memory knowledge that as occurred.) The next stage as the commercialization of the computer project would next occur. Although the university research project had designed the computers for the U. S. Department of Defense, an industrial firm would be required to manufacture it in volume. This is how and when IBM got into the computer business.

In June 1952, John McPherson of IBM participated in a committee meeting of a professional society and there talked to Norm Taylor of MIT. Norm Taylor advised that the MIT Digital Computer Laboratory was looking for a commercial concern to manufacture the proposed air defense system. Taylor asked McPherson if IBM interested. McPherson responded that IBM would indeed be interested.

McPherson returned to IBM headquarters and discussed the project with IBM

executives. It was the kind of opportunity that Tom Watson had been looking for — in order to rebuild IBM ’ s military products division and to improve electronic technology capabilities. IBM told Forrester of their interest. Forrester and his group were reviewing several companies as potential manufacturers of the Air Force computer. They visited Remington Rand, Raytheon and IBM. Forrester chose IBM to build the computers.

With the deal between MIT and IBM concluded, IBM rented office space at a necktie factory in Poughkeepsie, New York and got to work. The Whirlwind II project was renamed SAGE. By the following summer in 1953, 203 technical and 26 administrative people were working on the IBM part of the project.

66 INNOVATION AND NATIONAL SYSTEMS

The Air Force defense system was to have many digital computers at different sites around the country and to be in continual communication with each other.

They were to share data and calculate the paths of all aircraft over the country in order to identify any hostile aircraft. These first computers were to use electronic vacuum tube logic circuits and the ferrite core memory. IBM would use the design principles from the Sage project to design and produce their first commercial computer mainframe product line.

RADICAL INNOVATION AT THE MACRO AND MICRO - LEVELS

We recall that radical innovation involves both macro and micro - levels of innovation . The Machley - Eckert invention (EDVAC) and the Forrester invention (SAGE) both occurred as the macro - level of the U.S. national innovation system. Machley and Eckert proposed and performed a research project (EDVAC) in a university (University of Pennsylvania) funded by the U.S. Army ’ s research program. Forrester proposed and performed a research project (SAGE) funded by the U.S. Air Force ’ s research program. This is sketched in Figure 3.3 .

At this time of the innovation of the new computer industry, all the pioneering research had been performed at universities: Aiken at Harvard, Mauchly and Eckert at the University of Pennsylvania, and Forrester at MIT. Moreover, their National knowledge infrastructure

University research sector

Government research sector

EDVAC

U.S. army research program

SAGE

U.S. air force research program

Industrial

Macro-level

research sector

Micro-Level

Transforming

knowledge to utility

Business

Market

Product/service

Customer

IBM production

Air force

of sage computers

SAGE

defence system

computers

Application

system

Figure 3.3 Radical innovation process — Transforming knowledge to value RADICAL INNOVATION AT THE MACRO AND MICRO-LEVELS 67

research was primarily funded by the U.S. Federal government: Aiken by the Navy (with IBM assistance), Mauchly and Eckert by the Army, and Forrester by the Air Force.

Next the transfer of technology into commercial applications occurred through the formation of new firms and through existing firms entering the new industry.

Mauchly and Eckert formed a new company, financed on a government contract from the Bureau of Census; and IBM produced the Sage computer financed by the Air Force.

When IBM built the SAGE computers for the Air Force, it transferred technology from the university (MIT) to industry (IBM) This transfer of knowledge in the national innovation system of the U.S. provided IBM with a commercially -

important technology advantage for producing the early commercial computers.

IBM innovated production capabilities to build ferrite core memories in volume —

and this was a key strategic competitive advantage to IBM in the new industry of computers.

The commercialization of a radical innovation occurs at the micro - level of a national innovation system. Within a business at this micro - level, there are several business functions for operating the business and innovating new products/services:

• Hard - good businesses require a production function for producing a hardware product; this is usually organized as a factory, or production unit.

• Businesses require a marketing function of selling their products/services; and this is usually organized as a marketing department.

• Businesses require a finance function of controlling the finances of the business operation; and this is usually organized as a finance department.

• Businesses require an administration function for managing the personnel and operations in a business; and this is usually organized as an administration department.

• Businesses now require an information function to acquire and manage information and communication technologies for business operations, and this is

•

usually organized as an information technology (IT) department.

Businesses require an

engineering function

for the capability to design

new products and services; and this is usually organized as an engineering department.

• High - Tech businesses also require a research function for the capability of inventing and developing new technology, and this is usually organized as a corporate research laboratory.

We have seen how university/industry cooperation (MIT and IBM) invented and built the first practical computers for a government customer. IBM then proceeded to sell computers to businesses. Other companies, such as Sperrry Rand, as built and sold computers. But how does one company come to dominate a new industry? This occurs through further research and invention as a next - generation technology.

68 INNOVATION AND NATIONAL SYSTEMS

CASE STUDY:

IBM’s System 360 Computers

We recall that there are two kinds of radical innovations: (1) new basic technology and (2) next-generation of a technology. With a next-generation technology, a new generation of high-tech products and services can be designed to leap over competition and dominate an industry. This is what IBM did when it developed its System 360 computer line. IBM then dominated mainframe computer products for the next forty years (until they became an obsolete product in the 1990s).

Back in the 1950s, the next-generation-technology to be innovated in a next-generation computer was the electronics of transistors substituting for electron vacuum tubes. It was in January 1955, when IBM executives were considering that design of a new product-line of computers. In their discussion, they noted that all their customers wanted machines as fast as possible with as much memory as possible. (Even in early computer applications, the demand for speed and memory was apparent and would remain dominant performance requirements for new computers.) Their management issue was how to finance further research for a next-generation of high-performance computers. In that meeting, IBM

management decided to seek government funds to assist in developing a next generation computer.

One sees here another example of radical innovation at the macro-level of the national innovation system in the U.S. The invention of next-generation technologies is both a risky and expensive research activity. Earlier the U.S. Air Force purchase of computers for air defense had enabled IBM to enter the computer business. Thus IBM executives saw direct benefit in government-sponsored research (macro-level of innovation) and would seek to again use gain such its next generation of computers.

IBM wrote a proposal for research on a next-generation computer, which they called Stretch. The Stretch computer was to be wholly transistorized, replacing the electronic vacuum tubes in the logic circuits. IBM approached the US. National Security Agency (NSA) with its research proposal. In the January 1956, IBM received a contract for memory development and a computer design effort, totaling $1,350,000

dollars. IBM won a second contract from the Atomic Energy Commission’s Los Alamos Laboratory (to deliver the Stretch computer to the AEC within forty-two months for $4.3 million). As the Stretch research project continued, IBM received another contract from the Air Force to develop a transistorized version of its 709

computer—next-generation product.

However, with its radically new technology of transistors—instead of electron vacuum tube—the next generation Stretch project ran into technical difficulties.

The project turned out to be costly and take longer than expected. IBM delivered the computer at the previously agreed price, taking a loss. Moreover, the project had achieved only half its speed objective. Still there were commercial benefits from the Stretch technology, being the first IBM computer to be fully transistorized.

RADICAL INNOVATION AT THE MACRO AND MICRO-LEVELS 69

Government-sponsored research was not always profitable for IBM, but it kept IBM at the cutting edge of knowledge in the early days of the industry. IBM’s ability to translate the research into commercial technology provided IBM with economic and competitive advantage. But this was not easy. IBM had already become large enough that it was beginning to have trouble coordinating innovation within its different divisions and research laboratories. In 1959, IBM had introduced its advanced 7090 computer and was selling several different computer models, from an entry level to advanced models. Yet none of these models were compatible. When a customer desired to move up from entry level performance, they had to rewrite software applications.

IBM’s incompatible product models had become a severe problem. IBM was organized as two divisions—General Products Division for the lower-priced machines (GPD), and Data Systems Division, for the higher-priced machines (DSD). They independently designed their own computer lines. And in addition, two different research laboratories were also designing incompatible computers—the IBM Poughkeepsie Laboratory and the Endicott laboratory. Later in June 1959, IBM was reorganized into three product divisions: 1. Data Systems Division (DSD) for high-end computers 2. General Products Division (GPD) for low-end computers 3. Data Processing Division for sales and service The development of the next large computer, the 7070, was given to DSD.

Responding to this reorganization, the Poughkeepsie Laboratory in GPD began a frantic effort to regain control of the design of the high-end computers. Steve Dunwell of the Poughkeepsie lab was certain that GPD could design a computer they would call 70AB, which would be twice as fast and cheaper than the 7070

computer being designed by DSD.

We can see that at a micro-level of innovation—within a large company—

strategic rivalry can exist between its divisions over innovation strategies.

The two research laboratories, Endicott and Poughkeepsie, were internal rivals, with different designers and different judgments on best design. At that time, T. Vincent Learson had been made group executive responsible for the three divisions. When Learson learned of Dunwell’s decision, Learson ordered that DSD 7070

be continued, with maximum effort. This meant that the Poughkeepsie Laboratory of GPD was not allowed to try to exceed the 7070’s performance, and Dunwell retargeted the 70AB project toward a lower-performance region just above IBM’s current 1401 computer and below the projected 7070. GPD renamed the 70AB project as the 8000 computer series and formally presented the plan to top management.

But in that presentation, there was one IBM executive who did not like what he heard in the GPD product plan. He was T. Vincent Learson, the new group 70 INNOVATION AND NATIONAL SYSTEMS

executive responsible for all three IBM divisions. He was disappointed at the planned performance of the 8000 series. Although some improvements had been planned in circuit technology, to get the machine out fast, GPD planned to use older technology in other circuits. Learson was also disturbed at the lack of planed mutual compatibility of computers across IBM. Learson saw that IBM was spending most of its development resources propagating a wide variety of central processors and little development effort was devoted to other areas such as programming or peripheral devices. After the presentation, Learson decided to force IBM to formulate a coherent product strategy.

Learson asked Robert Evans (a PhD in electrical engineering) to review the 8000 series plans, telling him: “If it’s right, build it; and if it’s not right, do what’s right” (Strohman 1990, p. 35).

Evans decided it was not right. He wanted a product plan for the whole business, not just the upper-line of the GPD for which the 8000 series had been planned.

This brought Evans and Brooks (who was responsible for the 8000 series) into direct conflict. As Brooks (who lost the battle) recalled, “Bob [Evans] and I fought bitterly with two separate armies against and for the 8000 series. He was arguing that we ought not to do a new product plan for the upper half of the business, but for the total business. I was arguing that was a put-off, and it would mean delaying at least two years. The battle . . . went to the Corporate Management Committee twice. We won the first time, and they won the second time—and Bob was right” (Strohman 1990, p. 35).

Bob Evans then proceeded to get IBM to plan for a wholly new and completely compatible product line from the low-end to the top-end. Evans proceeded toward healing the wounds and getting the teams together. He assembled everyone at the Gideon Putnam Hotel in Saratoga Springs, New York, to plan out the new strategy for mutually compatible IBM computers. He assigned Brooks the senior job of guiding the project, which would be called the System/360.

The disagreement was an honest one about how important to the company was a better technology, versus the time to do it. Next-generation product strategy often involves disagreements about technical issues and the relative commercial benefits.

Strategy on next-generation products impacts the careers of the different technical individuals involved, and therefore different product-line strategies for radical innovation can have different impassioned champions in a large firm.

In spite of their battle over strategy, Evans and Brooks respected each other and continued to work together. Evans’s next task was to get the GPD aboard for his total compatibility strategy. For this, Evans needed the help of Donald T. Spaulding, who led Learson’s staff group. Spaulding helped Evans by proposing an international top-secret task force to plan every detail of the new product RADICAL INNOVATION AND NEXT-GENERATION PRODUCT LINES 71

line (System /360) and make necessary compromises along the way. Spaulding brought GPD into the new strategy by making John Hanstra of GPD chair of the group, with Evans as vice-chair.

This task force of top technical experts represented all the company’s manufacturing and marketing divisions. In November and December 1961, they met daily at the Sheraton New Englander Hotel in Greenwich, Connecticut. By the end of December, they had a technical strategy specifying the requirements of a next-generation of IBM computers, the System/360 product line. It took a top-level executive to lead a strategic vision on computer knowledge as compatibility across all product lines. It also required a total management commitment (worked out through internal political battles and eventual consensus) to translate that knowledge vision into a concrete technology plan. That knowledge strategy specified seven basic technical points:

1. The central processing units were to handle both scientific and business applications with equal ease.

2. All products were to accept the same peripheral devices (such as disks and printers).

3. In order to reduce technical risk to get the products speedily to market, the processors were to be constructed in micro-miniaturization in transistors (although integrated circuits had been considered).

4. Uniform programming would be developed for the whole line.

5. A single high-level language would be designed for both business and scientific applications.

6. Processors would be able to address up to two billion characters.

7. The basic unit of information would be the 8-bit byte.

There were problems in the product development, but they were all solved and the first 360 computer was shipped in April 1965: “The financial records speak for the smashing ultimate success of IBM’s gamble. In the six years from 1966

through 1971, IBM’s gross income more than doubled, from $3.6 billion to $8.3

billion” (Strohman 1990, p. 40).

RADICAL INNOVATION AND NEXT - GENERATION PRODUCT LINES

At the micro - level of innovation, radical innovations are commercialized as radically new product lines and later as next - generation product lines. We saw in the IBM next - generation technology of the System/360 computers that planning for radical innovation as next - generation products is critical to business success over the long term.

Products can be grouped into product models, product models into product families, and product families into product lines. Product lines provide the highest grouping of products. For example, in the automobile industry, different product lines 72 INNOVATION AND NATIONAL SYSTEMS

cover different uses: passenger cars, trucks, motorcycles, and tractors. A product line is a class of products embodying similar functionality and similar technology, and produced by similar production processes for a major market and application segment. For example, in the second half of the twentieth century, the product lines in computers became: (1) mainframe computers, (2) super computers, (3) minicomputers, (4) workstation computers, and (5) personal computers. When the twenty - first century got underway, computer product lines had changed to: (1) supercomputers, (2) laptops, (3) smart phones.

A business will vary a product design into a family of products to cover the niches in a business ’ s market. Variation of a product into different models of a product family is a redesign problem, altering the needs and specs of the product model to improve focus on a niche market. A product model may be replaced by an improved or redesigned product model. The time from the introduction into the market of a product model and its replacement by a newer product model is called a product - model lifetime .

But all the product models are made obsolete by a next generation of a technology; and the newer product models are called a new - generation of the product . Product generations are designed to provide substantial improvement in performance, features, or safety and/or to substantially reduce product. Product lines have finite lifetimes when technical obsolescence occurs in the product line.

Radical innovation of next - generation product lines based on next - generation technologies disrupts business competition in an industry.

When a radical innovation is commercialized, it will impact any industry using older technology that the radical innovation makes obsolete technology. This has been called a technology discontinuity . As we earlier noted, in his book, Capitalism, Socialism and Democracy , Joseph Schumpeter called this next - generation technology “ creative destruction ” in an economy (Schumpeter [1942] 2008).

CIRCULAR INTERACTIONS IN A NATIONAL

INNOVATION SYSTEM

The flow of knowledge in the process of radical technological innovation is linear , in that knowledge does ultimately flow from nature into utility. Yet the infrastructure processes of generating and using knowledge are interactive and circular . We had drawn the sketch of a national innovation system deliberately in terms of Venn diagrams to emphasize that there are important knowledge interactions between the research sectors and high - tech businesses. Such knowledge interactions in a research infrastructure next can be shown as circular , as depicted in Figure 3.4 .

There are two cycles of knowledge - creation activities: (1) a science - technology circular interaction and (2) a market - business circular interaction.

CIRCULAR INTERACTIONS IN A NATIONAL INNOVATION SYSTEM 73

Nature

Nature

Science

vation

Science

and

Inno

Technology

ϩ

Technology

ventionIn

ϭ

Engineering

vation

Business

and

Business

gical inno

market

production

Economic

of

market

high-tech

hnolo

Utility

products

/service

Tec

Figure 3.4 Circular interactions of knowledge for radical innovation Circularity in Science and Technology Knowledge Interactions a.

Science discovers aspects of nature (in experiments) never previously observed and deepens human understanding of the processes of nature.

Nature provides opportunities and directions for further scientific studies.

b. Science explains (theory) such nature, providing technology with a knowledge base on nature in which to invent new technology and improvements to existing technologies.

c. Technology, in turn, provides the improvement of instruments to science —

with which to discover new aspects of nature, both improving and extending the scientific observation of nature. Technology also stimulates further scientific research to improve the understanding of the nature, deepening the knowledge bases for technology.

Circularity in Business and Market Knowledge Interactions a. Technology provides engineering with a knowledge base, which engineers use to design the technical aspects of products and services. New technical knowledge provides the opportunity for technological innovation of the design of high - tech products or services. High - tech means improving the performance of products or services by use of new technology in their design.

74 INNOVATION AND NATIONAL SYSTEMS

Engineering, in turn, stimulates further technological research to improve technologies.

b. Engineers employed in a business then design the new high - tech products for the business to produce (or the high - tech service for the business to deliver).

Business, in turn, selects products/services to be designed by engineering.

c.

Business sells high

-

tech products/services to a market of an economy

—

developing and growing the market to demand the high performance of the innovative products/services. In turn, market applications of these products/services provide information for a business ’ s engineering department to design improvements in high - tech products/services.

And in this circularity, there has emerged a specialization of research roles in a modern national innovation system, as illustrated in Figure 3.5 .

The institutional sponsors of R

&

D are principally industry and the federal

government (with some support by state governments and private philanthropic foundations). The performers of R & D are industrial research laboratories, governmental research laboratories, and universities. Industry is the principal producer of technological progress. Universities are the principal producers of science. Industry is the principal producers of technology. Government is the principle sponsors of science.

In the second half of the twentieth century, the U.S. ratio of R & D expenditures to gross domestic product (GDP) ran about 2.5 percent. Other nations ’ R & D/GDP

ranged from 1 percent to 3 percent. In the United States in 2004, about 70 percent Government

support of

Scientific

Nature

science

progress

Science

knowledge

base

Technology

Society

sees science

invention

through technology

Engineering

use of

Industrial

technology

support

of

technical

progress

Business production of

high-tech

products/services

Economic value

in market

Utility

Figure 3.5 Transforming knowledge of nature into utility PERFORMANCE OF NATIONAL INNOVATION SYSTEMS 75

of R & D was sponsored by industry and 30 percent by government. In 2004, about 70 percent of U.S. R & D was performed by industry, 14 percent by universities, and 12 percent by governmental laboratories. Sources of the R & D funds were about 71 percent from industry, 24 percent federal government, and 5 percent university ( http://www.nsf.gov 2008).

Government funds science and university research advances science — providing the scientific knowledge base for new technology, of which industry is the principle creator.

PERFORMANCE OF NATIONAL INNOVATION SYSTEMS

Managing the whole of a national innovation system falls under the topic of science and technology policy of a national government. To evaluate the relative performance of national systems, the Organization for Economic Co - operation and Development (OECD) periodically reviews the innovation policies of different countries. In 2006, OECD reviewed the innovation policy for Switzerland and noted the four strengths of Switzerland in innovation:

1. Strong industrial research and innovation. Switzerland has a strong and varied industrial research base.

2. A high - quality research - oriented university sector and a well - developed research infrastructure.

3. A strong services sector. This sector, which includes a highly developed financial industry, plays an increasing role in the Swiss economy and innovation system.

4. Orientation toward high quality. A pervasive orientation toward high - quality products and services throughout the Swiss economy contributes to high standards, performance and reputation ” (OECD 2006, p. 8).

If this example is indicative of the kinds of strengths a nation ’ s innovation system needs to have in the modern globally competitive world, then one might conclude that these seven aspects of the research sector system are also important: 1. A national innovation system should have strong research capabilities in its industrial sectors.

2. A national innovation system should have a high - quality research - oriented university sector.

3. A national innovation system should have at least one strong internationally competitive industrial or service sector.

4. A national innovation system should have a culture of valuing high quality of performance.

76 INNOVATION AND NATIONAL SYSTEMS

5. A national innovation system should be supported by government policies that strongly fund appropriate R & D activities in universities and selected mission areas.

6. A way to identify cutting - edge science (which can help surpass current technology) should be a national science and technology policy priority.

7. Science and technology policy must support research for technology improvement in current industries, and research to establish new internationally competitive industries in new technologies.

KEY IDEAS

In the case study of:

• Invention of the First Computers, ENIAC and SAGE

• IBM ’ s System/360 next - generation computers We have examined the key ideas of:

• Scales of technological innovation

• National innovation system

• Next - generation technology

• Next - generation product lines

• Circularity of knowledge interactions

• Performance of national systems

4

INNOVATION RESEARCH

INTRODUCTION

We have reviewed how the radical innovation process requires research. But what kinds of research — in order to transform knowledge of nature into economic utility?

The kind of research to both invent and commercialize high - tech products/services has been called a logic of innovation research . We ask these questions:

• What are the goals of research in each stage of the innovation logic?

• What are the corporate costs in radical innovation?

As indicated in Figure 4.1 , we focus on the logical stages of innovation research to complete the process of innovation in both invention and commercialization.

Logic of Research for Innovation

Research

Invent

Commercialize

Market

Nature

Science

Technology

Economy

Utility

Exist

Discover

Manipulate

Embed

Use

naturally

nature

nature

nature

nature

The world as material

The world as money

Figure 4.1 Innovation process

77

78

INNOVATION

RESEARCH

CASE STUDY:

Technological Innovation of Nylon

An important case of research in a corporation that resulted in a major radical innovation is the case of DuPont’s innovation of nylon. David A. Hounshell and John Kenly Smith Jr. described how DuPont began a project for the scientific investigation of polymers that finally resulted in the first artificial textile fibers.

They summarized: “Nylon became far and away the biggest money-maker in the history of the DuPont company, and its success proved so powerful that it soon led the company’s executives to derive a new formula for growth. By putting more money into fundamental research, DuPont would discover and develop new nylons—that is, new proprietary products sold to industrial customers and having the growth potential of nylon” (Hounshell and Smith 1988).

Moreover, this business impact of new scientific technology did operate as a kind of Kondratiev wave, promoting economic growth for over fifty years: This faith seemed to be borne out in the late 1940s arid early 1950s with the development of Orlon and Dacron and the continued spectacular growth of nylon. DuPont . . . effected a revolution in textile fibers, and the revolution propelled earnings skyward. In fact, DuPont, which for its first hundred years had been an explosives manufacturer and had in this century become a diversified chemical company, was by the 1950s, in many respects, a fibers company that had some other businesses on the side (Hounshell and Smith 1988).

The research project responsible for the invention of nylon was possible because DuPont had earlier created a research program to perform fundamental, scientific research: “Twelve years earlier, on December 18, 1926, [Charles] Stine (then the director of DuPont’s Chemical Department) had taken the first step down the very long road to nylon by submitting to the company’s executive committee a short memorandum entitled

Pure Science Work” (Hounshell and Smith 1988).

Research at the DuPont Company was organized only for commercialization in developing chemical products and production processes—DuPont’s Chemical Department. Stine was proposing to create a new organization within DuPont to step back earlier in the innovation process—to science. Stine’s memorandum proposed that DuPont also undertake research “with the object of establishing or discovering new scientific facts” (Hounshell and Smith 1988).

Stine contrasted this science research against DuPont’s current research: “which applied previously established scientific facts to practical problems . . . fundamental or pioneer research work by industrial laboratories was not an untried experiment but rather had been successful in the German chemical industry and in the General Electric Company. But there were some important gaps in their (university) programs; and ‘applied research is facing a shortage of its principal raw materials (new chemical science)’” (Hounshell and Smith 1988).

Next in the memorandum, Stine listed four reasons why DuPont should spend its money on this new kind of industrial chemical research. First on his list was LOGIC OF RESEARCH FOR INNOVATION 79

the scientific prestige and public relations value to be gained through the presentation and publishing of papers. The second reason was that interesting scientific research would improve morale in the department and make the recruiting of Ph.D.

chemists easier. The third reason was that the results of DuPont’s pure science work could be used to barter for information about research in other institutions.

The fourth reason (and for Stine the most important reason) was that pure science might give rise to practical applications.

Stine’s vision of science-giving-rise-to-applications shows that he had a deep understanding of the concept of scientific technology—that in the modern world, new basic technology is based on new scientific discoveries.

The executive committee approved Stine’s idea and suggested he submit a detailed proposal for a new research center in DuPont. Stine submitted one in March 1927; and the committee approved a research budget. Beginning in April 1927, fundamental research in DuPont’s new research center was to receive $25,000 a month. Stine received $115,000 to build a new laboratory for fundamental research. (DuPont chemists in the existing Chemical Department quickly dubbed the new lab Purity Hall.) With the new building under construction, Stine began to look for twenty-five scientists to fill it.

The management role that Stine was playing was as a laboratory director, and he would need a staff of scientists to perform research projects in the new research laboratory.

A central management responsibility of laboratory director is the establishment of proper procedures by which scientists are selected as research project leaders.

Stine believed that his program would succeed only if he could hire “people of proven ability and recognized standing in their respective fields.” But he realized that it would be difficult and maybe impossible to recruit such people, all of whom worked in academia and had developed specific lines of research. Alternatively, he proposed to do what General Electric and Bell Labs had done successfully: hire

“persons of exceptional scientific promise but [with] no established reputation. In this case the nature of their work can largely be determined by us.” LOGIC OF RESEARCH FOR INNOVATION

We now look at the details of research in a national innovation system — as shown again in Figure 4.2 .

Stages 1 and 2 — Scientific Understanding and Feasibility of Manipulation The first kind of research that begins a radical innovation lies in science. Scientific research goals are to discover and explain nature. Scientific research can make velopment

ocess

ormation

vement

vement

ation prv

ic feasibility of manipulation

very and understanding nature

acturing product design

eting and sales of product

vention—functional manipulation

Disco

echnical feasibility prototype

Scientif

In

T

Functional application prototype

Engineering product design

Engineered product prototype

Manuf

Production process de

Full-scale production system

olume production of productV Mark

Production impro

Product impro

wledge-utility transf

as a linear inno

Kno

Science research

echnology research

research

research

T

Engineering

Commercialization

Utility

Research

Research in a national innovation system

e 4.2

e to utility

alue

Figur

Customer

system

et

Application

Mark

wledge to v

vernment

ransforming knowledg

Go

T

research sector

Product/ service

wledge infrastructure

Industrial

orming kno

research sector

versity

ransf

National kno

Uni

T

research sector

Business

80

LOGIC OF RESEARCH FOR INNOVATION 81

inventions in the creation of new research instrumentation or instrumental techniques. For inventions, science provides the scientific feasibility that an invention can actually be made.

Discovery occurs by (1) observation of and/or experiments on nature and (2) analysis of the observations and/or experimental results for empirical patterns in nature. Both observations and experiments require instruments. The difference between observations and experiments is that observations note nature as it exists, whereas experiments control the conditions of observation. Thus, experiments can often yield more information about nature than uncontrolled observation. Theory is constructed on the basis of empirical data (from observations or experiments).

Theory is tested for validity by comparing predictions of the theory to empirical data. This interaction between (1) theory construction and validation and (2) empirical data patterns from observation and/or experiments is the key of scientific method — understanding of nature as empirically grounded theory.

Scientific feasibility shows that the scientific basis of nature exists—upon which a technological invention could be created.

CASE STUDY (CONTINUED):

Technological Innovation of Nylon

By the end of 1927, Stine hoped to have hired at least eight scientists for his new research laboratory to perform research projects in fundamental scientific research.

But by the beginning of 1928 he had succeeded in hiring only one man. This was a thirty-one-year-old instructor from Harvard University named Wallace Hume Carothers. But it was a critical hire for Stine, as this one scientist alone would start the new lab with a major discovery. Hounshell and Smith commented on Carothers:

“During his nine years at DuPont, the brilliant but mercurial Carothers both made important contributions to polymer science and led the research effort that produced neoprene synthetic rubber and nylon” (Hounshell and Smith 1988).

Wallace Hume Carothers (1896–1937) became interested in chemistry as a high school student in Des Moines. In the fall of 1915, he entered Tarkio College in Missouri as a science major and simultaneously accepted a position in what then was called the “commercial department” of the college. Later, he assisted in the English department. Carothers prided himself on his ability to write clear and forceful prose, a skill evident in his scientific papers. When his chemistry professor, Arthur M. Fardee, left Tarkio for the University of South Dakota, Carothers filled in as the chemistry instructor, though he was still an undergraduate. After graduating in 1920, he obtained a master’s degree in organic chemistry at the University of Illinois. Then, joining Fardee in South Dakota, Carothers taught courses in analytical and physical chemistry long enough to save enough money to return to Illinois for a Ph.D.

82

INNOVATION

RESEARCH

Back in Illinois in 1922, he soon became bored with the drudgery of graduate school. He wrote to a friend that “it contains all the elements of adventure and enterprise that a nut screwer in a Ford factory must feel on setting out for work in the morning.” Only his research, which was driven by “the insatiable curiosity of the true scientific spirit,” kept him jumping the hurdles toward his Ph.D. For the 1923–1924 academic year, the college awarded him the chemistry department’s most prestigious fellowship, the income from which Carothers used to support his passions for billiards and coffee. He completed his doctorate in 1924 and remained at Illinois as an instructor for two years until he was hired by Harvard University. Although Carothers believed Harvard was the “academic paradise” for teaching, he really did not like to teach. Soon DuPont offered him a job that ostensibly entailed nothing but research.

Carothers resisted Stine’s recruitment efforts until he was absolutely sure he would really be allowed to perform purely scientific research at DuPont. At Harvard, Carothers was doing research on the thermal decomposition of ethyl-metal compounds. He asked Stine if he could continue this work in DuPont.

Stine told him that he could do research on whatever he pleased. But Stine added that the growth of a research group working for him would depend on his “capacity for initiating and directing work that DuPont considered worthwhile undertaking” (Hounshell and Smith 1988) Stine suggested to Carothers that research in the emerging field of polymer science would be of interest to DuPont. He asked Carothers if he had any ideas for research in this new scientific area. To follow up, Stine dispatched one of his assistants, Hamilton Bradshaw, to Cambridge to see Carothers.

In the intellectual Bradshaw, Carothers found a kindred spirit. They talked about scientific challenges offered in polymer research. They talked about the level of research support that DuPont would provide. Bradshaw raised DuPont’s salary offer. He offered Carothers $5,000, versus the $3,200 a year that Carothers was receiving as a lecturer at Harvard. Ten days later Carothers decided to join the DuPont Company.

After accepting the position, Carothers wrote to Bradshaw to propose research on the fundamental issues in polymers. He had read German research papers on the topic and understood that chemists were debating whether polymeric substances were held together by the same forces that operate in smaller molecules or whether some other kind of force—peculiar to these large molecular substances—was involved.

One the side of the debate for ordinary atomic forces, the chemist Hermann Staudinger had published an article asserting that polymeric molecules were practically endless chains of smaller molecules held together by ordinary chemical bonds. Carothers now thought of a research method to provide empirical evidence for this view. He wrote Bradshaw: “I have been hoping that it might be possible to tackle this problem from the synthetic side. The idea would be to build up some very large molecules by simple and definite reactions in such a way that there could be no doubt about their structures.”

Carothers had defined a research project he would began when he joined DuPont—a fundamental research project, a science project—and in an area of LOGIC OF RESEARCH FOR INNOVATION 83

interest to DuPont. In February 1928, Carothers moved to Wilmington, Delaware.

He wrote about his new job to a friend:

A week of the industrial slavery has already elapsed without breaking my proud spirit. Already I am so accustomed to the shackles that I scarcely notice them. Like the child laborers in the spinning factories and the coal mines, I arise before dawn and prepare myself a meager breakfast. Then off to the terrific grind arriving at 8, just as the birds are beginning to wake up. Harvard was never like this. From then on I occupy myself by thinking, smoking, reading, and talking until five o’clock.

The research issue of his project (as he had proposed to Bradshaw) was to resolve the scientific controversy over polymerization. His research method was to build long-chain molecules one step at a time, by carrying out well-understood chemical reactions. He chose one of the simplest reactions to test his hypothesis as alcohols reacting with acids to form esters: “He added a clever experimental idea.

He reasoned that if each reacting molecule has only one alcohol or acid group, then one reaction is all that can occur. But if the molecules have a group capable of reaction at each end, then the molecules can continue to react, building up a long chain in the process” (Hounshell and Smith 1988).

The compounds for his chemical reactions each had an alcohol group on each end with analogous acids. On each end with an alcohol group connecting to an acid, Carothers chained together larger and larger polymers. He built up polyesters that contained up to twenty-five alcohol-acid pairs and had molecular weights between 1,500 and 4,000. By studying these and other related types of compounds, Carothers was able to demonstrate experimentally that polymers were just ordinary molecules, only longer. This was a key research conclusion for the new chemistry area of polymers. His co-worker Julian W. Hill later recalled that this work “finally laid to rest the ghost . . . that polymers were mysterious aggregates of small entities rather than true molecules” (Hounshell and Smith 1988).

Carothers published his findings in a landmark paper in Chemical Reviews. His scientific project had resulted in a contribution to scientific progress in chemistry.

So Stine’s new chemistry laboratory for DuPont had begun with a scientific research project that did produce a major contribution to scientific progress. By the end of 1929, Stine felt that his fundamental research program had been “marked by excellent progress since publication of results has occasioned favorable comment from numerous sources, and several of our men are earning increasing recognition in the scientific world” (Hounshell and Smith 1988).

After Carothers’s scientifically successful project, there next occurred a dramatic shift in the purposes of Carothers’ research projects—away from scientific discovery and toward technological invention. This was to prove that Stine’s vision of scientific technology was correct. New scientific discoveries for DuPont did lead to new technologies for DuPont. Carothers next accomplished this—and in a big way. From his polymer synthesis research methodology, Carothers invented the first artificial rubber, neoprene, and the first artificial silk, nylon.

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Wallace Hume Carothers in his DuPont Lab

(http://heritage.dupont.com/2007)

Stage 3 — Invention

When a basic invention is created, then knowledge moves from science toward technology . A basic invention is a clever way to manipulate nature.

A basic invention shows how a particular function—by the manipulation of nature—can be accomplished by a new technology.

Stage 4 — A Technically Feasibile Prototype Whoever creates a basic invention — a scientist or an engineer or a technologist — the next logic stage of research is to demonstrate the invention. This step is accomplished by building a technically feasible prototype . This refines the invention to demonstrate technical performance in a range of usability.

A technically feasible prototype shows that the invention will perform a particular function—but is not necessarily ready for an application.

The issue in technology research is invention . The point of the research is to invent some new technology. In the case of nylon, the invention was a new artificial fiber.

Carothers and his colleagues accomplished this invention by altering the states of nature. The process for altering these states was an experimental logic for manipulating nature — chaining smaller molecules into longer chains of molecules.

LOGIC OF RESEARCH FOR INNOVATION 85

Often in the invention of technologies, the experimental procedures and instruments of science become the industrial processes of a new technology. Here we see that the experimental technique of chaining long polymers would become a chemical process for the production of nylon. And we also see that the experimental apparatus of the molecular still for removing water became an industrial device for producing nylon.

But all technologies can be improved for better control over the transformations of a technology, particularly in the application of a technology for a particular product or service or operations. This application of technology for a particular use involves solving a problem, an adaptive invention to the basic invention. So in the design of new high - tech products/process/service, further research sometimes can also be done to solve technical problems in the use of the technology. This is the second goal of technology - focused research — technical problem solving.

Nature is always more complicated than the use we make of it. Understanding how to simply make nature work for us — technologically — always stimulates technical problems — which further research for invention must solve.

Invention and problem solving are the two major research activities of technology-focused research.

CASE STUDY (CONTINUED):

Technological Innovation of Nylon

At the end of 1929, Carothers had eight other scientists working for him, whom Stine had hired to assist him. Most of them had come straight from graduate school. Carothers’ group would continue to perform chemical synthesis research to explore this new area of building artificial polymers: “In the history of industrial research, April 1930 was a mensis mirabilis. Within weeks of each other, chemists in Carothers’s group produced neoprene synthetic rubber and the first laboratory-synthesized fiber (nylon)” (Hounshell and Smith 1988).

Earlier a DuPont researcher, Elmer K. Bolton, had tried to make an artificial rubber, but without success (using a compound of a short polymer that consisted of three acetylene molecules, divinylacetylene [DVA]). In January 1930, Bolton was promoted from a researcher to a research manager. He became the new assistant director of Stine’s chemistry laboratory. Bolton then asked Carothers to explore the chemistry of DVA.

This new direction from Bolton would change the work of Carothers’s research group from pure scientific discovery to a technological goal—to build synthetic rubber. Stine and Bolton had different directions for research programs—one pure science and the other scientific application. Stine’s aim was new science,—to provide a base for the discovery of new technologies for DuPont. As this had been successful, inventing a new scientific experimental technique for artificial polymer constructions. Bolton then directed his research toward projects with greater application potential.

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(One can conjecture that DuPont’s selection of Bolton as assistant director might have been made in order to balance Stine’s enthusiasm for science with Bolton’s enthusiasm for technology. Industry does not make money from directly science, only directly from technology.)

Following Bolton’s suggestion to examine DVA, Carothers prepared and polymerized very pure DVA. Then Arnold Collins, a chemist working under Carothers, produced clear films instead of the normally yellow ones, Carothers decided to attempt to isolate and identify the impurities in the crude DVA that might cause the yellowing. Carothers had Collins distill the crude DVA. Collins recovered a liquid that his preliminary analysis suggested was a new compound. In April 1930, Collins recorded in his laboratory notebook that an emulsion of the new liquid had solidified “to white, somewhat rubber-like masses. They sprang back to original shape when deformed but tore easily.”

This was the first sample of neoprene, an artificial rubber; subsequently another DuPont lab took over the development and commercialization of neoprene.

In April 1930, another scientist working under Carothers, Julian Hill, discovered a synthetic fiber while attempting to produce chains longer than anyone had ever prepared in the laboratory. This discovery would lead to the invention of nylon.

Carothers had been building long polymer chains by a reaction of a carboxylic acid with an alcohol to give an ester. But by the end of 1929, the polyesters were limited in size to molecular weights of 5,000 to 6,000. Carothers thought that water, which was being formed as a byproduct in the ester - forming reaction, might be hydrolyzing ester groups back to acid and alcohol. The molecular weight - limit might reflect a chemical equilibrium between the forward and backward reactions. So the key to building longer molecules was to find a way of removing that water.

A new piece of scientific instrumentation would be needed to remove the water.

Carothers and Hill invented a device called a molecular still. Their idea was to make the equilibrium polymer in the usual way and then to finish the reaction in the still. In the still, a cold surface would trap water molecules from just above the polymer and remove them from the mixture.

Using the molecular still, Hill next heated an unusual acid - alcohol pair. He and Carothers had decided that the reaction of a sixteen - carbon - chain acid with a short three - carbon - chain alcohol might promote the formation of longer molecules. Upon removing a sample of the new polymer from the still, Hill observed that the molten polymer could be drawn into fibers. He then made the important discovery that after being cooled, these pliable filaments could be stretched, or cold - drawn, to form very strong fibers. Further tests on the sample showed that it had a molecular weight of over 12,000, far higher than any previous polymer.

This was a scientific invention of a new technology — a very strong artificial polymer. Encouraged by this result, the researchers tried new combinations. They were looking for a technological application of the new fiber; but this polyester LOGIC OF RADICAL INNOVATION — TECHNOLOGY RESEARCH 87

would not be suitable as a textile fiber because it melted below 100 ˚ C, the temperature of boiling water. And it was partially soluble in dry - cleaning solvents.

A textile fiber must stand washing in hot water and dry cleaning.

LOGIC OF RADICAL INNOVATION — TECHNOLOGY RESEARCH

Stage 5 — Functional - Application Prototype The next research step in innovation is to build a functional - application prototype having sufficient technical performance to serve effectively a customer ’ s application.

A functional-application prototype demonstrates that the technical performance of the invention is sufficient to be successful for a particular commercial application.

CASE STUDY (CONTINUED):

Technological Innovation of Nylon

Despite the initial success of science in the new laboratory, Bolton still would not encourage any more “pure science” projects. The lab would only do technology research projects from then on: “Bolton had opposed Stine’s fundamental research program at its inception. Now as it became his responsibility, he believed that fundamental research should be more closely directed or managed to give DuPont direct competitive advantages. Whereas Stine had maintained that fundamental research was justified by the scientific prestige it would bring the company, Bolton emphasized . . . practical applications” (Hounshell and Smith 1988).

Conflicts between Carothers and Bolton began: “Wishing to publish his synthetic-fiber findings, Carothers encountered opposition from Bolton’s new assistant, Ernest B. Benger, who said that: “On the basis of the possible great importance of the work . . . I have taken the attitude that the work should not be published and that our position should be protected by a well planned patent program” (Hounshell and Smith 1988). Carothers was incensed. He felt that Benger had unilaterally changed policy in the laboratory. There was a compromise. Carothers was allowed to publish a year later, if he waited until the patents were filed.

But both Bolton and Benger were excited by Carothers’ and Hill’s research.

Although the melting point of the new polyester polymers was too low for a fiber, they encouraged Carothers and Hill to continue to explore: Carothers and Hill decided to try the chemically analogous polyamides, compounds made by combining an acid and an amine. It was known that simple amides melted at higher temperatures than the corresponding esters. So Carothers and Hill tried to make fibers from a few compounds of this type. Nylon is a polyamide, but in 1930 no satisfactory fibers could be produced, and Carothers and Hill soon gave up on polyamide fibers.

(Hounshell and Smith 1988)

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But Carothers was getting bored with technology research for practical fibers.

He wanted to continue to do pure science, make new scientific discoveries. Bolton was discouraging this kind of project. Moreover, the Great Depression began in the United States in the fall of 1929, and Bolton began cutting back expenses and narrowing research direction in the laboratory.

Carothers was very discouraged, writing: “The only guide we have for formulating and criticizing our own research problems is the rather desperate feeling that they should show a profit at the end. As a result I think that our problems are being undertaken in a spirit of uncertainty and skepticism.” Carothers saw a role for science in the chemical laboratory to keep DuPont up to date on the latest scientific experimental techniques and instrumentation, and to explore new lines of science for future technologies.

Bolton did not agree. But he couldn’t stop Carothers, only discourage him: Carothers had established enough of a reputation for himself, both inside and outside DuPont, that he could continue to work on his scientific studies.

In spite of his concerns about what management expected from him, he followed his theoretical interests in the mechanism of polymerization, moving his research away from linear fiber-forming super-polymers toward the study of cyclic compounds consisting of eight- to twenty-carbon-atom rings, which he found could be made in the molecular still. This work on large-ring compounds completed his classic researches on polymerization and marked the end of his major scientific studies.” (Hounshell and Smith 1988) By 1933, the research for new synthetic textile fibers had not proven successful.

But “the development of a new synthetic fiber remained at the top of Bolton’s list of research priorities, and he kept trying to get Carothers to put at least one man on the problem” (Hounshell and Smith 1988). Carothers responded with a new approach. He was a very brilliant and creative chemist: “So early in 1934 Carothers began a new attack on it. . . . The two problems he faced were the unreliability of the molecular still and the melting points of polyamides, which were apparently too high for them to be spun into fibers” (Hounshell and Smith 1988).

Carothers thought that he might try beginning the polymerization synthesis from a longer-chain compound, which in itself would have a higher melting point:

“On March 23, 1934, Carothers suggested to one of his assistants, Donald P.

Coffman, that he attempt to prepare a fiber from an aminononanoic ester. After spending five weeks preparing the compound, Coffman quickly polymerized it and was convinced that he had made a super polymer, because upon its being cooled, it had characteristically seized the walls of the flask and shattered them.

The following day, May 24, 1934, Coffman drew a fiber from the four grams of polymer that he had made. He recorded in his notebook that he “heated [it] in a bath at 2000˚C just above its melting point. By immersing a cold stirring rod into the molten mass upon withdrawal a fine fiber filament could be obtained. It seemed to be fairly tough, not at all brittle and could be cold drawn to give a lustrous filament. . . . These were the first nylon fibers” (Hounshell and Smith 1988).

LOGIC OF RADICAL INNOVATION — ENGINEERING RESEARCH 89

There was still much more technology research to be done by Carothers’s group, to get just the right chemical compound with desired technical properties.

But what were desirable technical properties? This depended on the product in which the technology was to be used. This was a DuPont management decision.

They chose textiles for women’s stockings: “. . . an early decision (was made) that full-fashioned silk hosiery would be the first large market for the new material. DuPont’s management exercised considerable restraint by not yielding to the enthusiasm of researchers who saw nylon replacing, among other things, cellophane, photographic film, leather, and wool. Each year about seventy million dollars’ worth of silk went into stockings, which were knitted into eight pairs per American woman per year” (Hounshell and Smith 1988).

With this decision in 1934, Carothers’ group now shifted research goals toward technological invention specifically for a product application—engineering research: “Several of Carothers’s assistants prepared polyamides from virtually every combination of di-basic acid and diamine with between two- and ten-carbon-atom chains. Of the eighty-one possible compounds, only five looked promising; eventually, 6–6 (the numerical designation comes from the number of carbon atoms in the diamine and the dibasic acid, respectively), first prepared by Gerard J. Berchet on February 28, 1935, became DuPont’s nylon” (Hounshell and Smith 1988).

LOGIC OF RADICAL INNOVATION — ENGINEERING RESEARCH

Stage 6 — Engineering - Product Design

The next procedural step in innovation is to create an engineering - product design that can perform a function sufficiently well for a market of customers.

Stage 7 — Engineering Product - Prototype

The engineered-product prototype is the design of a product of sufficient performance that can be sold in large volume into the marketplace.

In the engineering stage of innovation, new products, services, and processes are designed. In design, the essential logic is to create morphological and logical forms to perform function. Form and function are the basic intellectual dichotomy of the concept of design. The logical steps involved require first determining the performance required for the function for the customer and then creating an integrated logical and physical form for fulfilling the function. Sometimes the design requires an improvement in the technical performance. This then requires further research — engineering research — to make an engineering design workable. Also, research that provides aides to the design process improves the design capability.

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An example of this is the invention of computer - aided - design (CAD) software and computer systems and their application in modern chemical engineering design.

Research in the engineering stage of innovation can be research for improving technical performance for an engineering design and/or research for providing engineering design aides.

CASE STUDY (CONTINUED):

Technological Innovation of Nylon

Research at DuPont next moved away from Carothers

’

s science and technology

research to the commercialization stage, to develop a production process to produce nylon textiles in large volume. New chemical unit - production processes had to be invented in the commercialization research: “ The early assessments of nylon showed that major production problems would have to be solved. Only one of the two intermediate compounds, adipic acid, was produced on a fairly large scale, and that was in Germany. The other one, hexamethylenediamine, was a laboratory curiosity. Also, methods of controlling the polymer chain growth had to be developed, and once a satisfactory polymer had been made, it had to be converted into a fiber ” (Hounshell and Smith 1988).

Other production processes also had to be developed for spinning the new polymer into a textile fiber:

DuPont ’ s fiber - spinning technology had been developed for the manufacture of rayon and acetate, which did not melt and had to be spun from solutions. The Chemical Department decided to try a potentially simpler, faster, and cheaper process: melt spinning. This entailed melting the solid polymer to a honeylike liquid that would be driven under pressure through a spinner

et, which consisted of a number of very small holes in a metal plate. The extruded filaments would form solid fibers upon cooling . . . . As new problems were recognized, the work was further subdivided. In retrospect, the development of nylon appears to be the solution of thousands of small technical problems . . . (Hounshell and Smith 1988).

Technical problem solving is the core of research in both the engineering and commercialization stages of innovation.

Eventually, all the technical problems were solved. Production plants were built to produce nylon fibers, and production began. “ In October 1938 Stine made his announcement at the World ’ s Fair site . . . . Finally, nylons went on sale nationally in May 1940, and the demand was overwhelming. Convinced that nylon would prove superior to silk, DuPont initially set its price 10 percent higher than that of silk . . . .

LOGIC OF RADICAL INNOVATION — COMMERCIALIZATION

91

In less than two years, DuPont captured more than 30 percent of the full - fashioned hosiery market. Then the United States ’ entry into World War II led to the diversion of all nylon into military uses. During the war, DuPont increased its nylon production threefold, to more than twenty - five million pounds a year; the biggest uses were for parachutes, airplane tire cords, and glider tow ropes ” (Hounshell and Smith 1988).

LOGIC OF RADICAL INNOVATION — COMMERCIALIZATION

Stage 8 — Manufacturing - Product Design

The sequence of activities in the logic of innovation moves from science (fundamental research) to technology (applied research) to engineering design (sometimes requiring further applied research) to production — which then requires production process research. But before large

-

volume production of a new engineered

-

product

design can begin, the engineering design may need to be modified to make it manu-facturable with existing production processes.

A manufacturing-product design modifies the engineering-product design to be volume-produced with production processes.

Often this requires changes in tolerances in parts of the product or even new materials in parts of the product — and sometimes applied research in materials or part production to make this possible. (In the case of nylon, “ the Chemical Department developed a simpler, faster, and cheaper process of melt spinning ” ).

Stage 9 — Production Process Development and Full - Scale Production System

Often in a new high - technology product or service, an existing production system technology is not sufficient to produce the new product in volume. New production processes may need to be invented or improved. Research in production - process development occurs in the innovation stage of commercialization.

Production-process development may require research focused on commercialization of a new high-tech product by inventing and developing new production technologies.

Stages 10 and 11 — Volume Production of Product and Product and Production Improvement

Even after sales of a new high - tech product (service) begin, research can be performed to improve production and to improve product designs. The research in this stage of commercialization is essential and important to competition. A new product 92

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needs to be improved to beat competitor ’ s products, by lowering the cost of production of the product and improving product quality.

Continuing research in commercialization for improving product quality and lowering product cost is necessary to remain competitive.

Commercialization is the final stage of innovation, taking a new product into a market through volume production. The design of a production system is an activity of production engineers, such as chemical engineers or manufacturing engineers.

But in producing a new high - tech product, sometimes new production technologies must be invented or adapted in a new production process. Then research for the development of the new process must be performed. This is called process development research . One goal of research in the commercialization stage of innovation may be for the development of a new production process.

Research that improves the ability to describe and explain the operation of a product as a system is helpful to design ability. In the example of chemical engineering production systems, the unit processes (such as catalysis, or diffusion, etc.) can be represented in physical equations, such as reaction rate or mass transfer, etc. These equations can predict how well an engineering design of a process will operate in production.

In commercialization, the idea of quality is essential to success in commercialization. A “ quality ” product or service will have a competitive advantage against a product or service of lower quality. So the point of commercialization is to produce a quality product. But there are several meanings to the term quality :

• Quality of product performance — How well does it do the job for the customer?

• Quality of product dependability — Does it do the job dependably?

• Quality of product variability — Does it produce the product in volume without flaws.

•

Quality of product maintainability

—

Can the product be serviced and

maintained?

A firm ’ s production system is a series of process subsystems: unit - production processes embedded within a sociotechnical system of the organization of production. Technological change in a production system can therefore occur in any aspect of the production system. For example, in the second half of the twentieth century, management in Japanese industry focused on product and production quality, resulting in Japan excelling in manufacturing quality in the world at that time.

In addition to research in commercialization for production process development, another important aspect of commercialization is the establishment of industrial standards for product use. For example, all electrical consumer products must meet electrical safety standards, usually established by a governmental standards agency. New drug introduction requires meeting standards of safety and efficacy, again established by a government agency (in the United States, the Federal Drug Administration).

LOGIC OF RADICAL INNOVATION — COMMERCIALIZATION

93

In software, standards may be set by an industrial association (as, for example, Internet communications standards) or by dominance of a particular product in the marketplace (as, for example, Microsoft ’ s Windows PC operating system).

Research in the commercialization stage may sometimes be performed to establish industrial product standards.

CASE STUDY (CONCLUSION):

Technological Innovation of Nylon

There is a sad footnote to the story of scientific technology and the innovation of nylon, that of Carothers’ fate: “Not long after the successful discovery of nylon, Carothers went into severe depression. After several months, he recovered and tried to go back to work. In the next two years his bouts of depression became more frequent and severe. They culminated in the summer of 1936 in a major breakdown, from which he never recovered. Personal problems, including the sudden death of his beloved sister, compounded his difficulties. Finally, on April 29, 1937, three weeks after the basic nylon patent application had been filed and two days after his forty-first birthday, Carothers committed suicide with cyanide in a Philadelphia hotel room” (Hounshell and Smith 1988).

In the years just before his death, Carothers had become obsessed with the idea that he was a failure as a scientist. It is true that by 1933 he had worked out most of the ramifications of his one big idea. Perhaps his inability to come up with another one exacerbated his problems. It is equally probable, though, that he was despondent because his mental state had affected his scientific creativity. Elected to the National Academy of Sciences in 1936

and a potential Nobel Prize candidate, Carothers stood with a select few, very near

the pinnacle of his profession (Hounshell and Smith 1988).

In hindsight, one can see the need for industrial research managers to take good care of their creative scientists. Although Bolton valued Carothers, it does not appear he made any extra effort to reward him. Bolton might have promoted Carothers to a “science-star” position—such an Industrial Fellow—with research autonomy and a research budget and staff to continue to pursue science. Bolton could have justified this, both financially and practically—and DuPont could well afford it, considering the billions earned by the new businesses established on polymer technologies. DuPont later did establish such positions for the career paths of their most creative and productive scientists.

A real reward for a key scientist who has created a technology for a company is the position (and research autonomy) of an Industrial Fellow.

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COSTS OF INNOVATION

The logic of innovation establishes categories of different kinds of research issues: science, technology, engineering, and commercialization . Each stage has a different research focus, as summarized in Figure 4.3 .

Each category of research focuses on different kinds of research issues: 1. In science, research focuses on advancing science through inquiry into nature (asking questions about nature) — in order to discover new nature and develop theory to understand nature.

2. In technology research, the focus is on on invention to create and improve a means of manipulating nature and problem solving to improve technical capability.

3. In engineering, research focuses either on developing and improving the principles and procedures for the design of objects or on improving the technical representation of systems (of products or processes).

4. In commercialization, research focuses on inventing or improving production processes or for establishing product/service standards.

And in each stage of research, the cost increases. Particularly, research project costs accelerate in the latter two stages, as illustrated in Figure 4.4 . Technology research begins after an invention to manipulate nature and is focused on improving the performance and safety of the technology. Here, the goals of the research in industry are primarily to reduce the technical risk of new technology that is to be embodied into new commercial products, processes, or services.

The point at which the question of commercial risk (will the research make money?) begins to overtake questions of technical risk (will the technology work?) is in the development of a functional prototype for the new technology. Here, the engineering research and engineering development work begins. In the commercialization stage not only scientists and engineers but also marketing personnel Science

Technology

Discovery

Invention

Theory

Problem-solving

Engineering

Commercialization

Design

Standards

System

Production

Figure 4.3 Types of research issues

COSTS OF INNOVATION 95

Research

Utility

Fundamental

Applied

Technology

Commercialization

research

research

development

New knowledge as discovery

New knowledge as

New knowledge as improvement

New knowledge as proprietary

and understanding and manipulation

functional manipulation of nature

of critical parameters and optimization

skill in the design and production

of nature

in response to identified need

of performance in functional manipulation

of goods and services, utilizing

of nature

functional manipulations

of nature

Functional

prototype

Technical risk

and design

Commercial risk

standards

Scientists

Scientists

Scientists

Scientists

and managers

and engineers

and engineers

and engineers

and managers

and marketing

and marketing,

Cost

personnel

production,

and managers

finance

personnel

and managers

University laboratory and industrial

University

laboratory

laboratory

Industrial

Industrial

laboratory

division

Time

Figure 4.4 Radical new product realization process and production managers participate. In this phase, the engineering and marketing personnel dominate commercialization activity, now with scientists playing a more minor role (solving technical problems in the application as they arise).

In the engineering and commercialization stages, the costs of the research and development, and design and production, begin to escalate from previous research costs (science and technology research) by orders of magnitude — tens times, hundreds, or even thousands more.

Most of the money a business may lose upon the commercial failure of a new product or new service occurs here in the commercialization stage, when the new product fails to deliver a performance or price advantage to a customer against competitive products. This is the commercial risk in innovation.

All the procedural stages in innovation are complex. The innovation process for transforming science into commercially successful utility requires difficult, long, and expensive sets of sequential procedures — from science to technology to engineering to commercialization. The hard fact of innovation is that historically it has sometimes taken a decade for the commercialization stage.

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KEY IDEAS

In the case study of:

• DuPont ’ s innovation of nylon

We have examined key ideas about technological innovation and economic development as:

• Logic of research in innovation

• Stages of research: science, technology, engineering, commercialization

• Costs of innovation

5

INNOVATION AND

CORPORATE R & D

INTRODUCTION

We have examined the logic of research for innovation. Now let ’ s look at the organizational context of that research — the corporate research laboratory. We have just seen that in DuPont a corporate laboratory was established to do scientific research that would lead to technological innovation. Without a research laboratory, a business cannot innovate new high - tech products, or be at the cutting edge of technological progress. The purpose of corporate research is to create (or acquire) and develop new technologies. Such new corporate technologies are used by engineering in the businesses of the corporation to design new high products, services, or processes.

The questions we will address include:

• How was the idea of “ scientific technology ” institutionalized in industry?

• What are the objectives of research in a corporate laboratory?

•

•

How is research for incremental innovation planned?

What are the cultural differences between a research laboratory and business divisions?

• How can the performance of a corporate research laboratory be evaluated?

As shown in Figure 5.1 , we focus on how a corporation organizes research for technological innovation.

97

98 INNOVATION AND CORPORATE R&D

Corporate R&D

Research

Invent

Commercialize

Market

Nature

Science

Technology

Economy

Utility

Exist

Discover

Manipulate

Embed

Use

naturally

nature

nature

nature

nature

The world as material

The world as money

Figure 5.1 Innovation process

We will look at the case of General Electric, when it established its corporate research laboratory in 1901, as described by G. Wise (Wise 1980). In the late 1800s, In 1879, Edison invented the first commercially practical incandescent light bulb with a carbon filament. And in 1882, Edison also innovated the first electrical power plan, which then served the Wall Street district of New York City. Edison sold his Edison Electric Light Company to the newly formed General Electric Company.

Thomas Edison

( http://en.wikipedia.org 2007)

In 1900, General Electric was still producing the carbon - filament type of lamp.

However, it was facing new technological threats to its incandescent lamp business.

One threat was a glower lamp, invented by the German chemist, Walther Nernst,

INTRODUCTION

99

which used a ceramic filament. This produced a higher efficiency, 2 lumens per watt, due to the higher temperature that the ceramic filament could bear. Second, there was the new mercury vapor lamp, developed by the American inventor, Peter Cooper Hewitt. George Westinghouse, a major competitor to GE, supported Hewitt ’ s work and had obtained the American rights to Nernst ’ s lamp. At the time, employed at GE was an outstanding mathematician and engineer, Charles Steinmetz.

Charles Steinmetz (1865

–

1923) was born in Prussia and attended Wroclaw

University for an undergraduate degree. He was finishing his doctorate in 1888

when he was investigated by the German police for belonging to a university socialist group of students. He escaped to Switzerland and soon emigrated to the United States. In New York, Steinmetz worked for an electrical firm building electrical power transformers and published research on magnetic hysteresis. The firm was sold to General Electric, and Steinmetz became a GE scientist. That same year, Steinmetz published the mathematics of alternating currents, making a major contribution to new field of electrical engineering.

In 1901, Steinmetz visited Peter Hewitt at MIT and recognized the commercial threat to GE of Hewitt ’ s mercury vapor lamp. From its physics, Steinmetz understood that gaseous lamps, such as a mercury lamp, would always be more energy efficient than incandescent lamps.

Steinmetz pointed out to GE the urgency of GE performing basic research to meet such threats and for its long - term future.

On September 21, 1900, Steinmetz wrote a letter to Edwin Rice, vice - president of GE ’ s engineering and manufacturing functions. Steinmetz proposed a new “ Research and Development Center ” for GE. Rice and the other key technical leaders of GE, Albert Davis and Elihu Thomson, agreed with Steinmetz ’ s proposal. Rice obtained approval for the new lab from GE ’ s president, Charles Coffin. Davis later wrote,

“ We all agreed it was to be a real scientific laboratory ” (Wise 1980, p. 410). GE

had an engineering department (to which Steinmetz belonged) but not a research laboratory. Organizing the pursuit of science in an industrial laboratory — to explore the basis for new technologies — was a timely and important idea.

Charles Steinmetz

( http://en.wikipedia.org )

100 INNOVATION AND CORPORATE R&D

Next, Rice hired Willis Whitney as a director to start the new lab. Whitney was then a professor at MIT. The historian Wise noted: “ He [Whitney] was not the first professional scientist to be employed in American industry, or even in General Electric. Nor was his laboratory the first established by that company or its predecessors . . . . Whitney ’ s effort marks a pioneering attempt by American industry to employ scientists in a new role

—

as

‘

industrial researchers

’ . . . ”

(Wise 1980, p. 410).

Willis Whitney (1868 – 1958) was born in New York and in 1890 graduated with a bachelor - of - science degree from MIT. He continued at MIT for two years, teaching chemistry as an assistant instructor. Then he went to Germany and obtained a doctorate from University of Leipzig in 1896. He returned to MIT to teach chemistry and performed research in electrochemistry (focusing on an electrochemical theory of corrosion). He then began working for GE to start its new research laboratory.

(He would direct the laboratory until his retirement in 1932.)

Willis Rodney Whitney

( http://en.wikipedia.org 2007)

To defend GE ’ s lamp business, Whitney formulated a research strategy to begin research in the new laboratory. Whitney ’ s strategy was to systematically explore all suitable elements in the periodic table to find an improved substitute for the carbon filament in the incandescent lamp. Whitney began hiring scientists to carry out his strategy. He assigned one possible element to each newly hired scientist, and one of these was William Coolidge, a fresh Ph.D.

Coolidge was born in 1874, the son of a farmer and a shoe factory worker.

He attended a one - room elementary school, then common in rural North America.

Graduating from a small high school as an outstanding student, he went off to college in 1891. He enrolled in a new electrical engineering program at the Massachusetts Institute of Technology (Wolff 1984).

INTRODUCTION

101

Coolidge graduated with a bachelor ’ s degree and decided to become a scientist.

But in 1900 the new universities in the United States were not yet training scientists.

To get a real scientific education, students had to go to Europe. Coolidge went to Germany, returning in 1905 with a doctorate from the University of Leipzig, and a $ 4,000 debt from his graduate education. He took a position at MIT as an assistant to Arthur Noyes (a then notable American scientist). Coolidge ’ s pay as an assistant was $ 1,500 a year.

Earlier, as a professor at MIT, Whitney had taught the young Coolidge in a chemistry class. Now at GE and learning that Coolidge had returned with a German doctorate, Whitney offered Coolidge a job in GE ’ s new laboratory. Whitney offered $ 3,000, which was double Coolidge ’ s MIT salary. Since this would allow Coolidge to quickly pay off his education debt, Coolidge was attracted by the offer. Yet he worried whether he could conduct scientific research in industry. Whitney assured him that he could, as this was the mission of the new GE laboratory. The only constraint was that the scientific research needed to be related to GE commercial interests.

Coolidge accepted the offer and moved from Boston to Schenectady to work at GE ’ s facility. There, he was pleased at the atmosphere for science that Whitney was creating: “ Dr. Whitney had already successfully transplanted a lot of academic atmosphere from MIT to the new laboratory . . . The misgivings I had about the transfer all proved to be unfounded ” (Wolff 1984, p. 81).

Whitney had a research strategy to find a replacement for the carbon filament, and he assigned to each of his researchers the responsibility for explor-ing each of the elements in the periodic table a metal with a melting point equal to or higher than tantalum. Coolidge received tungsten as his assignment.

Whitney had explained the commercial importance of the research, and Coolidge wrote to his parents: “ I am fortunate now in being on the most important problem the lab has ever had . . . . If we can get the metal tungsten in such shape that it can be drawn into wire, it means millions of dollars to the company ” (Wolff 1984, p. 84).

The technical promise of tungsten was that it had a high melting point (higher than carbon) and therefore might be usable to incandesce at a higher temperature, thus physically providing a higher lumen/watt efficiency. We recall that most of industrial research is developmental, some is applied research, and very little is basic research. Coolidge ’ s problem with tungsten was applied research. He knew the attractive feature of tungsten for the lamp application and the need for tungsten to be drawn into a wire for this application. But how to do that?

The applied problem of using tungsten as a filament was its natural ductil-ity. It had a high melting point but was brittle, too brittle to draw into a thin wire shape needed to perform as a lamp filament. In June 1906, Coolidge had his first research break, observing that mercury was absorbed into hot tungsten and, on cooling, formed an amalgam. Next Coolidge experimented making other amalgams of tungsten. He observed that he could make one with cadmium and bismuth absorbed into tungsten. Moreover, this amalgam could be squeezed

102 INNOVATION AND CORPORATE R&D

through a die to make a wire. Coolidge was on the right path. On March 1907, Coolidge discovered that by heating this amalgam to 400 ˚ F, it could be bent without cracking.

Coolidge had a potential candidate material for a new filament; the next step was to figure out how to manufacture it into wire. He visited wire and needle producing factories and toured several in the New England region. There he saw the swaging tech niques used for wire making. Swaging was a process of gradually reducing the thickness of a metal rod by repeated use of dies to draw smaller and smaller diameters. In May 1909, back in Schenectady, Coolidge purchased a commercial swaging machine and altered it to swage his tungsten amalgam into wire.

It did make tungsten wire, but when the wire was heated in a lamp as a filament, it broke. Coolidge researched the failure and learned that it was due to crystallization of the tungsten wire on heating.

This problem was solvable. Coolidge knew that in a similar problem of making ice cream, glycerine was added to prevent the forming of ice crystals as the milk freezes. Using this analogy, Coolidge tried adding another substance to the tungsten amalgam, thorium oxide, to prevent crystallization. It worked. On September 12, 1910, Coolidge achieved a technical success. He had developed a new tungsten wire for a lightening application and a manufacturing process to produce it.

William Coolidge

( http://www.harvardsquarelibrary.org/unitarians/coolidge.html 2007) GE management was pleased and immediately innovated the new material and process. Two years later, by 1911, GE had thrown out all the earlier lamp - making equipment and was producing and selling lamp bulbs with the new tungsten filaments. The research effort had cost GE five years and $ 100,000. By 1920, two - thirds of GE $ 22 million profit came from the new lamps.

INSTITUTIONALIZATION OF SCIENTIFIC TECHNOLOGY 103

GE Scientists in 1909: Irving Langmuir, Willis R. Whitney, William D. Coolidge ( http://www.harvardsquarelibrary.org/unitarians/coolidge.html 2007) In the twenty

-

first century, the General Electric Research and Development Center still existed, located on a site overlooking the Mohawk River, at the out-skirts of Schenectady, New York. In 1982, the GE Center had employed about 2,100 people, and its budget then was $ 150 million. This was a small portion of GE ’ s total research and development budget of $ 1.5 billion. In 2002, GE ’ s new CEO emphasized once again the importance of research capability to GE ’ s long -

term future.

Through the twentieth century, GE continued to produce incandescent lamps as one of the GE busineses. Then in 2010, GE announced the end of that production as higher energy - efficient coiled fluorescent lamps were replacing the incandescent light bulb.

INSTITUTIONALIZA

TION OF SCIENTIFIC TECHNOLOGY

As exemplified by the GE Research Lab (and the earlier example of the DuPont Research Lab), this kind of inclusion of science into the functions of business in the corporate research laboratory was a key event of the twentieth century — the institutionalization of scientific technology. We recall that in a national innovation system, universities are now the principal producers of science and industry the principal developers of technology. Scientific technology (technology development based on scientific understanding) enabled industrial research to couple directly with university science. And this direct connection was critical during the second half of the twentieth century for the dramatic creation and progress: (1) in the new computer science and information technology and (2) to the creation and rapid progress in molecular biology and biotechnology.

104 INNOVATION AND CORPORATE R&D

The first industrial research laboratories in the early twentieth century had been created in the electrical and chemical industries. In addition to DuPont and GE, many other now - famous U.S. corporate laboratories were begun in the early twentieth century: Bell Labs of AT & T, Dow laboratory, GM ’ s technical center, and many others. Industrial laboratories were also established in Europe, such as in I.G. Farben and Siemans. As the twentieth century proceeded, research laboratories became an important feature of high - technology firms worldwide. For example, in 1989, the NEC Corporation in Japan spent 10 percent of sales on research and development, performing R & D in the corporate research laboratory and in divisional laboratories distributed in the 190 businesses of NEC (Uenohara 1991).

The Industrial Research Institute has periodically sponsored surveys of the state of industrial R & D (see, e.g., Whiteley, Bean, and Russo 1994). For example, in 1992, the United States had about 1,000,000 R & D engineers and scientists, with Japan having about 800,000 and Germany about 600,000. The fraction of gross domestic product spent for R & D has been around 2.5 percent for the United States, Japan, and Germany.

The idea to employ scientists in industry as industrial researchers was the beginning of the institutionalization of scientific technology, the second step in institutionalizing a modern innovation system. The first step had occurred a century earlier at the beginning of the nineteenth century, when Germany began reforming its medieval universities into research universities. The third step of institutionalizing a modern S & T infrastructure did not occur until the middle of the twentieth century, when modern national government agencies began systematically funding basic research at universities. As we noted earlier, the modern national innovation system consists of research universities, industrial research centers, and government research - sponsoring agencies. But this was not completed until the second half of the twentieth century. It is a very recent “ cultural evolution ” of modern civilization.

Properly managing this is still a challenge.

For example, in the United States, Herbert Fusfeld identified three critical historical periods of evolution of industrial research: 1870 to 1910, World War II, and the period of declining technical self - sufficiency that began in the 1970s. From 1870

to 1910, new industrial research organizations in the United States began to give the new science - based chemical and electrical industries timely technical support:

“ The companies no longer had to depend on the unpredictable advances generated externally. From this start, there was a steady growth of industrial research up to World War II ” (Fusfeld 1995, p. 52). Next, the experiences of military research in the United States during World War II had consequences for subsequent industrial research: (1) “ A great reservoir of technical advances became available for further development in commercial areas ” ; (2) “ Public expectations were raised for the potential of science and technology in new products ” ; and (3) “ New techniques of systems development were successful in planning and conducting complex technical programs that required the generation of new knowledge as an integral part of the planned program ” (Fusfeld 1995, p. 52).

After that war, both U.S. research and industry dominated the world until the war - devastated economies in Europe and Japan were reconstructed. By the 1970s, industrial competition and progress was again worldwide. U.S. management realized INSTITUTIONALIZATION OF SCIENTIFIC TECHNOLOGY 105

that technical self - sufficiency within a U.S. company was no longer practical: “ For the 30 years following World War II, (U.S.) corporations were able to plan growth strategies based on technical resources that existed internally . . . That situation changed . . . ” (Fusfeld 1995, p. 52).

When the twentieth century ended, there were new conditions of a global economy, for which earlier Fusfeld argued that in the future U.S. corporate research needed to:

• Make strategic use of external resources from outside the company.

• Disperse corporate technical activity beyond a central laboratory.

• Emphasize the effective integration of research into total corporate technical resources.

•

Have a major effort on the organized pursuit of technical intelligence worldwide.

• Have extensive foreign - based R & D activity to support growth in international markets.

• Improve the integration of technical strategy into corporate business strategy (Fusfeld 1995, p. 54).

As another example in 1995 of awareness then of the new global conditions, Lewis Edelheit, GE ’ s senior vice president for Corporate R & D, had emphasized the importance of speed of innovation in the global economy: How can a corporate - level R & D lab renew its role as a vital part of a winning business team? My answer is . . . cost, performance, speed, quality. In the past, innovation often began with a performance breakthrough made at the corporate lab . . . The result was relatively slow development of often costly products that nevertheless might prove winners in the marketplace because they offered capabilities no competitor could match. That does not work today . . . because technology is exploding worldwide. Dozens of labs are at the forefr

ont today whereas yesterday there might have been only one or two (Edelheit 1995, p. 14).

Also, when the twenty - first century began, information technologies were altering the management of research in the global environment. By 2009, the information technologies of the Internet had been facilitating this international research cooperation between corporate research labs and universities. For example, Steve Lohr wrote that Hewlett - Packard had adopted a “ federated model . . . that leverages all the innovative work by outsiders in universities, start - ups, business partners, and government labs . . . . ” Lohr said, “ H.P. now runs a yearly online contest, soliciting grant proposals from universities worldwide. The company lists eight fields in which it is seeking advanced research, and scientists suggest research projects in those fields.

The H.P. grants are typically about $ 75,000 a year, and many of the collaborative projects are intended to last three years. In June (2009), the company announced 61

winners from 46 universities and 12 countries . . . ” (Lohr 2009).

106 INNOVATION AND CORPORATE R&D

Industry/university research collaboration already had a long history in the United States during the twentieth century. But the Internet was making it easier to connect corporate research labs with researchers in universities around the world in the twenty - first century.

OBJECTIVES OF CORPORATE RESEARCH

The general objectives of corporate research are to (1) have a window on science to identify new scientific progress for long - term threats and opportunities, (2) maintain technical competitiveness in existing businesses, and (3) create the technology innovation on which to build new businesses. Corporate research provides a firm a way to perform relevant science and to transform it into new technologies — a corporate institutional capability of scientific technology. For a corporation, incremental innovations protect current businesses, and radical innovation can create a new business.

For example, it was from Steinmetz ’ s visit to German research laboratories to spot new science (i.e., the new glower lamps) that Steinmetz was able to alert GE ’ s management to future threats to GE ’ s lamp business. Before the GE lab was established, there had been no organizational way for GE to systematically anticipate scientifid progress (except in individual initiatives such as those by Steinmetz). Establishing the GE lab provided the critical means for GE to maintain its incandescent lamp business by dramatically improving the filament technology. Coolidge went on to start an x - ray lamp business for GE on the basis of his new tungsten technology. Properly managed and exploited corporate research prepares a company for its future survival.

Successive top management of corporations who (1) fail to properly manage research and (2) fail to exploit successful research will (3) fail to prepare the company for future survival. But this is the challenge of managing corporate research. (In Chapter 1 , we saw how Xerox established a corporate lab, PARC, which invented the revolutionary Altos personal computer system; but then Xerox failed to commercialize it and lost a major opportunity for a prosperous future.) A failure of a past CEO to invest in research and commercialize new high - tech products/services — to prepare for the corporate future — will create problems of corporate survival for a future CEO.

Since one of the principal purposes of corporate research is to create and extend the lifetimes of the company ’ s products, anticipating the need for R & D support for products is an important element of research strategy. When product technologies are changing, product lifetimes will be short. In products with mature technologies, product lifetimes are long. But even in a long - lived product (such as the soap product labeled Ivory Snow), periodic product reformulations do occur to meet changing conditions in market, environment, and safety. Also, to maintain a long - lived product, research and innovation in manufacturing quality must be continually made.

OBJECTIVES OF CORPORATE RESEARCH 107

Desired

profit growth

Projected

Total profits from

profit gap

products C+B+A

to business profits

Product-line contributions

Profits from

Profits from

Profits from

product C

product B

product A

Time

Figure 5.2 Projected contributions from a business ’ s product lines A useful way for management to demonstrate the attention required for product development for a business is the so - called profit - gap analysis, which shows from a financial perspective the profits for new products in a business. Figure 5.2 plots the projections of contributions to a business ’ s profits from each of its product lines.

Some product lines, such as Product A, can be projected to continue to contribute steadily to profits; others, such as Product C, already have sales dropping and will be obsolete due to its aging technology (and the company plans to terminate the product line C). In between these extremes, some product lines, such as Product B, can be revitalized by redesign.

When all the profits from these three types are summed to the total profits of the company, the chart shows the projected profits of the company. If then the management plots on this chart a dotted line of where the desired profits should go, then the area beneath the dotted line and above the summed profits line shows the gap between desired profits and actual profits. This is the anticipated profit gap unless new products are introduced (Twiss 1980, p. 40). The profit gap displays the magnitude over time of the business need for new products.

CASE STUDY:

Genecor R & D

As an illustration of a technology planning process in corporate research, we review the research planning at Genecor in 2008. Genecor

’

s business was in

biotechnology, creating new pharmaceutical products. Ken F. Herfert and Michael V. Arbige described a biannual R & D review for Genencor in order to coordinate 108 INNOVATION AND CORPORATE R&D

business and technology strategies: “ Managing an R & D portfolio for expedited/

on - time commercial delivery across diverse businesses and with a rapidly changing corporate environment is a formidable undertaking . . . . At Genencor, we refresh our business strategies twice a year, including a renewed look at the projects and priorities of R & D investment ” (Herfert and Arbige 2008).

To perform reviews, Genencor used a business integration team. It was led by a business - unit head and consisted of high - level personnel from areas of R & D, manufacturing, intellectual property, and government regulatory responsibilities.

Each business integration team (BIT) was responsible for setting and achieving the specific goals of a particular business unit. “ Genencor has launched over 250

commercial products during its 25 - year existence ” (Herfert and Arbige 2008).

To adjust strategy across business units:

Genecor has established a matrix approach, consisting of business representatives from each BIT in order to resolve and prioritize conflicting goals . . . . In order to manage the corporate portfolio of multiple businesses, a common approach to investment assessment and justification must be followed across all of the businesses . . . a common ‘ Stage Gate ’

view of projects and other portfolio management approaches (was) . . . .

implemented across the businesses. (Herfert and Arbige 2008) Genecor had both short - term and long - term focused research projects: “ The R & D portfolio should be assessed from several perspectives to ensure an appropriate mix of programs, including short and long - term needs . . . . The overall R & D

activities can then be organized using business and portfolio management tools, which connect all the operational pieces ” (Herfert and Arbige 2008).

Genecor was geographically distributed: “ We have laboratories and scientists in multiple time zones and geographies, where each site is critical for overall commercial success of the portfolio . . . we have chosen a ‘ Centers of Excellence, ’

approach, where each distinct location has a critical mass and scientific talent to provide world -

class work in distinct areas of research and/or development ” (Herfert and Arbige 2008).

PLANNING INCREMENTAL INNOVATION

Radical innovation needs to begin in science and therefore must be planned as a next - generation of technology (as we saw earlier in Chapter 3 ). But incremental innovation can be planned differently, more directly as a set of projects, a portfolio of technology - focused projects — with the research logic focused on technology. This incremental technique has been called research - project portfolio management .

An example of research - management procedures — for portfolios of projects of incremental research management

—

is a

Stage Gate procedure (the term “ Stage

Gate ” is copyrighted by R. G. Cooper (Copper 1990)). Cooper suggested that a research idea should be reviewed in stages from discovery to commercialization in TWO CULTURES—RESEARCH LABORATORY AND BUSINESS DIVISION 109

the following stages: (1) discovery stage, (2) scoping stage, (3) build business case stage, (4) development stage, (5) testing and validation stage, (6) launch stage, and (7) post - launch review stage.

A similar procedure for incremental - research strategy was proposed by J. C.

Mankins, who called his procedure Technology Readiness Levels (Mankins 1995).

Mankins suggested eight steps:

1. Basic principles are observed and reported.

2. Technology concept and/or application is formulated.

3. Analytical and experimental critical function and/or characteristic proof - of -

concept are investigated.

4. Component and/or breadboard validation is done in a laboratory environment.

5. Component and/or breadboard validation is done in a relevant environment.

6.

System/subsystem model or prototype is done in an operational environment.

7. Actual system is completed and “ operationally qualified ” through test and demonstration.

8. Actual system is tested in successful mission operations (Mankins 1995).

All such procedures that propose to formulate strategies for innovation are possible only for incremental innovation but not radical innovation. Stages in transforming a research idea into a business idea are possible only when the research idea initially fits into an existing business (or extension of an existing business). Otherwise, it is difficult to build a convincing case for sponsoring a particular project. Different terminology can be used in transferring knowledge from research activities toward product/service commercialization. But the importance and usefulness of any of these procedures is that it effectively integrates R & D strategy with business strategy for incremental innovation.

TWO CUL

TURES — RESEARCH LABORATORY

AND BUSINESS DIVISION

Still, even procedures for planning research in stages run into the problem of the two cultures of technology and business — that of matter and money. For this reason, Paul D. Klimstra and Ann T. Raphael have argued for a dual track of evaluation — in any staged planning of R & D project portfolios — which they called an R & D product pipeline (Klimstra and Raphael 1992). Their recommended procedure for evaluating R & D projects required two parallel sets of evaluations on any project, one by research personnel and a second by business personnel in business units. As research projects proceed from research to product development, they recommended that research strategy should be formally reviewed by business units as part of their business strategy.

110 INNOVATION AND CORPORATE R&D

Stages in evaluation of research projects and/or joint research and business personnel evaluation of R & D projects work well for incremental innovation because current businesses can provide a clear market focus for innovation.

But the cultural challenge remains in corporate research strategy. As we saw earlier, Xerox failed to properly commercialize the brilliant research from its PARC

laboratory, which failed to share its technology strategy. And there are many other historical cases of innovation failures — precisely due to visionary conflicts between business units and research units.

Thus, many failures in successful commercialization of radical innovations have been due to this perceptual gap between technical and business personnel. As we have emphasized, these two are very different subcultures in a corporation — research and engineering as opposed to marketing and finance . The one culture is centered on science and technology and the other culture is centered on power and markets.

This is that great cultural gap — between matter and money.

Klimstra and Raphael emphasized some of the basic organizational differences between research and business subcultures: “ There are long - standing and deeply rooted problems involved in integrating R & D and business strategies . . . . R & D and business organizations have conflicting goals and practices ” (Klimstra and Raphael 1992). Among the conflicting goals and practices, they especially noted the differences about time horizon, financial, product, and methods (see Table 5.1 ).

Research and development (R & D) is a long - term investment. Even the shorter product developments from applied research to development can take two to three years. The longer developments from basic research usually can take up to ten years. Thus, R & D must have a long - term planning horizon and must be strategic.

In contrast, business units are always under the quarterly profit accounting system focused principally on this year ’ s business. Business units are short - term “ operational ” in their planning horizon and are tactical. But if an operational component is not included in R &

D strategy, it will not integrate properly into business unit ’ s planning horizons. Conversely, if business units do not have strategic ten - year planning horizons, they have strategic difficulty in integrating the R & D unit ’ s plans.

This is the basic MOT challenge — properly matching strategic R & D planning with tactical business unit planning.

Another differing characteristic is that organizationally, the R & D laboratories are expense centers , since they do not directly produce income, whereas business units TABLE 5.1 Comparing organizational units

Perspectives

R & D Org

Business - Unit Org

Time horizon

Long term

Short term

Financial

Expense center

Profit center

Product

Information

Goods/services

Method

Technology push

Market pull

TWO CULTURES—RESEARCH LABORATORY AND BUSINESS DIVISION 111

directly produce income. This can create arrogance by the business unit toward the R & D organization, making it more difficult for R & D to elicit support and cooperation from a business units.

The differing characteristics of products from the R & D center and business units also create important cultural differences in the organizations. Business units products are goods and/or services sold to customers of the firms, whereas R & D center products are information and understanding and ideas that must be communicated internally to business units and then embodied into goods and services. This can generate cultural differences on how the different organizations value ideas, since the R & D unit must place a higher value than the business unit on the “ idea ” of a future product.

The methods of the organizational units differ, with the R & D unit valuing scientific and engineering methodology and principles that result in “ pushing technology ” from the opportunity of technical advance. In contrast, the business unit with its direct contacts with the market will be primarily interested in market demand , meeting the needs of the existing markets.

It is in the transfer of innovative ideas from research into innovative products for a business unit that the R & D unit makes money. An innovative product design requires a visionary and collaborative partnership between research and marketing.

The cultural gap should be bridged.

This is the problem of making research relevant to business, and it can be facilitated by proper formal procedures to foster cooperation. As well, a culture of trust and relevance between the research lab and business units must be built up from experience. Finally, for proper coordination of technology strategy and business strategy, both research labs and business units should participate together in the research project evaluation. But the expertise that the participation of business units brings to research - project evaluation decreases dramatically from incremental to radical innovation. In incremental innovation the new products/services are clear, since they embody incremental performance improvements over existing products/services.

In radical innovation, wholly new kinds of products/services may be developed to be used in wholly new markets — differing from current businesses and markets of a corporation. Earlier we saw that it was this inability to see a different business for Xerox — of the Xerox PARC radical innovation of new computer architecture) about which the Office Products Division in the 1980s — that was completely wrong.

Also, we noted that in the 1960s, it was the inability of AT & T executives in the 1960s to see the future of the phone business — as going wireless — that was completely wrong.

The conflict between research and management is timeless. It is basically a political process about conflicts over organizational authority and strategy between research labs and business units. And the more radical the innovation, the more likely will the past market experience of business personnel provide wrong guidance for R & D projects. For example, one industrial researcher, Robert A. Frosch summarized his technical and radical frustration with the short - term nature of business culture:

“ After forty - odd years of working in application - and - mission - oriented research, I have come to believe that the customer for technology is always wrong . . . I have 112 INNOVATION AND CORPORATE R&D

seldom, if ever, met a customer for an application who correctly stated the problem that was to be solved. The normal statement of the problem is either too shallow and short - term . . . . What really happens in successful problem - solving . . . is the redefinition of the problem . . . ” (Frosch 1996).

EVALUATING CORPORATE R & D

R

&

D is an investment in the corporation

’

s future, and so should be ultimately

evaluated on return on investment (ROI). However, in practice, this is difficult to do because of the time spans involved. There usually is a long time from (1) funding research to (2) successful research to (3) implementing research as technological innovation to (4) accumulating financial returns from technological innovation. The more basic the research, the longer is the time to pay off. The more developmental the research, the shorter is the time. For example, the times from basic research to technological innovation have historically varied from seventy years to a minimum of ten years. For applied and developmental research, the time from technological innovation to break even has been from two to five years.

In addition to the varying time spans, the different purposes of research also complicate the problem. We recall that research is aimed at maintaining existing businesses or beginning new businesses or maintaining windows - on - science . Accordingly, evaluating contributions of R & D to existing businesses requires accounting systems that are activity based and can project expectations of benefits in the future and compare current to projected performance. The evaluation of research should be in keeping with the following purposes of research:

1. R & D projects can support current business.

a. The current products can be projected as to lifetimes.

b. This product mix is then projected as a sum of profits.

c. The current and proposed R & D projects in support of current business are evaluated in terms of their contribution to extending the lifetimes or improving the sales or lowering costs of the projects.

2. R & D projects for new ventures can be charted over expected lifetimes.

a. Projects that result in new ventures are charted over expected return on investment of the new ventures.

3. R & D projects can be used for exploratory research.

a. These projects cannot be financially evaluated, and should be treated as an overhead cost. They should only be technically evaluated on their potential for impact as new technologies.

Many scholars have tried to simplify this logic by trying to create analytical shortcuts to the evaluation problem. For example, Richard Foster, Lawrence Linden, Roger Whiteley, and Alan Kantrow (Foster et al. 1985) suggested trying to trace the following logic as an expression: Return ϭ Profits/R & D investment . This might EVALUATING CORPORATE R&D 113

be expanded as: Return

ϭ (Profits/Technical progress)/Technical progress/R & D

investment) . While at first this expression may seem appealing, its problem lies in measurement. Profits and R & D investment can be measured in dollars, but there is no general measurement for technical progress across all technologies. Accordingly, the expression as a whole is not accurately measurable.

There is no really useful shortcut to evaluating R & D. To be really evaluated as a business function, R & D must be tracked in accounting, as it really contributes to business. This requires establishing an appropriate accounting system for the research function. For example, Yutaka Kuwahara and Yasutsugu Takeda described an accounting method for measuring the historical contributions of research to corporate profitability (Kuwahara and Takeda 1990). In the accounting system, profit from each product had to be proportioned according to productive factors, one of which was a profit contribution factor by the research.

Accordingly, budgeting corporate R & D is difficult because it is a risky investment of different kinds and over varying periods of time - to - return of investment.

For this reason, most R & D is usually deducted as a current operating expense and treated as a part of the administrative overhead. In practice, most R & D budgeting is done by incremental budgeting, increasing research a little when business times are good, cutting research when profits drop.

The level of R & D expenditures often tends to be a historically evolved number depending on many variables, such as the rate of change of technologies on which the corporate businesses depend, the size of the corporation, levels of effort in R & D by competitors, and so on. In areas of rapidly changing technology (high technology), firms tend to spend more on R & D as a percent of sales than do firms in mature core technologies. For example, high - tech firms have spent in the range of 6 percent to 15 percent of sales, whereas mature technology firms may spend 1 percent of sales or less.

The share of R & D divided between corporate research laboratories and divisional laboratories also differs among industry, but generally with divisional laboratories having the most share because the direct and short - term nature of their projects contributes to profitability. For example, in high - tech firms, the corporate research laboratory might reach as high as 10 percent of R & D, but seldom more.

In the corporate research lab, R & D funding is usually of three types: (1) allocation from corporate headquarters, (2) internal contracts from budgets of business units, and (3) external contracts from government agencies. The corporate allocation provides internal flexibility for the corporate lab to explore long - term opportunities.

The internal contracts provide direct service to business units. External contracts from government agencies either provide a direct business service to government or additional flexibility for the research laboratory to explore long - term future technologies. Normally, internal contracts will provide the majority of corporate laboratory (unless the corporate laboratory is in the government - contract R & D business).

Many studies have tried (and some still try) to find correlations between R & D

investments and profitability — but without much success. The reason is that not all R & D is good research, and even not all good research will be useful to a firm.

The quality of managing the R & D function is more important than the absolute 114 INNOVATION AND CORPORATE R&D

quantity of resources spent on R & D. Thus, some firms that have managed R & D

well have received great benefit, while other firms even with great expenditure have not benefited from it.

KEY IDEAS

In the case study of:

• GE ’ s Corporate Research Center

• Genecor ’ s R & D

We have examined key ideas about technological innovation and economic development as:

• Objectives of corporate research

• Institutionalization of scientific technology

• Profit - gap analysis

• Planning incremental innovation

• Two cultures

• Evaluating corporate R & D

6

INNOVATION AND MARKETS

INTRODUCTION

Earlier, we saw how radical innovations have stimulated economic growth, as Kondratiev economic long waves. Let us now look into how, by innovation, markets are changed in an economy. Markets provide a trade structure to exchange goods and services between sellers and buyers. Radical innovation can alter markets in two ways: (1) changing the products or services available or (2) changing how the market itself operates. The kinds of questions will we examine focus on high-tech product innovation in markets:

• How can price and value be properly matched in new product innovation?

• How can the proper engineering specifications for a market be determined for the design of new high-tech products/services?

• How can a high-tech product be completed to fulfill a customer application?

• How do engineers and managers think differently about the market for a product innovation?

As shown in Figure 6.1, we examine how the activity of commercializing high-tech product/services alters markets.

Impact of innovation

upon markets

Research

Invent

Commercialize

Market

Nature

Science

Technology

Economy

Utility

Exist

Discover

Manipulate

Embed

Use

naturally

nature

nature

nature

nature

The world as material

The world as money

Figure 6.1 Innovation process

115

116 INNOVATION AND MARKETS

CASE STUDY:

Amazon.com

We have seen how radical technological innovation (e.g., the Internet) has had major impacts on the economy and on business competition (e.g., Google’s impact on media advertising). Now we look at another example of this drama of the Internet in retailing—the case of Amazon.com. Amazon completely changed the book-retailing business, as one of the first of the dot.com businesses. The competitive advantages of the new Internet technology were so strong that established competitors, such as Barnes & Noble as a bookseller, could not afford to ignore it. The innovation of the Internet occurred at the macro-level of nations, but its economic impacts have occurred at the micro-level of businesses. It was in the late 1990s, when a new business venture named Amazon.com was begun by an entrepreneur, Jeffrey Bezos. One competitor then experienced the technology imperative of Amazon.com—a national U.S.

book retailer, Barnes & Noble.

It had been only a decade earlier, in the 1980s, when Leonard Riggio, a new entrepreneur, built Barnes & Noble, and in doing so changed the retail book industry in the United States. As CEO of Barnes & Noble, Riggio had grown the book retailer into a national and traditional bricks-and-mortar retailer. But when the 1990s drew to a close, Riggio was not a particularly happy CEO.

Nina Munk summarized the time: “December 16, 1998, was not a good day for Leonard Riggio . . . sitting in his cramped windowless conference room at Barnes & Noble’s headquarters in lower Manhattan, Riggio just picked at his lunch . . . and shook his head in disbelief. Amazon, an upstart with sales of $600

million and losses that grow bigger every year, was now worth seven times more than Barnes & Noble Inc., a chain of 1,000 bookstores with sales of $3 billion” (Munk 1999).

Riggio was reacting to a stock announcement that Amazon.com stock had risen from $150 a share to $400 a share. By the end of the day, 17 million shares of Amazon changed hands. When the market closed, the value of Amazon.com had increased by 20 percent to $15 billion. The value of the stock held by Amazon’s founder, Bezos, was worth $5.7 billion.

For thirty-five years, Reggio had been selling books (compared to the roughly five years Bezos had begun selling books through the Internet); and Reggio was disturbed: “I am sitting here, hammering away day after day, to come up with new ideas for my stores, and then, in an instant with just a single press release, Jeff Bezos is worth another $1 billion” (Munk 1999).

Riggio had begun selling books as a college student, while attending night school at New York University. During the day he worked as a clerk at the NYU

bookstore. Deciding he could do a better job than the university bookstore, he dropped out of college in 1965 and started the Student Book Exchange (SBX), near the NYU bookstore. In six years, he had expanded to five campus bookstores INTRODUCTION

117

in New York City. Next, he bought Barnes & Noble (an unprofitable seller of text-books on Fifth Avenue and 18th Street). Riggio was thirty years old and ready to innovate. He loaded tables in Barnes & Noble with remaindered books. He installed wood benches for people to sit on and peruse books. He gave away free copies of the New York Times Book Review. He adopted techniques from other mass merchants to book selling, using aggressive advertising: “If you paid full price, you didn’t get it at Barnes & Noble.”

In 1986, he bought a chain of 37 bookstores (plus 142 college stores) and B.

Dalton, a chain of 800 bookstores (using junk bonds for financing). Suddenly, Barnes & Noble was the biggest bookseller in the country. His next strategy was to put bookstores in shopping malls. He continued to expand, buying small chains of bookstores, one after another (e.g., Scribner’s, Bookstop, and Doubleday book stores).

In the early 1990s, he changed strategy again, abandoning his mall-based strategy to build book superstores. Barnes & Noble’s booksuperstores were conceived as places to gather and spend time, with comfortable chairs, serving Starbucks coffee, and staying open until 11 P.M. In addition, he began building a big brand name, using celebrity authors and selling designer shopping bags, bookmarks, and advertisements with illustrations of Ernest Hemingway and Virginia Woolf.

Although the idea of the superstore was not original (Borders was the first to build gigantic stores), Riggio moved faster and more nimbly.

So it was that just a year earlier—in 1998, before Amazon.com—that Lenny Riggio had been dominant in selling books. Riggio was fifty-eight and the most important player in the book retailing industry. In the United States, Barnes & Noble had the most bookstores and the biggest market share of any competitor, and also was profitable. Barnes & Noble’s stock price in July of 1998 hit $48, a 220 percent increase over the prior 18 months.

Suddenly, Riggio found he had a new competitor to battle, Jeff Bezos. Times were changing. New technology, the Internet, enabled radically new business strategies. For example, Suzanne Zak, head of a money management group called Zak Capital, had been a large Barnes & Noble shareholder. On July 24, 1988, she attended a meeting hosted by Amazon, for analysts and money managers:

“Initially, like a lot of people, we were skeptical of Amazon,” she explained.

“But at that meeting, listening to Bezos a light bulb went off. I said, ‘We’re going to have a problem here.’ ” Zak sold all 400,000 of her Barnes & Noble shares.

Others also reduced their holdings. Barnes & Noble stock tumbled from $48 to the mid-$20s (Munk 1999).

The new Internet had provided a strategic competitive advantage in retailing.

Riggio needed to join the e-commerce strategy to try to catch up. He launched barnesandnoble.com. In 1998, it brought in just 320,000 new customers. But Amazon.com added millions. In 1999, Barnes & Noble’s share of the U.S. book retail market was 15 percent, while Amazon’s was just 2 percent. Amazon had 8.4 million registered customers, selling 75 percent of all books ordered online, while barnesandnoble.com had only 1.7 million, selling 15 percent online.

118 INNOVATION AND MARKETS

Jeff Bezos

Leonard Riggio

(http://en.wikipedia.org 2009)

(www.nola.com, February 27, 2008)

This case illustrates the dramatic change that the Internet made on businesses in the middle of the 1990s. The business model in a whole retail sector had to be rethought under the impact of the Internet. Technological change creates the possibility of business change; and then a previously good business strategy may become obsolete and noncompetitive—and very quickly.

The first entrepreneur to see business opportunities in new technologies creates not only new markets but also new competition for established businesses—a technology imperative.

BUSINESS MODEL—PRICE AND VALUE

The concept of a market is studied in the business topic called marketing. The two basic activities of marketing are (1) advertising to find customers and (2) sales to earn revenue from customers. The first point of advertising is to have a customer find the business, and the second point of advertising is to provide the customer with information to stimulate a sale. Thus, the business function of marketing is fivefold:

1.

Locate

customers.

2. Inform customers about the properties and prices of the products/services.

3. Match customers to products/services.

4. Enable customers to purchase a product/service.

5. Persuade customers to purchase a product/service.

BUSINESS MODEL—PRICE AND VALUE 119

The key marketing idea to persuade customers to purchase lies in the perspective of seeing the value of a product/service from the eyes of the customer. This customer-focused perspective is often called a business model of the business:

• Who are the customers for the business?

• What value does a product/service provide for a customer?

• What price will the customer pay for the product/service?

• What profit can a business make in selling the product/service?

A business model is how a company makes money from providing value to a market.

To get the correct business model, a business must first correctly identify and reach the proper customer, and second, make correct price-to-value judgments on products/services. A correct business model assists the business in making money.

We have seen that Amazon showed that one business model for the Internet was as a means to sell goods. Google would show another business model on the Internet—advertising. E-Bay showed that brokering the selling of goods could make money. And companies such as Travelocity have demonstrated another Internet business model, selling services.

Not surprisingly, the business models to make money in the information age turned out to be not too different from those in the manufacturing age—selling goods or services and advertising.

In any business model, there are two ideas about value—value to the customer of the product (called utility in economic jargon) and value to the business (called price). The challenge of a business model is to match up these two kinds of value—

utility to price. The price is the monetary units a customer needs to pay to acquire the product or to receive a service. The value is the utility of that product/service to the customer’s lifestyle. Marketing needs to carefully discriminate between different perceptions about price and value—between those of the business and those of the customer. We can visualize this difference of perspectives by looking again at the radical technological innovation process, but this time, we show a dotted line between the perceptions of price and utility (see Figure 6.2).

The value of a product/service to customers is experienced as value in the application of the customer. Ultimately, all the technology used by a business is directly (or indirectly) seen by a customer through the business’s product/service. Yet the engineering and marketing personnel of a business may not fully see the customer’s application. This is a common reason why product/service designs fail to satisfy a customer—when a high-tech business’s engineering and marketing personnel may not have fully recognized the customer’s needs (the value of a product/service) in an application.

This diagram sketches where these economic concepts of value and price differ—that is, where the customer and business have different perceptions about a 120 INNOVATION AND MARKETS

National innovation system

Price

Nature

University

Government

Value

research

research

Industrial

Industrial

research and

value-adding

development

structures

Research in

transforming

nature to utility

Business

Market

Production Administration

Customer

marketing

finance

Product

engineering

information

research

Task

Application

system

Figure 6.2 National innovation system for transforming nature to utility product or service. This perceptual shift emphasizes where the difficulty lies in successful commercialization of a new high-tech product/service. The dark line shows the divide between the business system and application system. It indicates that the direct experience of the business system with the application system often does not occur because the business system and application system do not overlap. This creates a kind of perceptual divide between a business and its customers. A business perceives the value of a product/service as its price, whereas a customer perceives the value of a product/service as its utility—usefulness.

The perceptual divide between price and value in a new high-tech product is the basic reason why marketing forecasts often fail about the potential of a basic new high-tech product—when such forecasts that are derived only a market analysis existing markets.

Thus, a marketing problem for an innovative new product/service often arises from a lack of detailed understanding and knowledge about the matching together of its price and utility—both linked in a customer’s application. Hence, marketing of new high-tech products is always a commercial gamble. As we saw in the earlier Google illustration, it is important for a company to continue to learn about BUSINESS MODEL—PRICE AND VALUE 121

its markets—who are its real customers, how to match price to utility, and how to improve utility for its customers.

The innovation gamble is won by the high-tech firm that best calculates how to match superior product performance to customer value in a new market.

CASE STUDY:

Personal Computer Industry

Before the Internet (and before Amazon and Google and others), technically there needed to be a personal computer industry. It was the personal computer product line that opened the mass market for the computer and computer communications over the Internet (and not the earlier mainframe computer of IBM). Accordingly, the historical growth of the early personal computer industry in the 1970s and 1980s provides a nice example of how a new high-tech product does creates a mass market. But the biggest challenge of the new personal computer industry was just—what was to be the market for personal computers?

The personal computer (PC) market began as kind of a hobbyist market (a technology innovation looking for market applications). Yet it grew to become one of the great market changes in modern society. Later, the PC would become essential to the Internet and electronic commerce. But in the late 1970s, the personal computer was seen as only a very low-end product (even barely a computer).

What made the PC technically possible were previous technical advances in semiconductor chip technology at the stage of large-scale integration (LSI). In the early 1970s, LSI was a next-generation technology with tens of thousands of transistors on a chip. Its application was to design and produce Dynamic Random Access Memory (DRAM) chips of 16K-bit capacity. I also in the 1970s, LSI was used to design and produce a microprocessor chip, which then was used as a central processing unit (CPU) in designing the first personal computers. And technical progress in CPU chip was in transistor density and cycle speed. Each next-generation technology of CPU chips increased word-length and processing speed. In the microprocessor chips of LSI technology, the word length of CPU

chips was then an 8-bit word length. (This meant that in VLS chip technology, all instructions for computer processing in the CPU chip were written in binary words of 8 units length (e.g., 10010111), using 16 transistors for each bit in a transistorized flip-flop circuit arrangement.) In the next-generation technology of VLSI, CPU word lengths expanded to 16 bits, using 32 transistors for each bit.

By the late 1970s, CPU microprocessor chips with 8-bit words could be purchased, as well as DRAM memory chips of 16,000 (16K) bit storage capacity. Putting a CPU chip and a DRAM chip together into a box (with a data bus chip and other parts) made it possible to build a very small computer for personal use. Yet this new product line of the personal computer was innovated not 122 INNOVATION AND MARKETS

by existing computer companies but by individual entrepreneurs. Entrepreneurs started new firms to exploit the new technological opportunities. By the middle of the 1970s, computers were being produced in three product lines: super, mainframe, and mini computers. Soon the personal computer would add a fourth low-end product line.

With the development of the bigger computers, many people with technical backgrounds began to wish that they could have their own computer. But big computers (mainframe computers and minicomputers) were expensive and then only purchased by large organizations. One of the first hobbyist groups, the Amateur Computer Society (ACS), was started in 1966 by Stephen B. Gray.

ACS published a bimonthly newsletter containing problems, answers, and information about where to get parts, schematics. and cheap integrated circuits. Still, trying to put together a computer was a massive job, beyond the range of most individuals (Gray 1984).

The situation for amateur computer makers remained this way from the late 1960s to 1973—when Frederick Faggin at Intel, designed a microprocessor on a chip. Intel produced and sold this as the Intel 8008 chip. Nat Wadsworth, a design engineer at General DataComm in Danbury, Connecticut, attended a seminar given by Intel about its new 8008 chip. The chip used an 8-bit word length and could perform all the mathematical and logical functions of a big computer. Wadsworth suggested to his management that they use the chip to simplify their products, but they were not interested. Then Wadsworth talked to his friends: “Why don’t we design a nice little computer and each build our own to use at home?” (Gray 1984, p. 12).

Two friends joined Wadsworth, and they designed a small computer. They laid out printed circuit boards and ordered several to be constructed by a company.

Then Wadsworth decided to manufacture a small computer, and in the summer of 1973, Wadsworth quit his job, incorporating the Scelbi (Scientific, Electronic, Biological) Company. In March 1974, the first advertisement for a personal computer based on a microprocessor appeared in QST, an amateur radio magazine.

Unfortunately, Wadsworth had a heart attack in November 1973 at the age of 30. Then he had a second attack in May 1974. He sold about 200 computers, half assembled and half in kits. While in the hospital, he began writing a book, Machine Language Programming for the 8008, which sold well.

About the same time as Wadsworth, H. Edward Roberts (another entrepreneur) was also building one of the first personal computers, using the new Intel 8008

chip. Roberts was working at the Air Force Base in Albuquerque, New Mexico.

In 1971, he had formed a company with a partner, Forrest Mims, called Micro Instrumentation Telemetry Systems (MITS). It manufactured electronic calculators, and Roberts bought out his partners. But by 1974, big firms had moved into the electronic calculator business with better products and cheaper prices. MITS

was $200,000 in debt, and Roberts decided to make computers with the new Intel 8008 chip (Mims 1984). At the same time, Arthur Salsberg, an editor at the magazine Popular Electronics, was looking for a computer project using the new Intel chip and learned of Roberts’s project. Salsberg called Roberts and told him if he BUSINESS MODEL—PRICE AND VALUE 123

could deliver an article in time for the January 1975 issue, it would be published.

Roberts called his computer the Altair, and it was featured on the front cover of Popular Electronics: “Project Breakthrough! World’s First Minicomputer Kit to Rival Commercial Models.” Salsberg titled his editorial in that January 1975 issue:

“The Home Computer Is Here!” (Mims 1984, p. 27). The publicity worked, and orders poured into the company. But the product was awkward and difficult to use. In 1975, Altair cost $429 in kit form and came without memory or interfaces.

Also, it had no keyboard and had to be programmed directly in binary by setting switches. But it was a computer. We note that the articles in QST and Popular Mechanics created the first markets for Wadsworth and Roberts.

The first purpose of marketing is to find customers and inform them about the product.

The first programming language to be adapted to the Altair was Basic. Earlier Basic had been written by a Dartmouth professor, as a programming language easier to learn than Fortran. In 1975, Bill Gates and Paul Allen wrote a “Basic” language interpreter for the Altair:

Ed Roberts still recalls the day in late 1975 when William H. Gates, the co-founder of Microsoft, walked into his office, anxious and depressed.

A rail-thin young man with a voice that cracked when he was excited.

Mr. Gates had just turned 20. People were stealing his software, he com-plained, and the prospects for his fledgling company appeared dim. “I’m going to quit,” Mr. Gates said, according to Mr. Roberts. “There’s no way I’ll make money on software.” To help tide a needed supplier through difficult times, Roberts took Mr. Gates on as a $10-an-hour contract worker. Mr. Gates has a different recollection. . . . The reason he did hourly contract work for about four months, he said, was to write some special test software to catch defects in computers made by MITS” (Lohr 2001, p 1).

Roberts used a computer bus (the S-100 bus) that was to become a first standard for the new personal computer industry: “For a couple of years, MITS was the dominant company in an industry that was just taking shape. Its starring role was short-lived, as Mr. Roberts sold MITS in 1977 amid a legal dispute with Microsoft that left him embittered for years. But during its brief run as the leader, MITS

pioneered innovations and set the pattern that the personal computer industry still follows. The MITS-Microsoft partnership established software as a separate business in personal computing” (Lohr 2001, p. 12).

After Roberts sold MITS, he went to medical school and became a country doctor in Cochran, Georgia, with modern personal computers in each examination room for taking notes and analyzing tests (Lohr 2001). Roberts died in April 2010.

124 INNOVATION AND MARKETS

DESIGNING A PRODUCT FOR A MARKET

For a business to design a commercially successful product/service to sell to customers, the business needs to know the needs of the customer—the so-called “face-of-the-customer.” This means knowing the kinds of applications and tools and tasks—in which the customer will use the product/service.

The technical risks in designing a product/service arise from knowing exactly and precisely the engineering specifications to satisfy the customer needs for an application—utility.

The technical risks arise from uncertainties about what the product or service can really do for a customer in applications, how well it does it, how much resources it will consume, how dependable is it, how easy is it to maintain and repair, and how safe is it. The risks can be summarized under these categories:

• Functionality

• Performance

• Efficiency

• Dependability

• Maintainability and repairability

• Safety and environment

Functionality of a product, process, or service means: “For what kind of purposes can it be used?” For example, different industries are often classified by purpose: food, transportation, clothing, energy, health, education, recreation, etc. The goods and services within these industries satisfy these different purposes. Furthermore, within a purpose are usually different applications. For example, in transportation, there are applications of travel for business, vacation, and personal travel.

The performance of a good or service for a function means: “What is the degree of fulfillment of a purpose.” For example, different food groups provide different kinds and levels of nutritional requirements.

The efficiency of a good or service for a level of performance of a function means: “What are the amounts of resources consumed to provide a unit level of performance?” For example, different automobiles attain different fuel efficiencies at the same speed.

Dependability, maintainability, and repairability indicate how frequently a product/service will perform when required and can be easily serviced for maintenance and repair.

Safety has both immediate and long-term requirements: safety in the performance and safety from after-effects over time. Environmental impact of a product/service includes the impacts on the environment from production, use, and disposal.

DESIGNING A PRODUCT FOR A MARKET 125

In contrast to technical risks in trying to design products for the customer, there also are commercial risks, from uncertainties about just who are the customers, how they use the product, what specifications are necessary for use, how they gain information and access to the product, and what price they are willing to pay:

• Customer type

• Application

• Specifications

• Distribution and advertising

• Price

The customer, application, and specifications together define the market niche of a product/process/service. The distribution and advertising together define the marketing of the product/process/service. The price set for a new product/process/

service needs to: (1) be acceptable to the market, (2) provide a large enough gross margin to provide an adequate return on the investment required to innovate the new product/process service.

Solving the technical variables correctly is both necessary and costly. (This constitutes the research and development costs of developing and designing a product.) Even if successfully accomplished, the commercial variables must be correctly solved. (This constitutes the production and marketing costs.) No matter how much a business may learn about a customer or a set of customers in market analysis, these technical and commercial factors will still vary across a market and even a market niche. There is always a range of technical variables possible in the design of a new product/process/service. Which variables will turn out to map correctly to the future required set of commercial variables is never clear initially but only in retrospect.

This is why for successful technological innovation, both engineering personnel and marketing and financial personnel need to communicate and cooperate—to match technical specs with market needs and price.

A business should know the tasks and details of a customer’s application system in order to understand the value of a product/service—as opposed to its price.

CASE STUDY (CONTINUED):

Personal Computer Industry

After the first attempt at a personal computer by Ed Robert’s, more technical advances continued in components of the personal computer. Frederick Faggin, the designer of the 8008 chip, left Intel to start a new company, Zilog, producing the Z80 chip. Chuck Peddle, at MOS Technology, created a new 8-bit microprocessor chip with an extended instruction set, the 6502. These two chips, the 126 INNOVATION AND MARKETS

Zilog Z80 and the MOS 6502, were to become the basis of the first expansion of personal computers.

The knowledge infrastructure of the mainframe computer industry had created a latent market for personal computers, and the first customers were all computer hobbyists. Amateur groups had formed and hobbyist magazines were looking for the right product. Next marketing infrastructure to sell personal computers had to be created. In 1975, Dick Heiser started the first retail computer store in Los Angeles, California. Upstate, Paul Terrell opened a retail store in Mountain View, the Byte Shop (Ahl 1984).

The computer hobbyists needed a whole application system for personal computers. The first marketing challenge of the new industry was not only to provide the major device but also peripheral devices and supplies to enable the personal computer to perform computational applications. The market for the first personal computers therefore evolved as a technical market, with high-tech products needing to be completed as an application system of personal computation. The first high-tech products were created by entrepreneurial engineers, who left employment with large companies and government labs to start their own companies. Publicity about new products in hobbyist journals was essential to the commercial success of a new consumer technical product. Another important component for the personal computer was the programming language, and (as we earlier described) Bill Gates and Paul Allen had written a Basic language interpreter for the Altair (Gates 1984).

The early personal computers used Basic as a programming language.

The personal computer that finally ignited the mass market was the Apple computer. In January 1976, at a West Coast electronics trade show (Wescon), Chuck Peddle decided to sell the new MOS 6502 chips at $20 each. (The first Intel 8008 chip had cost Roberts $100 a chip.) One of the first customers for Peddle’s 6502 chip was Steve Wozniak, who was then a technician at Hewlett-Packard.

Wozniak took the chip home and wrote a Basic language interpreter for it. Then he made a computer, calling it Apple I. He showed it in the spring of 1976 to the Homebrew Computer Club in Palo Alto, California. Again, it was only partly complete, without keyboard, case, or power supply. Yet two friends, Steve Jobs and Paul Terrell, were impressed. Jobs formed a company to sell the computer.

Terrell ordered 50 units for his Byte Shop as assembled machines. He did not want to sell kits (Ahl 1984).

With orders in hand and needing cash for production, Jobs sold his Volkswagen and Wozniak sold his two HP calculators. Wozniak stayed on his job at HP, while Jobs hired his sister and a student to assemble the units. In 29 days, they delivered their first 50 units. Then the garage-based company sold another 150 computer for $666 each. By the end of the summer, Wozniak was designing the successor product, which was to have a keyboard, power supply, and plug-in slots for the S-100 bus. The entrepreneurs understood that they had to improve their first design for better marketability.

Jobs and Wozniak almost had it together as a new venture: with engineer, manager, first products sold, and an improved model. What yet was missing was business experience and capital. The next member of the Apple team to be added was A. C. “Mike” Markkula. Markkula was trained as an engineer and had worked for COMPLETING HIGH-TECH PRODUCTS FOR APPLICATIONS 127

Intel and Fairchild during their meteoric growth in the days of the first integrated circuits. Intel stock options had made Markkula a millionaire, and he retired at age thirty-four. Markkula visited the garage of Jobs and Wozniak and was impressed.

He invested $91,000 of his own money and began an active role in planning and management. He hired Mike Scott as president. The four of them, Wozniak, Jobs, Markkula, and Scott, set out to make Apple a Fortune 500 company.

Apple was one of four major competitors for the major device product in the initial stage of industrial growth of the personal computer. The other early competitors were Radio Shack, Commodore, Texas Instruments, and Atari. (Chapter 7 will discuss Commodore 64 and how it drove Texas Instruments and Atari out of personal computers. Later, the IBM PC would drive Commodore out of the industry.) The next steps in innovation in the personal computer industry were in peripheral devices and in applications. The tape cassette had early been used to store programs, but it was slow. In the middle of 1978, two companies, Apple and Radio Shack, introduced floppy disk drives for their computers. This facilitated software applications.

In 1975, Michael Shrayer had purchased an MITS Altair, and he had written a text editor routine for it. A text editor is a software tool to make it easier to write programs and to alter programs. On completing the routine, he wrote a second piece of software to help him document the software, which he called the Electric Pencil. This was the first word-processing software for the personal computer, which he began selling in 1976. Word processing became one of the major applications of personal computers (Shrayer 1984).

Later, a second major application was the spreadsheet. In 1978, Dan Bricklin was a student in the Harvard Business School. He and his friend Bob Frankston had been working on a spreadsheet program for the Apple. Spreadsheets were just what business students needed. Bricklin had an assignment in his consumer marketing class to analyze the “Pepsi Challenge” marketing campaign. Bricklin used his new spreadsheet routine, projecting the financial results of the Pepsi campaign five years out (instead of the two years projected by all his other classmates). His professor liked it. Bricklin and Frankston knew that they had a useful business tool, VisiCalc. They introduced the new product at the National Computer Conference in New York in 1979 (Bricklin and Frankston 1984). With the spreadsheet application, Apple sales really took off. The personal computer had become a useful personal business tool for the business manager.

By 1980, the major device, parts, relationships, and infrastructure of the personal computer system were developed, and the industry began extraordinary growth.

COMPLETING HIGH-TECH PRODUCTS FOR APPLICATIONS

We saw that the value of a product/service to a customer is in its utility in a customer’s application. And for a radically new high-tech product service, the application system may not be complete for the product to be useful, of value. For example, in the first personal computer of Roberts there was no software, no keyboard, no screen, 128 INNOVATION AND MARKETS

and so on. Even the first Apple lacked business-useful software until VisiCalc was invented. But radically new high-tech products often begin as incomplete for many real applications. This was the challenge of the first personal computers—getting the product complete enough for a customer’s application. Until a customer can apply the product to an application, a customer can gain no value from the product, no matter how technically clever the product.

For a market to emerge, a new high-tech product must be completed for an application.

A technology, a product, and an application are different systems. A technology is a system of knowledge about how to manipulate nature in a logical scheme to achieve a functional transformation. A product is a system of a device or process that embodies a technology to perform the functional transformation. An application is a system of tasks requiring a transformative device. Because they are different systems, it usually takes more technological systems than the core technology to complete a product system, and it usually takes more products than the major device project to complete an application system.

In the design of the major device of the personal computer as a product system, it took more technology systems than the core technology of the CPU chip. It also required the technology systems in the data bus, internal memory of the memory chip, software technology of the operating system, etc. To use the personal computer in an application for computation, the customer required additional peripheral devices (such as keyboard for data input, a monitor for data output, a recorder, or floppy disk for data storage, a programming language such as Basic with which to program the computer, instruction for understanding how to use the computer).

The first marketing problem for a new high-tech product is to determine what, how, where, and when it can be completed by additional products for a customer’s application. The first market of the personal computer industry was a market of computer hobbyists who could build applications of the personal computer. Bill Gates was one of the first customers for Roberts’ Altair computer in order to adapt the programming language of Basic to run on the computer. Gates’s purpose was to become a personal computer entrepreneur, as was the purpose of many of the first customers of the new high-tech personal computer product.

The value-added to a customer of a high-tech product is a device in the customers’ application system.

Although illustrated by the history of the personal computer’s origin, these lessons are general—technical entrepreneurs selling first products for a hobbyist or scientific or industrial or business market. Technical education and communications and publicity play an essential role in the commercial success of the early marketing COMPLETING HIGH-TECH PRODUCTS FOR APPLICATIONS 129

of high-tech products. Thus, the major device and peripherals and supplies and a technical infrastructure around a new application system are all necessary to come together to begin a high-tech industry.

Marketing of new high-tech products is not only an advertising challenge but also but an application-system-completion challenge.

CASE STUDY (CONTINUED):

Personal Computer Industry

In computers, marketing leadership has gone to the company that had hardware performance almost as good as anyone else’s but provided superior software for applications. With this principle, IBM dominated the mainframe computer market from the 1960s through the 1980s. IBM was watching the new personal computer market and entered it in 1981 with an IBM PC. At the time, the IBM PC had the superior technology of a new Intel chip with 16-bit word length, but it was not IBM’s chip. IBM claimed 7 percent of the market that year. In 1982, it matched Apple’s share at 27 percent. In 1983, IBM emerged as the clear and dominant leader with 36 percent, and Apple slipped to 24 percent. IBM’s brand name justified the new product to the business market as not just a “toy” but a true “computer”. IBM

alone had established the “industrial standard” for personal computers.

IBM had played a “close-follower” technology strategy in 1981 in entering the personal computer market. It used just a right balance of technology leadership and marketing leadership—a little technology leadership and a lot of marketing leadership. The IBM PC had a 16-bit central processing and chip, superior to the 8-bit chip of the Apple and competitors in the personal computer market in 1981

(superior in the sense that it allowed a larger memory address). Yet since the IBM

PC used an 8-bit bus, the combination made it easy for software applications to be written for the IBM PC (using the larger memory that the larger addressing capacity of the 16-bit processor made possible). Therefore, the principle of IBM’s entry into the personal computer market in 1981 was just a little technology leadership, but not too much.

One marketing aspect of IBM’s successful entry into the personal computer market in 1981 was that IBM focused on the business market, pricing it for business and marketing it through its own sales personnel, opening new retail outlets, and distributing it through the new personal computer dealer networks. The important marketing edge was the IBM name, with the installed base of IBM business customers and IBM’s reputation for service. This combination of marketing and technology leadership at first worked well for IBM. There was an IBM computer for small businesses and for the desks of sharp managers. Another factor of IBM’s early success in personal computers was the choice of an open architecture and operating system (imitating Apple), which facilitated the rapid transfer and development of software applications for the new IBM personal computer.

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MARKET POSITIONING

A market’s evaluation of the potential of new technology in a product can be examined through five criteria:

1. Quality—through product superiority

2. Value—through correct market focus

3. Price—through efficient production

4. Opportunity—through timing of innovation

5. Profitability—through investment return

The quality of a product to be delivered depends on its embedded technologies. Will new technology improve the quality of existing products or provide new quality products? The value to a customer depends on the application to which the customer applies the product. Will new technology improve the value or provide new value for customer applications? The price of the product depends not only on the technologies in the product but also on the technologies in the production of the product. Will new technology reduce the cost of the product?

New technology will provide a competitive advantage to the innovator only when its innovation occurs before competitors’ use of the new technology, and thus, timing is important when evaluating new technology. Is there a window of opportunity to gain competitive advantage or, conversely, to catch up with and defend against a competitor’s innovation? Finally, new technology must also be evaluated on the capital required for innovation. All innovations cost money to develop and commercialize. What is the capital required? Can that be recovered in a timely manner? What will be contributions to profitability from the technological innovation?

As new technology develops, new applications are often discovered, as well as new relationships to existing technologies and applications. Successful product innovation of the new-generation technology requires that corporate management recognize its significance and correctly focus the marketing of the new product lines. This is the hardest thing to do, since the new technologies will probably affect new markets in unforseen ways. Therefore, it is important that technological development in a corporation be accomplished in a manner that strongly encourages a shared vision of the directions and rates of technological advance between the research laboratories and the production divisions. The research personnel will be most creative about technological opportunity, while the production and sales divisions will be most sensitive to market needs. Technology leadership must thus be aimed at the right market at the right time and at the right price, or else the technology leader may end up showing the way to competitors without capturing a dominating lead. Since this may not happen in the first pass, it is important for the innovating corporation to be flexible and quick to adapt the new product to evolving market situations.

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Marketing of new high-tech products is difficult, as both the radically new technology of the product and its applications must be developed together—

simultaneously and interactively.

To emphasize this challenge in marketing high-tech products, Shanklin called the making of new markets by a new technology a kind of “supply-side marketing—any instance when a product can create a market.” Traditional market analysis for new products approaches the task by market segmentation, identifying the customer group (industry or consumer) on which to focus the new product or service. This can be done because earlier or similar products have defined the function and applications of the product. Incrementally innovative new products can target a market that can be segmented. In radically new products, however, the largest market has often turned out to be different from the initially envisioned market. For example, Kodak’s plastic photographic film was intended for a professional photographer’s market, which at that time used glass plates, but it quickly found a larger market in the amateur photography market, which “the film created” (Shanklin 1983).

Technology strategy should emphasize product flexibility, improvement in performance, and lowering of cost in order to seek new applications and create new markets.

The more radically new the innovative product, the more flexible should be the view of the potential market. Critical points of market acceptance are when the application, performance, safety, ease of use, and price match a new market group of customers.

Ryans and Shanklin called this kind of marketing for innovative products positioning the product: “Stated simply, market segmentation is a too narrowly defined term to describe the target marketing activities that need to be employed by the high-tech company. Rather, positioning seems to best describe the steps that the high-tech marketer needs to follow if it is to identify correctly the firm’s target markets and to place them in priorities” (Ryans and Shanklin 1984, p. 29).

By positioning, Ryan and Shanklin meant that the marketer first “identify a broad range of potential users, listing applications, performance, ease of use, and price required for each group.” Next, the marketer should prioritize these groups in terms of preferred market for the product. In the product design, flexibility that covers a wider range of groups, or is easily adapted from group to group, will increase the likelihood of commercial success. E. Peter Ward also reviewed the steps in market positioning: 1. Focus on the range of applications possible with the new technology.

2. Project the size and structure of corresponding markets for the applications.

3. Judge the optimal balance of performance, features, and costs to position in markets.

4. Consider alternate ways to satisfy these markets.

5. Analyze the nature of the competition.

6. Consider the modes of distribution and marketing approaches (Ward 1981).

132 INNOVATION AND MARKETS

Roland W. Schmitt also emphasized the importance for marketing personnel to work closely with research personnel, in order to facilitate the linking of technological opportunity with market need: “These [marketing] experts should not, however, give blind allegiance to the latest analytical techniques or the dogma of marketing supremacy. Rather, they should have the temperament of a research experimentalist, putting forth hypotheses about the market and devising economical and efficient market experiments” (Schmitt 1985, p. 126).

Positioning in a new market through experimentation is the key to improving the understanding of the eventual market implications of new technologies.

The more radical the innovation, the more experimental must be the marketing approach.

CASE STUDY (CONTINUED):

Personal Computer Industry

In the end, the personal computer story did not turn out happily for IBM. When IBM introduced its first personal computer, the product manager responsible for the introduction of the IBM PC failed to look at the long-term marketing consequence of his product design. Instead of drawing on IBM’s technical capabilities and developing a proprietary operating system, he chose to license an operating system from Bill Gates’s fledgling company, Microsoft. Also, instead of having IBM develop a proprietary microprocessor chip for the computer, again he chose to purchase CPU

chips from the semiconductor-manufacturing firm of Intel. Both choices turned out to be fatal for the long-term position of IBM in personal computers.

IBM had mistakenly introduced a high-tech product without significant proprietary technology.

This was IBM’s great (but inadvertent) favor to Intel and Microsoft. During the 1980s and 1990s, Intel and Microsoft prospered, while IBM’s market share in personal computers was eaten by lower-cost clone PC manufacturers (such as Compac and Dell). IBM dropped from a high of 37 percent of the personal computer market in the middle 1980s to a low of 8 percent in the 1990s. In 2000, IBM sold its laptop business to Lesnov, a Chinese company.

PROPRIETARY TECHNOLOGY

Technology is implemented into products, production, service, and operations. Some of the technology implemented can be generic (publicly known) and some proprietary (known only by the company). Both generic and proprietary technology are PROPRIETARY

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important to competitiveness, but in a different way. Generic technology is necessary but not sufficient to competitiveness, while proprietary technology is not necessary but sufficient. Any competitor’s technology must be up to the same standards as the best competitor’s generic technology, for this is a publicly available standard of engineering. Products, production, and service that do not embody the most advanced generic technology are not globally competitive. In contrast, proprietary technology consists of the additional details in making generic technology work completely in a product, production, service, or operation. Proprietary technology consists of the details that make the competitive differences between competitors who are all sharing the best generic technology available.

A products without proprietary technology simply invite imitators into its market.

Michael Porter emphasized that competitors within a market can gain competitive advantages either through differentiation of their products or through cost advantages (Porter 1981). Product differentiation can be either on quality or on market focus.

Cost advantages may be through lower prices or higher profit margins (allowing greater expenditures for marketing, research, etc.). Thus, the relationships within a market between competitors can be changed by technology that either differentiates products or lowers prices.

Technology may also affect the barriers for entry of new competitors into a market. For example, the microprocessor on a chip dramatically simplified the technical problems of making a computer and lowered the costs of producing a computer.

This allowed a rash of new competitors to enter the computer market, challenging established computer makers. Thus, Apple went from a small business to a major competitor by creating a market for low-end and low-cost personal computers.

New technology can also affect a market through product substitution into an industry’s market with products from another industry. The lowering of computational circuit prices by microprocessor chips created market opportunity for product substitution. Microprocessors were used in minicomputers to first take the lower part of the market for computers and then later in personal computers for an even lower-performance end of the personal computer market. Personal computers took over the market of “dumb” terminals on time-sharing mainframe computers. New technology can create surprising market changes.

A brand name is always important in marketing because it uses name recognition to indicate reputation of the company. In commodity products, only brand name and price features distinguish the products of competitors. But in high-tech products, an additional distinguishing factor is performance. To establish a new brand by a new company, technical performance is essential. Proprietary technology and quality is important to protect the brand over time against competition.

A brand name cannot maintain a premium price when customers learn that the brand does not provide higher quality than competing products.

134 INNOVATION AND MARKETS

CASE STUDY (CONTINUED):

Personal Computer Industry

After IBM’s successful establishment of the personal computer market in 1981, the competition in personal computers continued. If Apple was to survive IBM’s successful attack on the new personal computer market, Apple would need a competitive advantage, advanced and proprietary technology. In 1984, Apple introduced a second generation of technology in personal computers. We recall from Chapter 1 that Steve Jobs visited Xerox PARC and there saw a technical vision of the new personal computer. This vision was first implemented in Apple in a “Lisa” project. In 1983, Apple introduced the Lisa to compete with IBM but met with disappointment. While recognized as a technology leader, Lisa had been priced too high at $10,000, almost twice the cost of the IBM

PC XT (Morrison 1984). Then Jobs at Apple responded in 1984 by introducing the Macintosh, a smaller version of the Lisa and priced in the IBM PC range $2,000 to $3,000 ). This model sold well and kept Apple as a major competitor to IBM in the personal computer market in the middle 1980s. Apple had made the strategic choice of playing technology leader against IBM’s market leadership. Without IBM’s prestige mainframe business and extensive business installations, Apple could not then match IBM’s marketing strength. However, the Apple Mac continued to lead technology over the IBM PC clones for over a decade, until these finally caught up with Apple in technological performance with Microsoft’s Windows 95. By the year 2000, Microsoft and Intel dominated the parts market for the core technologies of the personal computer (microprocessor and operating system software), but no single firm dominated the personal computer major device market.

MARKET NICHES

No market is homogeneous. Customers in the market for a product, good, or service have different needs and applications for the product and different price sensitivities and different aesthetics. Thus, analysis of an existing market into its segments of more homogenous classes of customers is important to know how to design the right kind of product at the right price for a class of customers, or market niche.

For any kind of market, the most general segmentation occurs along the dimensions of price and performance. Some customers prefer a product/service that has high performance or advanced features, and they are willing to pay high prices in a market and create a high-performance market niche. Some customers are so price sensitive that they are willing to purchase a product of lower performance but at a much lower price and create the low-performance market niche. Some customers can differentiate quality products with advanced performance when offered at a low price and create a quality market niche. Finally, some customers will accept KEY

IDEAS

135

low-performance and pay high prices for fashionable products and create a fashion market niche.

A quality market niche is the hardest niche to serve and differentiate from a low-performance market niche because a producer for this kind of market must excel competitors in manufacturing skill and efficiency to produce at low cost. In 1980, Japanese auto makers had gained a decisive lead in quality over U.S. automakers and used it over the next two decades to grow a substantial market share in the U.S. market. Such a market niche is only available to a competitor who gains and maintains a distinct and clear advantage of quality over competitors.

Some kinds of changes do occur in stable markets, and these are what we will call circular change, because this kind of change recurs periodically. The sources of circular changes in a stable market are as follows:

• Providing further market niches

• Fashion

• Demographics

Market structure is composed of markets niches in which different features and/or performance of a product/service are valued by different segments of customers.

The corporate capability of serving different market niches depends on the cost of production and the responsiveness of the market niches to price differentials.

Refinement of market niches is possible as production flexibility improves and costs of production drop. Anticipating changes of market niche structure is very important to market strategy.

Fashion differentiates commoditylike products/services without differences in performance or features. Fashion changes are cyclic over the long-term, as they depend on aesthetics within market groups. Change in fashion tends to be lead by some groups and by design-leaders within these groups. Anticipating fashion change requires identification at a time of the fashion leader groups and design leaders.

Demographics can alter markets as the size of the population grows (or shrinks) and as the relative percentage of the age-groups in a population change. Over the twentieth century, lifespan in industrialized countries continued to lengthen, with a resultant increasingly larger proportion of the population growing into the old-age category. This has dramatically increased consumer spending on medical care, retirement communities, and nursing homes. In addition, prosperity in the second half of the twentieth century and into the twenty-first century in both industrialized and industrializing countries led to increasing consumer spending by youth, leading to expansion of youth-oriented markets.

KEY IDEAS

In the case study of:

• Amazon

• Personal computer industry

136 INNOVATION AND MARKETS

We have examined the key ideas of:

• Business models

• Perceptions of price and of value

• Engineering specifications for a high-tech product/service

• Completing high-tech products for applications

• Market positioning

• Proprietary technology

• Market niches

7

INNOVATION AND PRODUCT

DEVELOPMENT

INTRODUCTION

Innovation strategy within a corporation can result in either radical innovation (such as xerography) or incremental innovation (the many improvements to xerographic technology). However, radical innovations are rare; most innovations are incremental. Incremental innovations occur in the successive improvements of technology in products/services/processes.

How is incremental innovation properly managed? It is managed in the redesign of new models of products/services and improving a technology for these new models.

This is usually called a product development process . The questions we will address include the following:

• How does incremental innovation occur in a business ’ s product development cycle?

•

•

What are the sources of delays in the product development process?

What are the criteria for the proper management of the product development process?

• What are the kinds of quality does innovation need to target in improving high - tech products/services?

As indicated in Figure 7.1 , incremental innovations involve subsequent small -

scale but important improvements in a technology, which are designed into new models of product/services, thus improving competitiveness of a business in a market.

137

138 INNOVATION AND PRODUCT DEVELOPMENT

Product development

Research

Invent

Commercialize

Market

Nature

Science

Technology

Economy

Utility

Exist

Discover

Manipulate

Embed

Use

naturally

nature

nature

nature

nature

The world as material

The world as money

Figure 7.1 Innovation process

CASE STUDY:

Commodore 64 Personal Computer

In the history of the early personal computer industry, after the radical innovation of a personal computers, there followed many incremental innovations in PC

products (by different companies). One interesting case of incremental innovation occurred in 1982 with the development of Commodore ’ s 64 personal computer.

Tekla Perry and Paul Wallich summarized the design of this computer, and we can use this case to illustrate the process of incremental innovation in product development (Perry and Wallich 1985).

In 1981 – 1982, Jack Tramiel, who owned Commodore, ordered the design and production of a new personal computer model called the Commodore 64. The

“ 64 ” was the first personal computer to use the then - new 64K memory chip.

Although now long obsolete, it was then a hot and competitively priced product for the emerging home computer industry.

In 1981 when the personal computer companies were emerging as a new sector in the computer industry, it had distinct and new markets, a business market for word processing, spreadsheets, and accounting and a home market for games.

Both markets were served by the same computers. Apple was the leader, closely followed by Tandy, Texas Instruments, Atari, and Commodore. The prices of the computers were in the range of $ 1,500 to $ 2,500. The home market was price sensitive to the $ 1,000 barrier. Children loved the new video games, and a many parents willing to spend up to $ 100 on them, but few were willing to spend more.

To get performance up and price down in this game market, the product required increased speed for graphics and sound. The Atari 800 had pioneered the technical solution to speeding up graphics performance by designing a special video - display chip to assist the microprocessor.

In the early 1980s, the video display chip was the key to improving the visual performance of PC games. At Commodore, a group of semiconductor engineers MARKET - PULL OR TECHNOLOGY - PUSH INNOVATION

139

then decided to design two special chips, one for graphics and one for sound.

This group of engineers had moved to Commodore from another company, MOS Technology, which Tramiel had recently bought. Albert J. Charpentier led the group, and he had been head of MOS ’ s Large Scale Integration (LSI) Group. Charpentier thought that new chips could enable “ the world ’ s best video game ” (Perry and Wallich 1985, p. 48). Charpentier proposed this project to his boss, Charles Winterble, who was then director of worldwide engineering for Commodore. Winterble approved the project.

MARKET - PULL OR TECHNOLOGY - PUSH INNOVATION

We pause in this case study to consider how incremental innovations can be conceived from two different sources of inspiration — market - pull or technology - push.

Market - pull innovation occurs after a market exists. Then customers ’ experiences with products/services in that market can provide a good source of information on the performance of the products/services and what improvements in their technology would be desirable. This has been called a market - pull technological innovation.

But when a radical innovation creates a new product/service functionality that no customer has previously experienced, then customer information is not useful. For basic new technologies, the motivation must be for new products to create new markets. This kind of motivation has been called technology - push innovation.

Technology - push innovation occurs predominantly in cases of radical technological innovation. Market - pull innovation occurs predominantly in cases of incremental technological innovation. Both sources of innovation have been important in the history of technology.

Yet in the early studies of technological innovation, there were arguments about which was best. For example, in the 1970s, Eric von Hippel studied samples of innovations in the scientific instrumentation industry: gas chromatography, nuclear magnetic resonance spectrometry, ultraviolet absorption spectrophotometry, and transmission electron microscopy (Von Hippel 1976). He found that the user

-

scientists of the instruments made 80 percent of the innovations in his sample and the manufacturers of the instruments made 20 percent. From this and other studies (Von Hippel 1982), he argued that market - pull was a more important source of innovation than technology push. But historically, research instrumentation has been invented by scientists. This means that Von Hippel ’ s conclusion about the importance of market - pull as a source of innovation actually depended upon his selection of his subjects - of - study, instrument inventors. These were users of the innovation who were technically sophisticated and had research capability.

A contrasting study was performed by K. D. Knight, who looked at innovations in computers and found that manufacturers (rather than users) dominated innovation from 1944 to 1962 (Knight 1963). In computers during this period, industry was more technologically advanced than most individual computer users; in this case, technology - push was a more important source of innovation than market -

pull. Other studies, such as those by A. Berger (Berger 1975) and J. Boyden 140 INNOVATION AND PRODUCT DEVELOPMENT

(Boyden 1976), considered plastics innovation among manufacturers who were technologically sophisticated and had research capability, further documenting the importance of technology - push.

Both manufacturers and users can be sources of innovation, but only when either one is technologically sophisticated and a research performer. It is the locus of sophisticated technical performance in an industrial value chain that determines whether market - pull or technology - push for any sector will be most important for innovation.

In the history of major new innovations, technology - push strategy has always preceded any market - pull strategy. However, once a basic innovation has occurred, then good market - pull strategies can be formulated. But whatever the source, for technological innovation to be commercially successful, it must be matched to markets. Christopher Freeman nicely summarized this: “ Perhaps the highest level generalization that it is safe to make about technological innovation is that it must involve synthesis of some kind of (market) need with some kind of technical possibility ” (Freeman 1974, p. 193).

Technological innovations have been motivated either (1) by conceiving a technological opportunity or (2) by perceiving a market need — called technology -

push strategy or market - pull innovation strategy.

Market - pull innovation strategy most often creates the incremental innovations. Technology - push innovation strategy most often creates the radical innovations.

CASE STUDY (CONTINUED):

Commodore 64 Personal Computer

The Commodore - MOS graphics and sound chip would be an incremental technological innovation for Commodore ’ s MOS group. The first step was to find out what the current high - quality chip products could do. This kind of practice in design is called competitive benchmarking . They spent the first two weeks finding and looking at comparable graphics and sound chips — industrywide. They also looked at the graphics capabilities of the then - most advanced game and personal computers: “ We looked heavily into the Mattel Intellivision, ” recalls Winterble.

“ We also examined the Texas Instruments 99/4a and the Atari 800. We tried to get a feel for what these companies could do in the future by extrapolating from their current technology. That made it clear what the graphics capabilities of our machine had to be ” (Perry and Wallich 1985, p. 49).

Competitive benchmarking for new product design requires one not only to find and look at the best and most advanced competing products but also to extrapolate future technical capability of competitors. New products should be designed not only to beat the current products of competitors but also to beat future products.

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Designing of products to beat competitors ’ future products is the fundamental reason why technology forecasting (foresight) is vital to new product development.

Next, the design team at MOS borrowed all the ideas from competitors ’ products that they liked. (A competitor ’ s ideas can be borrowed as long as they are not patented, copyrighted, or obtained wrongly as trade secrets.) In designing new products, technology should be approached both as cumulative and anticipatory .

This means that in the design and production of new products, the technical goal should be to include at least all the best current practices and highest current performance and then add to them. However, it is not enough to just try to include all the best features in a product, because that might make the product too expensive for a target market.

Innovative product design should balance “ best practice ” with a competitive advantage in the proper ratio of performance to cost; this is the fundamental reason why technology strategy is vital to new product development.

Commodore ’ s design team looked next at constraints that would limit how much of the best practices and how much of the highest - performance - and - improvements they could squeeze into the chip design. For the design of the new chips, the most important constraint has been and still is the minimum line width for etching structures into the silicon to make the transistors. At that time the line width was technically limited to no smaller than 5 micrometers (five millionths of a meter) in size. This constraint limited the numbers of transistors — hence, the complexity of circuits and speed of performance. The fundamental challenge in new product design is always the balancing these tradeoffs between desirable performance and technical and cost constraints.

The fundamental reason that technology implementation is important to new product development is to provide improved product performance at a lower price.

The design team formulated two kinds of design criteria: (1) the wish list of features and performance they would like to see in the chips, and (2) the limit to the numbers of circuits and features they could put on one chip with 5 micrometer technology: “ We prioritized the wish list from what must be in there to what ought to be in there to what we ’ d like to have . . . ” (Perry and Wallich 1985, p. 49).

This kind of innovation logic in product design is basic. First, there should be a decision as to what the customer needs that is better than what competitors could supply now or in the future. Second, there should be a decision to prioritize desirable features into “ must, ” “ ought, ” and “ like to have. ” If a design cannot 142 INNOVATION AND PRODUCT DEVELOPMENT

accomplish both the “ must ” and “ ought ” features, it will likely be a technical failure. If the design cannot also accomplish many of the like - to - have features, it probably won ’ t be a commercial success. Accordingly, we can see that (1) a new technology must be correctly anticipated and (2) the proper tradeoff between performance and cost must be made in the initial phases of product design.

Commercial success by a firm in the early part of a new industry critically depends on appropriate design decisions made between corporate research and product development.

With the prioritized wish list of required and desirable features, Charpentier ’ s group laid out the circuits and transistors for the computer ’ s chips. Several more product design decisions had to be made (such as sophistication versus simplicity, time to completion versus complexity, etc): “ Because design time rather than silicon was at a premium, the chips were laid out simply rather than compactly.

‘ We did it in a very modular fashion . . . ” (Perry and Wallich 1985, p. 49).

The actual design process consists of many details and decisions and tradeoffs.

Scientific and generic engineering principles can help guide some of these. Other decisions are based on practical engineering experience, and others on practical product and production experience. Some are simply are judgments about what the customer will want, and some are about technical pride — money and matter.

Design is the first step in a new product development. Next comes prototype production and testing. Fortunately, MOS had a chip - fabrication line that could be interrupted to produce samples of the new design. This was necessary for debugging and refining a design until it worked: “ David A. Ziembicke, then a production engineer at Commodore, recalls that typical fabrication times were a few weeks, and that in an emergency the captive fabrication facility could turn designs around in as little as four days ” (Perry and Wallich 1985, p. 49).

That cooperation between production and product design facilitated the exceptionally fast design - to -

product time of nine months. Thus, by November 1981, the chips were completed and samples produced and tested. All designs require debugging and refining; and reducing redesign time is one of the major keys to competitiveness.

For commercial success in innovation, technology strategy and product development strategy should also include production strategy.

INCREMENTAL INNOVATION IN PRODUCT DEVELOPMENT

In the product

-

development cycle, the technology implementation stage begins with research (benchmarking), which then leads to product planning (necessary and desired features), and then into product design before product production. This can be seen in Figure 7.2 .

INCREMENTAL INNOVATION IN PRODUCT DEVELOPMENT 143

Technology

Technology

anticipation

acquisition

Product

development

cycle

Technology

Technology

motivation

implementation

Research

Product

planning

Product

design

Product

production

Figure 7.2 Cyclic incremental innovation process Incremental technology improvements need to be motivated then anticipated and then acquired. Implementation of new technology next requires research in how to embed the technology into a new model of a product, plan that new model, design, and produce the new product model. All this is how the product development improves product models through incremental innovations.

Within the larger picture of a national innovation system, the product development cycle operates in a given business at the micro - level of a national innovation system, as shown in Figure 7.3 .

As we saw in the case of Commodore, new technology is introduced into a product during a product development process. New product design is not a single event, but a series of repetitious design activities taking place within the ongoing activities of the firm. Current products are being produced. New product models are being designed. Production improvement is being planned and implemented. New generations of product lines and/or new product lines are being researched, planned, and designed. Innovation in product development is a cyclic innovation process, focused on incremental innovations. This cyclic innovation process is driven by the need incremental improvements to technology to periodically improve the value of products to the customer.

The product development process operates in a cycle, innovating principally through incremental innovations.

144 INNOVATION AND PRODUCT DEVELOPMENT

National knowledge infrastructure

University research

Government research

sector

sector

Industrial research

Macro-level

sector

Micro-level

Transforming

Business

knowledge to utility

Technology

Technology

anticipation

acquisition

Market

Product

development

Customer

cycle

Technology

Technology

High-Tech

motivation

implementation

product/

service

Application

system

Figure 7.3 Product development cycle within a business and in the context of a national innovation system

CASE STUDY (CONTINUED):

Commodore 64 Personal Computer

Next, there was a very significant change in the stage of product planning in this case — a changed vision of the product. In November of 1981, Charpentier and Winterble reported the success of the project to the CEO of Commodore, Jack Tramiel. While listening to their intended sales of the chips, Tramiel had in the back of his mind the recent success of Commodore ’ s new personal computer product, the VIC - 20.

When Charpentier and Winterble showed Tramiel the new chips, Tramiel imagined a new personal computer product for Commodore that would use the competitive advantage of the new technology and provide a product successor to the popular VIC - 20. Tramiel saw that the new chips could be used in a new product as good as the best on the market but at a fraction of the price. He was also anticipating other new technological products soon to be introduced, new 64K random access memory (RAM) chips: “ Jack (Tramiel) made the bet that by the time we were ready to produce a product, 64K

RAM ’ s would be cheap enough for us to use, ” Charpentier said (Perry and Wallich 1985, p. 51).

DELAYS IN A PRODUCT DEVELOPMENT PROCESS 145

This is an example of the integration of technology strategy with business strategy.

The integration of technology strategy with business strategy in a new technology industry is the competitive force that drives economic long waves and industrial dynamics.

Tramiel decided to use Charpentier ’ s graphic and sound chips not to sell to competitors but to produce a new product, called the Commodore 64 personal computer, with a major competitive advantage in price: “ When the design of the Commodore 64 began, the overriding goals were simplicity and low cost. The initial cost of the Commodore 64 was targeted at $ 130; it turned out to be $ 135 ” (Perry and Wallich 1985, p. 51).

The competitive advantage for the Commodore 64 was its price and potentially improved graphics. Tramiel had set performance and price goals for a new product, the Commodore 64, which would use Charpentier ’ s new chips. But now the next problem was to design that computer. This new design was relatively straightfor-ward, since it was a redesign of existing personal computer designs.

DELAYS IN A PRODUCT DEVELOPMENT PROCESS

In a product development process, there is only a limited window of opportunity when innovating new technology in a product can gain a competitive advantage.

Tramiel saw a competitive advantage in selling personal computers, if he could get a significantly cheaper product out into the market before competitors.

A speedy product development process is necessary to use technological innovation as a competitive advantage. Moreover, once that window of opportunity opens, the product must have been correctly designed for the customer in terms of proper application focus, adequate performance and features, and reasonable pricing, safety, and dependability.

Speed and correctness are the criteria for a good product development process and are essential when innovating new technology.

But speed and correctness are not always easy to accomplish in the product development cycle, and different problems cause delays in the process. Jim Solberg emphasized that the different kinds of uncertainties taken together are the sources of delays and errors in the overall product design and production system (Solberg 1992). Solberg noted that each of these provide feedbacks of uncertainty in the prior decisions of parts of the overall system, depicted as errors and delays in the production development process, as shown in Figure 7.4 . These uncertainties result in changes and in demands for new work by preceding units that introduce delays into new product innovation.

146 INNOVATION AND PRODUCT DEVELOPMENT

Technology

implementation

Need for production improvement

Need for

new products

Product cost/quality problems

Research

Design

problems

Production

Product

problems

planning

Technology

risks

Product

design

Uncertainty

about

customer

Product

requirements

production

Tradeoffs

between

performance

Variation

and cost

in

production

Figure 7.4 Sources of delays in the product development process

• There are uncertainties about timing in the anticipation of new technology —

when will it be ready for embodiment in new products?

• There are uncertainties about the performance of new technologies — how much actual improvement will a technology achieve in a new product?

•

There are uncertainties in customer requirements in the new product — how will the new technology alter the customer ’ s use of the product?

• There are uncertainties in the tradeoff between performance and cost that the product designer can provide — what should be the appropriate tradeoff for the product design?

• There are uncertainties in production of the product from the inherent variability in the physical production processes — what design parameters could minimize the effect of such processes varaiability?

Production variation can create feedback loops in the product development process that create delays. A new product designed for production may turn out not to be producible to quality and cost without improving the production process. Production may be delayed until research creates the needed production improvement. Cost/ quality problems might require changes in product plans or in redesigns of a product.

DELAYS IN A PRODUCT DEVELOPMENT PROCESS 147

The performance/cost tradeoffs in product design may also create delay, requiring further product development from research or alteration of product plans and schedules for product introduction. Changes in customers or in customer requirements can also produce delays in planning new products by requiring more research for the creation for new products. The technical risks in research may result in research projects being delayed or in not achieving a desired performance level, which will delay the whole downstream product development process.

Solberg (and others) suggested that the development of procedures and software tools to promote concurrency and virtual and rapid prototyping in the product development cycle will speed up and aim toward correctness in the activities of a product development cycle. These include product development procedures and tools for managing innovation in the product development cycle.

Design and development time is an important factor in the commercial success of new products. Being too late into a market after competitors enter first is a disadvantage unless one comes in with a superior product or substantially lower price.

Essential to fast product development times is the use of multifunctional product development teams. For example, B. J. Zirger and Janet L. Hartley studied the product development times of several electronics companies (Zirger and Hartley 1996). They found that fast product developers had cross - functional development teams that included explicit goals about fast time to market and overlapped their development activities with concurrent engineering practices.

As another example, Jeffrey L. Funk studied product development at Mitsubishi ’ s Semiconductor Equipment Department and at Yokogawa Electric and identified their several strengths in product development: (1) the use of multifunctional teams in problem solving, (2) close relationships with customers and with suppliers, (3) focus in incremental improvements to product and production, and (4) learning how to improve the product development process (Funk 1993).

There have been many other studies about the importance of the product development cycle to commercial success. For example, Robert Copper studied a sample of 203 new product projects in 125 firms, interviewing senior managers. He identified seven factors that financially successful product - development projects have in common: 1. Superior products that delivered unique benefits to the user were more often commercially successful than “ me - too ” products.

2. Well - defined product specifications developed a clearly focused product development.

3. The quality of the execution of the technical activities in development, testing, and pilot production affected the quality of the product.

4. Technological synergy between the firm ’ s technical and production capabilities contributed to successful projects.

5. Marketing synergy between technical personnel and the firm ’ s sales force facilitated the development of successful products.

6. The quality of execution of marketing activities was also important to product success.

148 INNOVATION AND PRODUCT DEVELOPMENT

7. Products that were targeted for attractive markets in terms of inherent profitability added to success (Copper 1990).

We note that of these factors, the first and last have to do with the relationship of the product design to the market — a superior product aimed at a financially attractive market provides both competitiveness and profitability. Factors five and six have to do with marketing activity. Sales efforts are helped by products that fit well into an existing distribution system and by properly managed marketing activities that test and adapt a product to a market. Factors two, three, and four have to do with the quality of the technical development process. Good management of the technical activities in product development help produce good products. And good project management includes having a clearly focused product definition, managing well the different phases of the product development process, and having the proper technical skills to execute the project.

The success of a new product introduction requires not only a good product design but also requires good management of both the product - development process and marketing process.

The product development cycle should be managed to provide the following:

• Innovation in product performance

• Innovation in productivity

• Responsiveness to market changes

• Competitive advantages

Software development presents different management challenges than those of hardware development. In software, logic is particularly complex and production simple. In hardware, design is simpler than production. For example, Robert B.

Rowen emphasized that technical difficulty in large software projects arises both from the complexity of the application and the process of managing a large team of programmers (Rowen 1990). (The differences between hardware and software development is a topic we will later discuss in detail.) CASE STUDY (CONTINUED):

Commodore 64 Personal Computer

In the design of a new product (and particularly in every new high - tech product) there are always bugs — bugs in design and in production. Unfortunately, these small bugs can have a major competitive impact. This is why the process of innovation is not a one - shot thing. Commercially successful innovation requires a series of subsequent innovations.

DELAYS IN A PRODUCT DEVELOPMENT PROCESS 149

Despite the cleverness of the new Commodore 64 and its very low price, the product quickly gained a reputation for poor quality, which eventually hurt Commodore ’ s business reputation. One of the bugs was in Charpentier ’ s graphics chip design, which required a “ patch ” in order to allow the computer to display both black - and - white and color information without conflict. This problem was quickly fixed by Charpentier and was later corrected in a second model of the graphics chip.

Another bug arose from the decision to use a read - only memory (ROM) chip from the previous VIC - 20 in the new design. Used in the new product system of the Commodore 64, it proved sensitive to spurious voltage spikes that occurred when control over the bus changed. Unfortunately, it produced a visible “ sparkle, ” small spots of light dancing randomly on the display screen. This defect was found after three weeks of hard work and corrected.

There were other problems that came both from design choices and from production. For example, cheap components were purchased that degraded performance. Assembly errors were made that also affected performance. Finally, there was also a major problem in the disk drive, which operated too slowly.

This problem came from (1) a marketing - influenced decision for compatibility with the VIC - 20, and (2) from a deliberate decision by Commodore to scrimp on software development.

In the end, all the bugs, the problems, and the poor market and production decisions added up to a public perception of a poor - quality product: “ The one major flaw of the C - 64 is not in the machine itself, but in its disk drive. With a reasonably fast disc drive and an adequate disk - operating system (DOS), the C - 64 could compete in the business market with the Apple . . . . With the present disk drive, though, it is hard pressed to lose its image as a toy ” (Perry and Wallich 1985, p. 51).

We see in this example that the design of a product is only part of the competitive problem. Bugs will always occur and wrong decisions will be made. It is important to rapidly correct problems, improve production quality, and produce a new model correcting wrong decisions. Otherwise, the competitive advantage of the new technology can be rapidly lost.

In the product development cycle, continuing and rapid product improvement is fundamental to maintaining market share after a successful innovation.

With its relatively low price, the Commodore was a commercial hit for a time.

The Commodore 64 was singlehandedly responsible for driving Texas Instruments out of the home personal computer market and was a major contributor to the bankruptcy of Atari.

Even early in a new technology industry, price is important as a competitive factor, especially in a consumer market.

150 INNOVATION AND PRODUCT DEVELOPMENT

Techology-system progress in computer technology

1. Transistor density

2. Processor speed

3. Word length

Commodore 64

8 bit word length

16 bit word length

31 bit word length

64 bit word length

128 bit word length

Product-system computer lines

1. Main-frame computers (1950–1990)

2. Mini computers (1960–1980)

3. Work-station computers (1970–1980)

4. Personal computers (1980–present)

Commodore 64

5. Laptop computers (1995–present)

6. Cell phones (1990–present)

7. Smart phones (2000–present)

Figure 7.5 System progress in computers

Looking back at the history of performance quality of computer product lines, we can see where the Commodore 64 product fit into that history, as summarized in Figure 7.5 . The Commodore 64 lasted from 1981 to 1985, with an 8 - bit word -

length microprocessor.

Finally in this case of Commodore, one can ask six questions about how product development was managed:

1. How did Charpentier anticipate the possible technical change?

Charpentier had two different types of information in mind when his group decided to make a new generation of graphics and sound chips. The fi

rst was information about a “ market need, ” as the home electronics game market was exploding in 1980 and 1981. The second was information about the technical advances in chip density in 1980 – 1981 that were moving toward very large scale integration (VLSI), which Charpentier knew would provide the capability of creating a chip with 5 - micrometer line width technology. Together these provided a market and a technical basis for anticipating new technology. These two kinds of information sources for anticipating new technology are what we called market - pull and technology - pull .

2. How did Charpentier plan and create technical change?

Charpentier fi rst obtained approval for the project from Winterble, Commodore ’ s director of engineering, with an agreed - on project budget and deadlines. Technical goals, market goals, and budget and schedule goals were set. Technology strategy must be translatable into research and development projects. Implementation of technical change occurs within discrete technical projects that are planned, budgeted and managed.

DELAYS IN A PRODUCT DEVELOPMENT PROCESS 151

3. How did Tramiel, Winterble, and Charpentier plan a product to use the new technology?

The fi nal decision about the product use of the new technology involved not just engineering personnel (Charpentier and Winterble) but the CEO of Commodore, Tramiel. Tramiel redefi ned the product use of the technology, from the selling of the new chip to the internal use of the chip for a new personal computer model. Tramiel ’ s business strategy focused on the opportunities of the personal computer market, as providing a larger market and higher profi tability than as a chip supplier. The decision about what kind of product in which to implement new technology requires considera-tion of business criteria as well as technical criteria.

4. How was the product/technology strategy integrated with Commodore ’ s business strategy?

Tramiel ’ s business judgment focused on the market size and the price sensitivity of the home computer market. He correctly judged that at that time price was more immediately important to consumers than technical capability. If the Commodore could deliver the same or slightly better technical performance than competing home personal computers but at a fraction of the cost, it could knock out competition. And it did. The integration of technology strategy and business strategy requires a focus both on the competitive advantages of a new - technology product and on marketing and pricing strategy, which exploits these advantages.

5. Why did the product, after such dramatic initial success, eventually fail to build the business position of Commodore?

Tramiel did not apparently appreciate the impact that customer perception of quality would have on the long - term competitive position of Commodore. Poor decisions were made in design, production, procurement, and software development, which together created a public perception of Commodore ’ s poor quality and disappointing performance. Quality was never emphasized nor was the product improved in a timely manner for better performance. The C64 did not ensure Commodore ’ s future, as it quickly became obsolete. Every product provides an opportunity, then, for impressing customers about the quality of the company.

Product performance must include not only technical performance but also quality.

Any new product introduction by itself will never ensure the continuing business success of any company. Product improvement must be continuous, improving product quality. Tramiel ’ s strategy was short - term without long - term business vision. Tramiel failed to continue to invest in new technology and thereby succeed in the fast pace of competition in personal computer progress. Tramiel planned product development once, but not continuously.

In the long term, a company survives through a perception of both quality and cost — value and price.

152 INNOVATION AND PRODUCT DEVELOPMENT

PRODUCT QUALITY

Products can be high - tech type products (differentiable in technical performance) or commodity type products (not differentiable in technical performance). High - tech products can be competed through performance, but commodity products must be competed through cost, production quality, and/or fashion. Michael Porter once summarized the technology contribution to competitive strategy as either: (1) product differentiation or (2) low - cost and high - quality production (Porter 1985).

Accordingly, the term quality is an important term in business, but it is used with at least four meanings:

1. Performance quality — the quality of how well a product technically performs in its central function, a high - tech product of superior performance 2. Design quality — the quality of a product design as focused on a particular application, a high - tech product focus on application 3. Production quality

—

the quality of a production process that reproduces quantities of a product rapidly, without defects, and at low cost, a high - tech production process

4. Service quality

—

the quality of a product in service as to durability and maintenance, a high - tech serviceable product (Godfrey and Kolssar 1988) A technological innovation may affect one or more of the different kinds of quality: performance, design, production, and service.

Technological innovation is an important source of improving product/service quality, in all of the meanings of quality.

KEY IDEAS

In the case study of:

• Commodore 64 PC

We have examined the key ideas of:

• Market - pull or technology - push innovation

• Incremental innovation in the product development cycle

• Product development process

• Product development delays

• Product quality

8

INNOVATION AND STRATEGY

INTRODUCTION

How does a business formulate and implement innovation strategy? This is complicated because different aspects of innovation implementation occur at different levels of a large corporation. We look at how a corporate organization facilitates (or hinders) proper innovation strategy and implementation. We ask:

• How do strategic perspectives about technology differ between the two views in a business — top view and bottom view?

• What aspects of implementation of innovation occur at the top, middle, and bottom of a corporation?

As shown in Figure

8.1

, we will look at how the worlds of matter and

money

properly interact (or fail to properly interact) in a hierarchical business organization.

Research

Invent

Commercialize

Market

Nature

Science

Technology

Economy

Utility

Exist

Discover

Manipulate

Embed

Use

naturally

nature

nature

nature

nature

The world as material

The world as money

Innovation strategy in corporate organization

Figure 8.1 Innovation process

153

154 INNOVATION AND STRATEGY

CASE STUDY:

3M Diversifies Through Innovation

The case study of the 3M Corporation provides an example of a large firm that has long been successful in managing innovation at the different levels of its organization. For example, in 1980 95 percent of 3M ’ s sales of $ 5.4 million were innovated as products from its coating and bonding technology. At that time 3M

made about 45,000 products (including product variations in size and shape). They were diverse products, from sandpaper and photocopiers to tape, skin lotions, and electrical connectors. This was 3M ’ s secret for diversification success: They knew their businesses, for they had invented most of them (Smith 1980).

3M encouraged innovation by its employees, presenting annual prizes to its best innovators with its Carlton Awards. The award was named after Richard Carlton, president of 3M from 1949 to 1953. It was given annually to a few scientists who have made major contributions to 3M ’ s technology. For example, Paul Hansen developed a self - adhesive elastic bandage sold by 3M under the Coban label. Dennis Enright developed telephone - cable splicing connectors. Arthur Kotz developed an electronic system for microfilming directly from computers. W. H.

Pearlson ’ s research in fluorine chemistry led to the development of agrichemical products for 3M and to its Scotchgard fabric - protection spray (Smith 1980).

In 1980, there were forty business divisions created from products developed by 3M ’ s research. Earnings for 3M had risen each year from 1951 through 1980

(except in the 1972 oil - crunch year).

Growth through innovation had been 3M ’ s strategy since its early days. It began as Minnesota Mining & Manufacturing Co. in 1902 at Two Harbors, Minnesota, when local investors purchased a mine. The mine was supposed to have contained high - grade corundum, a hard mineral used in abrasives. Instead, the corundum was low grade, useful only for ordinary sandpaper. Sandpaper was a commodity business with low profit margins. The disappointed investors decided to look for products with higher value. The new company sent its sales personnel searching for innovative ideas. They went onto the shop floors of their customers to look for needs for which no one was providing a product. In automobile factories, they saw workers choking on dust from the dry sandpaper. They reported this to 3M, and researchers created a sandpaper that could be used when wet. This was the first step in starting 3M ’ s technological capability — adhesives and coatings. It was also the first step in 3M ’ s success formula — communication between salespeople and researchers for innovation.

The next product also came from observations by the salespeople. They noticed that workers in automobile plants had a difficult time keeping paint from running on two - tone - painted cars. Richard Drew, a young technician in 3M ’ s lab, invented masking tape. Another famous 3M product also came from Drew ’ s inventiveness.

In 1930, he conceived of how to put adhesive on cellophane. (Cellophane itself had been invented by DuPont in 1924.) Next, John Borden, a sales manager at 3M, created a tape dispenser with a blade on it — and the “ Scotch Tape ” product was born.

INTRODUCTION

155

About half of 3M ’ s innovative products came from 3M salespeople looking for needs in their customers ’ shops and offices — market pull. The other half have occurred from bright research ideas looking for applications — technology push.

For example, 3M ’ s research lab came up with a synthetic fabric from pressing rayon or nylon fibers together; it was unique in that it had no weave (similar to felt material). 3M thought first of using it for disposable diapers, but it was too expensive. Then it thought of using it for seamless cups for brassieres, but again, that was too expensive. The health care division came up with the right application — surgical masks, which would be more comfortable for doctors and nurses than woven masks because they could be pressed into the right shape — and hospitals could afford it.

That 3M ’ s divisions were created from innovations contributed to an organizational culture in which innovation was seen as a high priority to the corporation.

Furthermore, each product - line division had an associated product - development department, to maintain and improve the technology and applications of product lines. Above the divisions, the 3M board and chief operating officer ran the corporation with a vice president of R & D. Reporting to the VP of R & D were two central corporate units — a corporate research laboratory and a new business development unit. Thus, 3M was organizationally structured for business diversification by innovation — divisions created from innovations and a central corporate research laboratory creating new innovations, which were nurtured into businesses by a new business development unit.

In writing about 3M ’ s success, Ed Roberts emphasized that in 3M ’ s culture, the top management commitment to innovation was clearly articulated in policy:

“ From top to bottom 3M ’ s management provides active, spirited encourage-ment for new venture generation. Many at the company even speak of a special eleventh commandment: ‘ Thou shalt not kill a new product idea ’ ” (Roberts 1980, p. 139). The company also provided multiple sources of funding support within the company for new ventures. Any of the different groups could provide funding. If an idea was taken to one group and turned down, the proposer was free to try his idea with any other 3M group. The company used product teams, which they called “ business development units. ” Early in the development of a new product, a complete product team was recruited from research, marketing, finance, and manufacturing. These teams were voluntary, in order to build in commitment and initiative. The incentives to join a new venture for 3M employees were the opportunities for advancement and promotion that the sales growth of a new venture might provide. 3M also emphasized direct financial measures of performance for each new venture: return on investment, profit margin, and sales growth rate.

Edward Roberts summarizes some of the requirements to creating corporate growth through new innovative business ventures: (1) proper organization, (2) top management committed to innovation, (3) appropriate funding for innovation, (4) innovative product teams, (5) proper reward systems for internal entrepreneurial activity, and (6) proper performance measures for gauging the contribution of innovation to the corporate bottom line (Roberts 1980).

156 INNOVATION AND STRATEGY

MICRO - LEVEL INNOVATION IN A BUSINESS

There are different levels of organizational activity in any corporation, and as Rias Van Wyk has emphasized, innovation strategy must differ at the different levels of business organization (Van Wyk 2009). In any large firm there must occur a proper coordination of innovation strategy across the different organizational levels.

We can picture this by viewing innovation strategy as a distributed management process — occurring at the different levels in a firm, as shown in Figure 8.2 .

In a diversified firm, there are usually at least four levels of management hierarchy: 1. Corporate level — Board, CEO, and firm executive team 2. Business level — President and business executive team 3. Division level — Division director and administrative staff 4. Office level — Office/project manager and technical staff Properly implemented, innovation strategy focuses on different issues at each level of a corporate hierarchical structure of an innovative company, with a different strategic focus at each level:

1. At the corporate - level of innovation strategy , strategic thinking for innovation is focused on diversification and merger strategy — based on innovation.

Authority

structure

Functional

Corporate-level

Board of directors

specialization

innovation strategy

Chief executive officer

President

President

President

Business-level

business 1

business 2

business 3

innovation strategy

Divisional-level

innovation strategy

Information

Production

Marketing

Finance

Personnel

Research and

technology

manager

manager

manager

manager

engineering

managers

manager

Operations-

Operations

Operations

Operations

Operations

Operations

Operations

level

level

level

level

level

level

level

innovation

strategy

Figure 8.2 Levels of strategic thinking in a corporation MICRO-LEVEL INNOVATION IN A BUSINESS 157

2. At the business - level innovation strategy, strategic thinking for innovation is focused on launching new high

-

tech business ventures

—

built on

innovation.

3. At the divisional - level innovation strategy , strategic thinking for innovation is focused on the management of technical programs for business competition —

implementing innovation in new products/services/processes.

4. At the operations - level innovation strategy , strategic thinking for innovation is focused on the management of technical projects for business competition — developing new products/services/processes.

In organization theory, it is recognized that all organizations have both authority structures and operational processes. An authority structure divides the organization hierarchically by power and responsibility and horizontally by functions.

Complimentary to an authority structure, operational processes integrate procedures across the business functions to produce and sell products. As operations, organizations are established to perform repetitive activities of value adding — in transforming resources to sales. This creates profits through economies of scale and scope.

Business processes are designed within the organization to order these repetitive activities as operations, controlled by procedures. The design (or redesign) of the types and sequence of activities and the procedures to control these activities forms the operational processes of an organization.

But operations require focus and coordination, and for this an authority structure is established over the operations — to focus all operations on achieving a mission of the organization. Authority structures are usually expressed by the organization chart of a business. Typical authority structures within a business are organized by business function with a manager for each function: production, marketing, finance, personnel, research and engineering, and information technology. The authority structure of an organization assigns power and accountability to operate in divisions of labor over the operations structure.

Successful innovation occurs when the strategies in these levels are properly integrated. Conversely, failures to properly integrate the levels of strategy have resulted in major business losses.

CASE STUDY:

Merger of AOL and Time Warner

Just how financially serious it can be to fail to integrate levels (1) and (4) of the four levels of strategy can be seen in the case of the merger of AOL and Time Warner in 2000. It turned out to be a financial disaster for Time Warner shareholders.

We recall that in the last decade of the twentieth century, the innovation of the Internet was beginning to change everything. That booming U.S. stock market 158 INNOVATION AND STRATEGY

had priced most of these new e - companies exceedingly high. America Online (AOL) was one of these, providing service access to the Internet to subscribers. In January, it used its very high market value to merge with an older media company, Time Warner.

The business community thought this merger was the first sign that the electronic marketplace, e - commerce, was beginning to mature. For example, Richard Siklos and Catherine Yang of Business Week wrote about the deal: On the surface, it looked like just another awesome megadeal . . . . America Online is the acquirer. The trading symbol for the new company, tellingly, is AOL. Given the realities of the New Economy, it could hardly be otherwise. By now, the pattern is clear: the digital will prevail over the analog, new media will grow faster than old, and the leaders of the Net economy will become the 21st century Establishment (Siklos and Yang 2000, p. 37).

On December 10, 1999, the market capitalization of America Online was about $ 250 billion, whereas the market capitalization of Time Warner was about $ 85 billion (Loomis 2000). The difference was in the stock market ’ s multiplication of their relative price - to - earnings (P/E) ratios. In the last twelve months, AOL had earnings of about $ 1 billion, so that its P/E was 250/1. Time Warner ’ s earnings were about $ 1.3

billion, so that its P/E ratio was about 65 to 1. Thus, AOL ’ s P/E was being valued over Time Warner ’ s P/E at a multiple of 250/65 — so that AOL stock was 3.8 times more valuable than Time Warner ’ s stock, based on earnings. This was the heart of the deal. AOL ’ s vast P/E ratio gave it the leverage to take over Time Warner, and Time Warner was willing to be acquired, hoping the resulting company would have a P/E ratio more like AOL than Time Warner ’ s. AOL was in a rapidly growing new market, e - commerce, into which Time Warner had tried to enter but failed.

Yet in terms of assets — valuable products and steady, proved earnings — Time Warner had a much larger asset base. For example, Time Warner had 73 million consumer subscriptions and AOL had 24 million. Time Warner product brands included: (1) the magazines of Time, People, Sports Illustrated, Fortune, Money — all major magazines of the time; (2) the cable network companies of HBO, Cinemax, CNN, and TNT, among others — major cable channels of the time; and (3) the movie and music production company of Warner Bros. In contrast, America Online had AOL, Netscape Navigator, and stakes in several companies.

Time Warner brought to the merger a powerhouse of media content - producing companies. Whereas America Online principally brought success in the new electronic businesses of the time, Barry Schuler (then president of AOL Interactive Services) expressed AOL ’ s strengths: “ We [AOL] are good at aggregating eyeballs and delivering services [on the Internet] ” (Nocera 2000, p. 68).

AOL purchased Time Warner for $ 183 billion, but with AOL having just one -

fifth Time Warner ’ s revenue and only 15 percent of its employees. Time Warner, once a new upstart in the 1920s, had become a major media establishment company by the 1990s.

MICRO-LEVEL INNOVATION IN A BUSINESS 159

The merger of Time Warner in AOL was motivated by the hope of major shareholders of Time Warner that the higher P/E of AOL would rub off on their stock. The deal was to have AOL shareholders receive one share in the new company for each of their current AOL shares, and for Time Warner shareholders to receive 1.5 shares for each of their current Time Warner shares. The end result was that AOL shareholders had 55 percent of the new company and Time Warner shareholders had 45

percent of the new company. At the time, the Time Warner share holders expected a market premium of 70 percent for their shares.

What were the strategies of the two CEOs of AOL and Time Warner when they merged? Steven Case, founder and then CEO of AOL, had two major strategies.

The first was to transmute AOL ’ s high trading multiple of the booming market stock market of 1999 into assets and revenue streams that would survive any drop in the high - tech companies ’ valuation of that time: Time Warner stood out as the only company with the content, distribution, global reach and customers. Case wants it all: The branded content from Warner Music, Turner cable networks, and Time Inc. Magazines that can be digitized and sold online. The cable pipes to speed delivery of AOL.

A global promotional platform that will save AOL a fortune in ad spending. Relationships with about 73 million subscribers to Time Warner cable systems, HBO, and Time Inc. Magazines . . . . Time Warner ’ s old - fashioned media properties deliver a stable stream of revenues, about $ 27.1 billion in 1999, and cash flow, about $ 6 billion . . . that are shielded from the vagaries of the Internet world (Gunther 2000, p. 74).

From a bottom - up strategic perspective (business up to the larger world), Case ’ s strategic perspective was that the cash flows of Time Warner ’ s major publication and television empire would provide AOL with a steady and major source of income over the long term. Also from a bottom - up strategic perspective, the acquisition of Time Warner ’ s businesses would provide a step in the direction of improving AOL ’

s bandwidth problem in information technology. AOL had been providing Internet service connection through customers ’ existing copper telephone lines — slow and technically limited to 54 Kbit modem connections.

The market demand was for broadband Internet connections. Time Warner then owned a major cable company, which could provide these much faster broadband connection to its cable customers. So AOL, having Time Warner, had access to a cable - customer market of 20 million.

The CEO of Time Warner before the merger was Gerald Levin. His strategic perspective for Time Warner also involved various top - down and bottom - up strategic perspectives. From the top - down — anticipating the growing importance of the Internet and electronic commerce — Levin saw the need to continue moving Time Warner into the digital world:

Levin can empathize (with Case ’ s vision of the Internet world), as a cable and tech guy stuck atop a content giant . . . Before Ted Turner dreamed 160 INNOVATION AND STRATEGY

up CNN, Levin made his reputation by putting HBO onto a satellite in 1975. He ’ s also been burned by technology, notably when Time Warner spent upwards of $ 100 million on a prototype interactive TV network in Orlando. But his biggest tech bet, on the potential of two - way cable lines, paid off handsomely . . . . Time Warner ’ s stock, a so - so performer for much of the 1990s, surged . . . during the period since Levin took over in 1993 (Gunther 2000, p. 74).

From the bottom - up perspective of Time Warner ’ s recent business capabilities, Levin saw a strategic advantage for immediately merging Time Warner into one of the biggest successful players in electronic commerce. Levin ’ s ventures for Time Warner into the Internet world had not been strategically successful: “ But Levin ’ s hard - won reputation as a tech - savvy executive has faded since then. He passed up the opportunity to buy a portal like Lycos or Excite, and Time Warner ’ s own Internet hub, called Pathfinder, flopped . . . So when Case called to offer him the chance to be CEO of AOL Time Warner — the biggest game in cyberspace and media! — why, how could Levin resist? ” (Gunther 2000, p. 74).

In July 2000, shareholders of both companies approved the merger. But eventual success of the merger was not certain. Both CEOs had argued for a business synergy between content provision and content delivery — which was assumed would increase combined revenue, but in practice did not. Why not?

Both AOL and Time Warner were in the businesses of information. AOL was in the business of being an information channel provider as an Internet service provider. Time Warner was both in the business of providing information channels (television, movies, and magazines) and also as an information content creator in these channels. Progress in information technology was bringing both these firms into similar business strategies — channels and content. Strategic thinking by both CEOs required two different perspectives on the future: (1) a perspective on changes in the larger environment of the business and (2) a perspective on future business operations about their current strengths and weaknesses to changes in operations for future

strengths.

What happened in terms of strategy was that the CEO of AOL (who understood (1) to (3) level integration) persuaded the Time Warner CEO to merge, when the latter had not understood the technical problems at the (3) level of AOL. The merger was beneficial to AOL shareholders, who had their short - term valuable stock transformed into long - term valuable Time Warner shares. But it was bad for Time Warner shareholders, who would, due to the merger, lose three - quarters of their former wealth in Time Warner.

The CEO of Time Warner apparently had not fully appreciated the technical future of the Internet (the bottom - up technical strategy) — wherein only high - speed connection was desired by customers. AOL would eventually decline because it could not provide high - speed, broadband access to the Internet. Nearly a decade later in December of 2009, Time Warner spun off AOL as a separate company —

losing the entire value of AOL from that merger, a cost born by the earlier Time Warner shareholders.

TOP-DOWN AND BOTTOM-UP STRATEGY IN INNOVATION 161

TOP - DOWN AND BOTTOM - UP STRATEGY IN INNOVATION

The concept of innovation strategy is a concept within the domain of a topic called strategic thinking . The idea of strategy is to make present decisions to guide an organization ’ s future directions . And in terms of the future, the long - term basic problem for any company is survival . Lowell Steele of General Electric once summarized:

“ To survive over the long term, a company must have two strategic capabilities — the capability to prosper and the capability to change ” (Steele 1989).

The failure to prosper imperils corporate survival because when expenses exceed income over a long enough period, any company will fail and end in bankruptcy.

Profitability is necessary for any company in the long term. And now prosperity requires not only profitability but also growth. Modern stock markets often value asset growth over short - term dividends. In corporate takeovers, continual corporate growth in earnings and sales is necessary for management to retain control. This combination of continuing profitability and continual growth presents a tough strategic problem — because all markets eventually mature and growth in a company ’ s business is limited by growth of its markets. The need for continual corporate growth has (1) created the driving force for corporate diversification but also (2) is the root cause of the dissolution of large companies. Successful management of a portfolio of different businesses in the same company is a very big challenge.

To maintain corporate prosperity over time, periodic change in business strategy is necessary to adapt the firm to new times. One of the successful CEOs at the end of the twentieth century was Jack Welch, and his most widely quoted strategic precept was: “ Control your own destiny, or someone else will ” (Teitleman 2001, p. 31). The failure to control destiny — as the failure to perform appropriate change at the right time — imperils future survival. Business exists in competition. For a business to survive, it must continue to be competitive. A business may become competitively obsolete — in its products and in its services — providing less value to customers than competitors provide. Change requires an ability to anticipate the external dynamics of the environments in which a company operates — markets, competition, innovation, government regulation, economic conditions, globalization, and so on. Change also requires an ability to alter a company ’ s directions — e.g., in products, production, marketing, organization, personnel, businesses, and so on. Lowell Steele emphasized that change should be the focus of strategic thinking: “ Strategy is concerned overwhelmingly with questions of change. How much must the enterprise change in order to survive and to continue to prosper? How much change can it finance and manage? How fast can it change? These are profoundly difficult questions ” (Steele 1989, p. 178).

As we noted, in thinking strategically, there are two perspectives from which to view a corporation ’ s strategy: (1) big picture and (2) operational picture. The big picture can most easily be viewed from a top - down perspective and the operational picture can most easily be viewed from a bottom - up perspective, as shown in Figure 8.3 .

The two different perspectives create different views and even different kinds of logics in strategic thinking: (1) a big picture view with a logic of proceeding from 162 INNOVATION AND STRATEGY

Environment

Company

Planning scenario

Strategic plan

Top-down logic

Bottom-up logic

1. Scan environments of

1. Trends in sales and

businesses

customer applications

2. Interpret changes in

2. Benchmark products

environments as threats

and services against

or opportunities to

competitors’

businesses futures

3. Anticipate

3. Analyze present business

technological

operations in terms of

progress

strengths and weaknesses

for the future

4. Identify operations

and support functions

4. Redefine business portfolio

for process

strategy to create strong

reengineering

future industry positions

5. Forulate technology

5. Set targets for businesses

strategy and business

and allocate resources

strategy

Company

Operating unit

Business strategic model

Technology strategy

Figure 8.3 Two perspectives on strategy

the general to the specific changes of the future, and (2) an operational reality view with a logic of proceeding from the specific to the general changes of the future.

In this logic of strategic thinking, one can always look toward the future by describing the “ big picture of everything ” and then deducing how changes there can impact the particular situation of one ’ s own future action. At the top of an organization, information to see the big picture is more readily available than at the bottom of an organization. Conversely, at the bottom of an organization, information to see the reality of operations is more readily available than at the top of the organization.

This strategic challenge that the CEO of TimeWarner failed at — of understanding innovation strategy both from top - down and bottom - up perspectives — is a frequent failing of leadership.

Historically, a very famous (and terrible) example of these differences in information between the big - picture - of - the - world and the reality - of - operations was the difference in perspective between the generals and the soldiers in the tragic First World War in Europe. From 1914 to 1918, the war stagnated into trench warfare, with the generals on both sides planning one more great battle to win the war. Each battle resulted in thousands of deaths with no substantial gain in territory. The ‘ big picture ’

of all generals ’ strategic perspectives was simply to mass both artillery and soldiers for an attack. But from the soldiers ’ operational perspective, the deadly combination of the massed artillery and the new technology of the machine gun made every attack simply impossible — resulting only in slaughter of the attackers.

After four years of this trench war, both the German/Austrian armies and the British/French armies were too exhausted to win, and a new, fresh army of Americans TOP-DOWN AND BOTTOM-UP STRATEGY IN INNOVATION 163

was brought into battle to finally end the war. But all during that war, was that the failure of that ‘ big - picture - strategy - of - massed attack ’ big - picturestrategy of massed attacks All the generals thought failure was simply been due to a lack of courageous spirit in their soldiers. None of the generals of that war personally experienced the operational reality of charging into the withering storm of rapid machine - gun fire.

But the lieutenants in those armies had such bitter experience; and when, as generals, they prepared for the next war (World War II), they changed their big - picture battle strategy toward the innovation of the battle tank and the airplane.

The top - down perspective of innovation strategy uses a deductive logic that begins with the great picture of change in markets and goes down toward the smaller units of business.

The bottom - up perspective of innovation strategy uses an inductive logic that begins with the specific picture of operations and generalizes toward technological progress.

As we saw in the AOL – Time Warner case, these top - down and bottom - up perspectives are critical to business success. We can look again at the hierarchical structure and strategy in innovation and see where these perspectives should operate, as shown in Figure 8.4 .

Corporate-level innovation strategy:

Authority

Funding corporate research

Board of directors

structure

Chief executive officer

President

Top

Business-level innovation strategy:

business 1

Down

Launching new business ventures

Top

Down

Functional

and

specialization

Bottom

up

Bottom

up

Bottom

up

Research and

Information

Divisional-level innovation strategy:

engineering

technology

managers

manager

Programs of product/service planning

Project-level innovation strategy:

Operations

Operations

Research project and product-development projects

level

level

Figure 8.4 Levels of innovation strategy in a corporation 164 INNOVATION AND STRATEGY

Yet the reality of power in corporations creates problems about the proper communication of innovation strategy, particularly bottom to top. In a large corporation, it is the proper coordination of the top with the bottom that is critical to successful innovation. But this coordination is always difficult because the top level has much more political power in a firm than does the bottom operational level.

This difference of power sometimes makes it difficult for bottom - level operational personnel to communicate news (particularly bad news) upward to the powerful top - level executives.

For successful innovation at the micro - level of a business, successful innovation strategy must be coordinated between the different levels of innovation: top - to - bottom and bottom - to - top.

The most difficult challenge for innovation strategy lies in the proper integration of (1) financial innovation strategy at the top - level with (3) technical innovation strategy at the bottom level.

To properly think about the integration of top - down and bottom - up perspectives in innovation strategy, use a checklist of strategic thinking: 1. Check that top - level strategy is focused on innovation through financing research and rewarding innovation.

2. Check that middle - level strategy is focused on entrepreneurial launching of new high - tech business ventures.

3. Check that bottom - level strategy is properly focused on research and development projects for innovation.

TOP - LEVEL INNOVATION STRATEGY

From the top -

level, corporate innovation strategy has two aspects — a research aspect and a financial aspect. Research should focus on funding innovation, while the finance should focus on business acquisitions, which could provide future innovation capability. Corporate growth can occur from research by launching new product lines and new businesses — financed by cash flows from existing businesses.

Corporate strategic competencies can be in innovation strengths, in marketing strengths, in financial strengths, or in managerial strengths. For example, in innovation, Jon Didrichsen distinguished between corporations that had a strong kind of technological branching competence, such as 3M in adhesives and coatings, and those that have a broad technological competence in a scientific area, such as DuPont in chemistry (Didrichsen 1972). Later, C. K. Prahalad and Gary Hamel argued that a strategic competency was necessary for diversified corporations in the new conditions of global competition: “ During the 1980s, the top executives were judged on their ability to restructure, declutter, and delayer their corporations.

TOP-LEVEL INNOVATION STRATEGY 165

In the 1990s, they ’ ll be judged on their ability to identify, cultivate, and exploit the core competencies that make growth possible — indeed, they ’ ll have to rethink the concept of the corporation itself ” (Prahalad and Hamel 1990, p. 79).

CASE STUDY:

Cymer Inc.

We turn next to the middle - level focus of innovation strategy. This should result in the starting of a new business venture based on new technology. As an illustration of a new business start - up on the basis of new technology, we can look at Cymer Inc. It was commercially successful because the entrepreneur ’ s product vision correctly met a new market need — value to price for a customer. The historical setting was the middle of the 1990s, when the world ’ s IC semiconductor chip industry continued its pace of rapid technological progress in increasing transistor density on a chip by reducing feature size. Technical progress in chips had proceeded exponentially through a series of next - generation technologies:

“ Over the past 30 years . . . chip makers doubled the number of transistors on a microchip every 18 months. For example, Intel ’ s Pentium, vintage 1993, had 3.2 million transistors; its Pentium Pro, released about two years later, uses 5.5

million transistors ” (McHugh 1997).

IC chips were produced by (1) projecting circuit diagrams onto photographic coatings on silicon wafers and then (2) etching the photographic pattern into physical features. As feature

-

size of transistors approaches the size of the

wavelength of the projecting light, that light can no longer image accurate features. (This is the result of the wave nature of light, whose diffraction patterns around objects ultimately limits the resolution of the images at given wavelength of light.)

In the 1960s, chip makers used visible light. By the late 1990s, chip makers used the shorter wavelengths of invisible ultraviolet light, produced by hot mercury gas, getting chip feature size down to 0.35 micron. Next, chip makers hoped to jump from mercury light down to x - rays, which have much smaller wavelengths; but in 1997, x - rays were still too costly and difficult to use for chip making. The new light source for chip photo - lithography was excimer laser light at a 0.25 micron wavelength. In 1997, these lasers were produced by a relatively new firm called Cymer Inc. (McHugh 1997).

In 1985, two recent PhDs from the University of California at San Diego, Robert Akins and Richard Sandstrom, were considering their futures. Akins ’ s thesis research was in optical information processing that uses lasers, and Sandstrom ’ s thesis research was in laser physics. They were working for a defense contractor, HLX Inc., on esoteric projects that used lasers (such as laser - induced nuclear fusion and satellite - to - submarine laser communications). One day they were relaxing on a beach at Del Mar, tossing Frisbees and drinking beer. They both wanted to make real money, and they speculated about opening a business.

166 INNOVATION AND STRATEGY

They even discussed fast - food outlets. But they decided to use their special talent in lasers. They founded Cymer Laser Technologies in 1986 to build excimer lasers.

Excimer lasers could produce laser light by using a mixture of gases (krypton and fluorine), pumped into a two - foot aluminum tube, which is then zapped by a 12,000 volt charge across two electrodes inside the tube. The voltage creates a charge in the gases for 75 billionths of a second that excites the krypton atoms to temporarily couple with the fluorine atoms. This forms a temporary krypton -

fluorine molecule, but the unstable molecule breaks apart as soon as the voltage drops. In doing so, it releases a burst of deep - ultraviolet laser light of 0.25 micron wavelength.

In 1986, Akins and Sandstrom began building the prototypes of this excimer laser in the research labs of the university (for which their new company owed the University of California $ 250,000 for the use of its facilities). Cymer also succeeding in winning research funding from the U.S. government ’ s defense agencies to continue the development work for the laser. The tough technical problem was getting the laser to run dependably for months at a time, while handling the thousand - times - per - second jolts of 12,000 - volt charges to the laser. During this time of product development, Akins and Sandstrom took out second mortgages on their homes to keep Cymer running.

In 1988, Cymer received its first outside investment from a venture capitalist, Richard Abraham. Abraham had been a researcher and factory manager at Fairchild Semiconductor, Motorola, and Texas Instruments. He understood the importance of the deep - ultraviolet laser to the future production needs of the IC

chip industry. Abraham ’ s condition for investing was that “ Cymer had to focus entirely on semiconductor applications for its lasers ” (McHugh 1997, p. 156).

Later in that year, further investments came from Canon and Nikon, after teams of their scientists and executives made visits to Cymer. Canon and Nikon manufactured the photo equipment, called steppers, which semiconductor manufacturers use to step the photo of a circuit over a silicon wafer. They would be customers for Clymer ’

s laser. They bought 6 percent of Cymer.

In 1995, semiconductor manufacturers began to use excimer laser light for production of chips: “ Demand from chip makers forced the stepper companies (such as Canon and Nikon) right into high - volume buying soon after Cymer launched its $ 450,000 excimer laser model ” (McHugh 1997, p. 156).

Cymer ’ s sales exploded: $ 18 million in 1995 and $ 65 million in 1996. In September 1996, Cymer sold stock to the public at $ 9.50 a share and in December at $ 42 a share. It raised $ 80 million in the two offerings. In February of 1997, Cymer stock traded at $ 50. Akins ’ s stake was 2 percent and worth $ 2 million.

(He hadn ’ t needed to sell hamburgers, after all.) As we earlier discussed about national innovation systems, this case nicely illustrates the point about innovationoccuring in two contexts of a macro - level and a micro - level. At the macro - level context, research is performed in a national innovation system, which is innovated commercially in the micro - level context of an individual business. As shown in Figure 8.5 , there was, for Cymer, (1) a source MID-LEVEL INNOVATION STRATEGY—NEW HIGH-TECH BUSINESS VENTURES 167

Two recent PhD’s from the University of California at San Diego National knowledge infrastructure

Research funding from

the U.S. Government’s

University

Government

defense agencies

research sector

research sector

Industrial

research sector

Transforming

knowledge to utility

Investment

Market

Business

Product/service

Customer

Cymer

Excimer lasers

Canon and Nikon

Application system

Chip photo-lithography

Figure 8.5 Transforming knowledge to value of new technology from research projects in a university that (2) were funded by a government agency, along with (3) venture capital to invest in Cymer.

We recall that this is a general pattern for high - tech new ventures within a national innovation system, containing: (1) research universities, (2) government research - funding agencies, and (3) venture capital sources.

MID - LEVEL INNOVA TION STRATEGY — NEW HIGH - TECH

BUSINESS VENTURES

Historically, most radically new technologies have been innovated by new small businesses. The particular challenge to a new high - tech business is to design a new high - performance product and also develop a new market for it — both at the same time. But the importance of the new technology justifies the business risks.

For example, in early studies of the success of new business ventures in the United States in the 1970s, new high - tech business ventures had already showed twice the survival rate of other new business ventures (Vesper 1980).

For new business ventures, the appropriate management style is an entrepreneur style. This style is in contrast that of a manager in a big, existing organization, a bureaucratic manager . These distinguish two different types of leadership in (1) starting economic activities as opposed to (2) running ongoing economic activities. Entrepreneurs start new firms, while managers run ongoing firms. There are 168 INNOVATION AND STRATEGY

important differences in the optimal leadership styles for starting versus running.

Often, good entrepreneurs make poor managers of ongoing operations; and often good managers make poor entrepreneurs when presented the challenge of dealing with substantial change.

What does a new high

-

tech venture require? Six key new high

-

tech venture

ingredients follow:

1. A source of advanced new technology from research . For Cymer, the source of new technology occurred in university research funded by government agency.

2. A new high - tech product or process or service based on the new technology .

For Cymer, the new high - tech product was an excimer laser.

3. A focus on a customer willing to purchase the new high - tech product/service, a market . For Cymer, the customers were firms such as Canon and Nikon, which would use the new laser in new chip photo - lithography equipment.

4. An organization of a new firm to produce and market the new product/process/

service to its focused market . Cymer was incorporated as a new company.

5. An initial venture team to launch the new high - tech firm, which includes technical, business, and marketing and financial experience . Cymer was founded by two technical people, Akins and Sandstrom, who became entrepreneurs.

6. Capital necessary to launch the new high - tech firm . Initial venture capital funding for Cymer came from Richard Abraham.

The management team for the new venture should include persons experienced and skilled in (1) technology and product/process/service design, (2) production management, (3) marketing management, and (4) capital and financial management. Of course, often new teams are begun with less than four persons, but each of the four areas of product, production, sales, and finances must be covered — even initially by one or more persons doing double duty. The sixth key ingredient in a new business venture is the requirement for capital. Capital provides the ability to gather and organize resources necessary to begin or expand a productive operation. All capital requirements are evaluated not only on amount but also on timing. In estimating the requirement of capital, the new venture team must carefully forecast its requirements amount and when required, as well as the timeline for returning to investors their capital.

In launching a new venture, timing is critical. Integrating technology change and commercial application must occur at a time of market opportunity. The financial risks are several: (1) having the capital necessary to develop and produce a product/

service; (2) having the capital required to exploit the market opportunity; (3) timing the exploitation to recover the invested capital; and (4) generating sufficient working capital in order to meet competitive challenges. The competitive advantages of a strategic technology occur within a window of opportunity, followed by competitive challenges to this opportunity. When technology leaders introduce a new product that creates new markets, competitors will enter by focusing on the obviously weak features of the innovative product, and the new venture needs to maintain its competitive position by continuing more innovation.

MID-LEVEL INNOVATION STRATEGY—NEW HIGH-TECH BUSINESS VENTURES 169

Many new ventures go bankrupt due to a cash

-

flow problem

—

not receiving

enough income to pay current bills. The critical delays in the activities that decrease cash - flows are as follows:

• Production development that does not attain superior technical performance

• Sales efforts that lag behind sales projections

• Production volume that significantly exceeds product demand

• Delay in delivery of products sufficient to lose sales

• Delays or failure of customers to pay for purchases CASE STUDY :

Pilkington ’ s Float Glass R & D Project

Finally, we turn to the corporate bottom - level of innovation implementation — the technical project. We can illustrate this level in the case study of the Pilkington ’ s innovation of the float - glass process — as described by Christopher Layton (Layton 1972). The historical setting of this project occurred in the 1950s when an invention to radically change how to manufacture plate glass was developed and substituted as a new production process throughout the glass industry. In 1952, Pilkington was the major glass manufacture in the United Kingdom, controlling 90 percent of that market with annual sales of 113 million pounds sterling. At that time, the world ’ s glass industry was oligopolistic — dominated by a few large producers, with major shares of their respective national markets. Then some of the other major glass firms in the world were as follows:

• In the United States, Libby - Owens - Ford (annual sales 187 million pounds sterling)

• In Canada, PPG Industries (annual sales of 477 million pounds sterling)

•

•

In Japan, Asahi (annual sales of 265 million pounds sterling) In France, Saint Gobian (annual sales of 436 million pounds sterling) (Layton 1972)

Up until 1952, flat glass was continuously manufactured in one of two ways. The first way was to draw glass upward as a ribbon from a bath of molten glass; this produced a low - cost, fire - polished glass. Yet it had considerable optical distortion, since the glass surface wavered as it was drawn upward. The second method was to roll cooling glass horizontally between rollers, which pressed the glass to the right thickness but left marks from the rollers. Then expensive polishing and grinding had to be done, which produced optically better glass but at a much higher cost.

Such was the state of the technology when a young Alastair Pilkington, a cousin of the company ’ s owners, went to work for Pilkington. Alastair began by working in the sheet - glass division and became familiar with the inexpensive method of glass production, drawing glass upward. Next, he became a production manager of the plate - glass division and there saw the very expensive process of grinding 170 INNOVATION AND STRATEGY

and polishing plate glass. We recall that the process of radical innovation has a series of logical steps proceeding from invention, to functional prototype, to engineering prototype, to pilot plant (for a process), to marketing and sales. This project went through these steps.

1. Invention

Alastair ’ s inventive mind began thinking about alternative ways of producing plate glass. He saw glass pulled vertically. But why not float it horizontally? This was his inventive idea! Molten glass could be floated out on a bed of molten metal.

He chose to try a hot bath of tin. Molten tin had a low enough melting point to remain molten as the glass solidified on it. Moreover, the tin could be kept free of oxide if the atmosphere was controlled. Alastair presented his idea to his superior, the production director of the flat - glass division, who took the idea to the company ’ s board. The board recognized the potentially vast economic return if the idea worked and approved a budget for an R & D project.

2. Technology Research and Functional Prototype

A small project group was created in 1952, consisting of Richard Barradell - Smith and two graduates, reporting to Alastair Pilkington. They built a small pilot plant, costing 5,000 pounds sterling, that produced 10 - inch - wide plates. Fortunately, they found that when glass cooled on the molten tin, it happened to be precisely 7 millimeters thick. (This happened due to a physical balance between the surface tensions and densities of the two immiscible liquids of molten glass and molten tin.) This was lucky because 60 percent of the flat - glass trade was then in 6 - millimeter - thick glass. By stretching the glass ribbon a little as it cooled, they could reduce the natural 7 - millimeter thickness to the desired commercial 6 - millimeters.

3. Engineering Prototype and Testing

Next they built a second pilot plant to produce 48 - inch - wide plates (this was commercial width). It cost 100,000 pounds sterling (five times more than the first 10 - inch machine). These two machines and their research took five years, until 1957, to get the machines working properly. By then, the research team thought they had perfected the production technique enough to try a production - scale plant.

4. Production Prototype and Pilot - Plant Production

In 1957, the R & D team was expanded to eight people, with three graduates from different disciplines. The group was moved into the plate - glass factory and built a plant costing 1.4 million pounds sterling (14 times the cost of the engineering prototype).

The major purpose of research is to reduce technical risk before production -

scale investment is committed, because technical problems always occur. At this plant - scale of production, the control of the atmosphere had to be improved to prevent oxidation of the tin. Next they had to solve the problem of flowing the LOWER LEVEL OF INNOVATION STRATEGY—TECHNICAL PROGRAMS 171

large quantities of glass out rapidly onto the tin. There was a problem also at this scale of stretching the glass to the right thickness and pulling the cooled glass plate off the molten tin. At each scale - up, previously solved technical problems had to be resolved, a process typical of engineering problems in production. This production - scale problem - solving phase of the development took fourteen months from 1957 through 1958, and produced 70,000 tons of unsalable glass.

Then finally, success! All the problems were solved and salable glass was produced. Again a problem occurred, but now they understood the process and solved the last technical problem, and production was ready.

5. Initial Production and Sales

In 1959, the plant went into commercial production. Another plant was built in 1962 and another converted in 1963. The total expenditure and time on the development and commercialization of the new process had cost l.9 million pounds sterling over 10 years. Pilkington had innovated a commercial process to produce plate glass, at the inexpensive cost of sheet glass. It obtained worldwide patents on the valuable process.

What business strategy should it choose for commercial exploitation over the world? All its competitors were vulnerable. The process was so radical in improved quality and lower cost that Pilkington could take away any competitor ’ s market.

Pilkington decided to license the new process to competitors in other countries, rather than trying to expand internationally. Several reasons went into the decision: the great amount of capital required for worldwide expansions, the location needs for plants, near supplies of iron - free sand and sources of soda ash, quarries for dolomite and limestone, and cheap fuel — all resources their competitors had lined up in their own companies. From 1959 to 1969, all producers purchased licenses from Pilkington.

LOWER LEVEL OF

INNOVATION STRATEGY —

TECHNICAL PROGRAMS

In an R & D laboratory, there are many projects, and a formal process for formulating, selecting, monitoring, and evaluating projects is usually established (Costello 1983).

Senior management sets research priorities and business directions. Technical managers then define project requirements around which they and technical staff generate research ideas. Technical staff draft R & D proposals that are sent to middle and senior technical management, who select which projects to fund. Periodic reviews of project portfolios by senior technical management result in selecting or terminating projects.

Projects within a program are compared on their technical merit and commercial potential according the selection criteria of the program. There has been a long history of attempts to provide quantitative models for doing this, and they have been called formal models for project selection. Periodically, there are reviews of this topic. For example, in 1994, Robert L. Schmidt and James R. Freeland reviewed progress in formal R & D project selection models (Schmidt and Freeland 1994).

172 INNOVATION AND STRATEGY

They grouped models into two approaches: decision - event or decision - process . The decision - event models focused on outcomes: Given a set of projects, the model determines the subset that maximizes an objective. But the history of actual use of these models is poor, as Schmidt and Freeland stated: “ Classical R & D project selection models have been virtually ignored by industry . . . This fact became apparent in the early 1970s ” (Schmidt and Freeland 1994, p. 190).

The decision

-

process approach focuses on facilitating the process of making project selection decisions rather than attempting to determine the decision.

Schmidt and Freeland suggest that formal project selection models should assist coordinating the decisions about selecting and monitoring a project portfolio. Other reviewers of the formal project selection literature agree. For example, Carlos Cabral - Cardoso and Roy L. Payne wrote: “ Although a large body of literature exists about the use of formal selection techniques in the selection of R & D projects . . . much skepticism exists about their value ” (Cabral - Cardoso and Payne 1996). They too argued that a judicious use of formal techniques can improve communication and discussion about projects in their selection.

But what constitutes judicious use ? All formal decision methods of R & D project selection techniques require estimation of probabilities on project characteristics such as technical risk, likelihood of commercial success, and so on. These are often impossible to quantify because of the uniqueness of innovation projects. The risk of quantitative decision aides when valid quantitative data cannot be input is the old adage of “ garbage in — garbage out. ” The danger is that formal selection methods may as often result in the wrong project selection as the right one. Also generally ignored in the project - selection - literature has been the problem of validating a project -

selection decision model. All models (decision or otherwise) need to be validated by experience or experiment before they are used for serious business decisions.

BOTTOM LEVEL OF INNOVATION STRATEGY —

TECHNICAL PROJECTS

The technical project manager is responsible for formulating and planning the project, staffing, and running the project, and transferring project results to customer. In doing this, the technical project manager needs to coordinate and integrate activities across multiple disciplinary and functional lines. In addition, all this must be performed within a finite budget and according to a firm schedule. One of the problems is that the project manager has responsibility, but with only limited authority. The project manager does not control personnel positions, has a limited budget, must “ sell ” the project to upper management and to project personnel, and also satisfy a customer. Kerzner summarized the difficulties of the role: “ The project manager ’ s job is not an easy one. [Project]

managers may have increasing responsibility, but with very little authority ” (Kerzner 1984, p. 10). Gary Gemmill and David Wilemon studied kinds of frustration felt by technical project leaders and found that the most frequent kinds were these four: 1. Dealing with apathy of team members

2. Wasting project time when the team goes back over the same issues BOTTOM LEVEL OF INNOVATION STRATEGY—TECHNICAL PROJECTS 173

3. Getting the team to confront the difficult problems 4.

Getting appropriate support for the project from the larger organization (Gemmill and Wilemon 1994)

Richard Beltramini examined a set of product development projects and concluded that many factors were contributory to the success of a project team, among which were (1) how the team was organized, (2) how information was handled, (3) the timing of project and its performance, (4) training of the participants, and (5) resource availability (Beltramini 1996).

There are many software tools for project management, particularly for the time and budget and resource aspects of project management, such as project scheduling software. Also embodied in some software are techniques for determining priority of tasks and risks of completion, such as Program Evaluation and Review Technique (PERT). Other forms of software, such as groupware , facilitate the communication and recording of project documentation. In addition to the technical stages of an innovation project, several management phases occur in any project. Technical projects need to be planned and completed, and technical knowledge attained must be transferred for implementation in products/production/services/software.

There are three types of technical projects — research, design, and operations. Each has different technical stages for their type of project. Research - based projects are usually called R & D projects . Design - based projects are usually called engineering projects . Operations - based projects are usually called systems projects . R & D projects innovate new technologies. Engineering projects reapply existing technologies in product model design and sometimes add in new technologies. Systems projects develop new or improved operations, often as software. Each of these types go through different stages. The three types of projects are all important in R & D management because technological invention may begin as an R & D project, then move into an engineering project for development and design of a new product or into a systems project for the improvement of operations or development of a new service.

In innovating new technology, project management techniques should be adapted to technical risks. For example, Aaron Shenhar has emphasized identifying the degree of technical risk in a project:

“

The technology used in projects is one

of the main aspects which should receive special attention, since there are great differences among projects. Some projects use well - established technologies, while others employ new and sometimes even immature ones, and involve enormous uncertainties and risks

”

(Shenhar 1988, p. 2). Shenhar suggested classifying a development project by the degree of risk in the technology being implemented:

• Low - tech projects

• Medium - tech projects

• High - tech projects

• Super - high - tech projects

A low - tech project is a project in which no new technology is used. A medium -

tech project is a project in which some new technology is used within an existing 174 INNOVATION AND STRATEGY

system but is relatively a minor change in the whole technologies of the system. A high - tech project is a project that is using only key technologies as components but in which the integration of these technologies is a first - time achievement. A super -

high - tech project is a project in which new key technologies must be developed and proved along with their integration into a new first - time system.

For low - tech projects , project planning should use the experience of prior projects of the same type for estimating scheduling, budgeting, staffing, and for arranging for completion and transfer. Here a PERT chart is very useful, wherein past experience can be used to estimate probabilities of risk within the chart.

For a medium - tech project, it is important to determine how much competitive advantage the new technology provides in the project: nonessential or essential. If nonessential, then a substituting technology should be within the scope of the design and ready if substantial problems develop with the new technology. If essential, it is important to first plan and schedule the development and testing of the new technology before system integration is far advanced. The scheduling of system development should not be begun until confidence is demonstrated that the new technology will perform properly and on time, for the whole project schedule. Here, probabilities in a PERT chart should only be used for established technologies and not for the new technology.

For a high - tech project, it is important to plan and schedule the system integration in a modular, subsystem fashion, so that subsystems can be thoroughly tested and debugged before final system integration and assembly occurs. For a super high - tech project, the planning of the project should be in the form of a series of “ go/no - go ” decisions. Each stage of the project development should consist first of developing and testing a key technology before the full project system integration program is launched. Whenever a key technology fails, a no - go decision should be made to keep the full - integration program on hold until either the key technology can be made to work or until an adequate substituting technology can be implemented.

KEY IDEAS

In the case study of:

• 3M

• AOL and Time Warner

• Cymer Inc.

• Pilkington ’ s float - glass R & D project We have examined the key ideas of:

• Four levels of inn ovation strategy

• Top - down and bottom - up strategy

• Top - level acquisition and innovation strategies

• Middle - level new high - tech business ventures

• Lower - level technical program management

• Bottom - level technical project management II.

TECHNOLOGY

STRATEGY—

TECHNICAL BASE

OF INNOVATION

9

INTEGRATING TECHNOLOGY

AND BUSINESS STRATEGY

INTRODUCTION

We have seen how new products/services are developed in the cyclic product development process of a business. The future products and their production processes focus the technology strategies for future business competitiveness. The integration of technology strategy with business strategy thus occurs in the planning of a business ’ s future product lines. The first strategy problem of technology/business strategy is to identify which technologies are relevant to a business, currently and in the future — and their potential for technical progress. We address the following questions:

• What technologies are relevant to the business?

• Which of these technologies are changing?

• What change in these technologies could provide a competitive advantage?

•

Are there potentially substituting technologies that could make these technologies obsolete?

In Figure 9.1 , when technology and business strategy is integrated, this first requires locating the technological progress relevant to the future of a business.

177

178 INTEGRATING TECHNOLOGY AND BUSINESS STRATEGY

Technology and Business Strategy

Research

Invent

Commercialize

Market

Nature

Science

Technology

Economy

Utility

Exist

Discover

Manipulate

Embed

Use

naturally

nature

nature

nature

nature

The world as material

The world as money

Figure 9.1 Innovation process

CASE STUDY:

Innovation in the Steel Industry

The range of technologies relevant to a business depends on the industrial sector in which a business operates. As an illustration, we will review the history of technical progress in the steel industry. The modern age of steel began in the 1700s with the invention of a new basic iron production - process, the Bessemer process.

Today, all large structures depend up internal steel frameworks and reinforcement, as well as do railroads and ships and military equipment — all benefiting from technological change in steel production for the last two hundred years. Bela Gold, William Pierce, Gerhard Rosegger and Mark Perlman have described the long - term technical progress in steel production (Gold et al. 1984).

Some 3,500 years ago was the beginning of the Iron Age, whenthe technology of iron production using charcoal (as partially burned wood) to melt iron ore was invented. But why a charcoal fire was better than a wood fire for melting iron ore was not understood by ancient iron makers. They knew the how but not the why (technology without science). Next, iron was partly transformed into steel by hammering the iron into desired shapes (such as a sword) by reheating iron in smoky braziers and recurrently hammering the hot iron on anvils. This reheating in the smoke of the braziers could produce steel surfaces on the hammered iron.

Again the ancients knew how but not why.

Now with the science of chemistry, we know that burning wood to melt iron ore allows impurities from the wood fire to contaminate the melted iron, making the iron brittle when cooled. We know now that the difference between iron (which is relatively soft and brittle) and steel (which is hard and tough) is a few percent of carbon atoms (about 12 percent) mixed in with the iron atoms. When the ancient steel makers beat iron into shapes (such as swords) in a smoky charcoal fire, they beat carbon atoms from the smoke into the surface of the iron shapes, coating the iron with steel. It was from that new science of chemistry (emerging in the 1700s and early 1800s) that Henry Bessemer understood the difference between a wood fire versus a charcoal fire for iron production.

INDUSTRIAL VALUE CHAIN 179

Henry Bessemer (1813 – 1898) was born in London and desired to be an inventor of technology. At the age of twenty - one, he moved to Paris and gained employment at the Paris Mint. He invented improvements to steel die making and to the optical telescope — for which he was elected a member of the French Academy of Science. After the February Revolution of 1848 in France, Bessemer returned to England.

Bessemer ’ s inventive idea was to partially burn coal into “ coke ” to get the same result — ridding the coke of chemical impurities so it could be used to melt iron ore. Since coal was so much more abundant in England than wood, the Bessemer process of coke - fueled iron production would revolutionize the iron industry. In 1851, he filed for a patent for the mass production of steel, the Bessemer process, using the blowing of air into the molten steel for remove impurities through oxidation, fueled by coal coke. And the age of steel began.

Abundant and inexpensive steel makes possible steel bridges and steel railroads and steel steam engines. Bessemer ’ s technical process was one of the three basic inventions that drove the Industrial Revolution in England in the 1800s — with the other two basic inventions being the steam engine and textile machinery.

Henry Bessemer

(http://en.wikipedia.org 2009)

INDUSTRIAL VALUE CHAIN

The idea that technologies organize an economy into industrial sectors through value -

transformations has been called an industrial value chain . The term was popularized in the 1980s by Michael Porter (Porter 1985) and later elaborated on by Lowell Steele (Steele 1989). The general form of an industrial value chain is sketched in Figure 9.2 .

An industrial value chain begins with a resource - acquisition industrial sector.

The next sector is a materials - processing sector. Resource - acquisition and materials -

processing sectors organize separately because the technologies and investments and 180 INTEGRATING TECHNOLOGY AND BUSINESS STRATEGY

Nature

Resources

extraction sector

Materials refining synthe-

sis and shaping sector

Tools and

Information

equipment

Part and

technologies

sector

components sector

sector

Major device

manufacturer sector

Service

Retail

sector

sector

Customer

Figure 9.2 Industrial value chain

operations differ markedly between acquisition and processing operations. Materials -

processing sectors supply materials to part - producing sectors. For example, the automobile industry is supplied by many different kinds of part - producing firms, such as tires, wheels, brakes, batteries, electronics, and so on.

The next industrial sector is a major - device fabricator sector. For example, in transportation applications, key major - devices are automobiles for land, airplanes for air, and ships for sea. In addition, within a major - device sector, sub industrial sectors are frequently organized. For example, the automobile industry divides into cars, trucks, tractors, motorcycles, tanks, etc. The airplane industry divides into military and commercial sectors. Sometimes major - devices are retailed directly to customers (such as cars) or sometimes integrated into a service - providing industry (such as airlines).

Major - device industries or service industries often retail through a distribution system. For example, in the case of cars, there is an automobile - dealer industry for sales and service. In the case of airlines, tickets may be sold directly by airlines or through a travel - agent industry.

Finally, the customer is served by the products and services created in the industrial value chain. These customers may be consumers or business firms (involved themselves in different industrial value chains) or government. After purchase of the major device, the customer will continue to purchase supplies for the device and maintenance and repair services.

INDUSTRIAL VALUE CHAIN 181

All along the chain sectors of tool and equipment manufacturers and computer and communications manufacturers provide equipment to the different businesses in the different sectors of an industrial value chain.

An industrial value chain describes the industrial structures in an economy —

connecting industrial sectors that transform resources from nature into products for customers.

When a new technology creates a new functionality, an industrial structure will grow to supply products to the customer embodying the new functionality. The concept of industrial structure provides a way to analyze the economic leverag-ing of the new technology. Not only are new firms established to produce the new technology, but also new firms are established to provide supplies and parts to that firm. New firms may also be established to distribute and service the new product.

Thus, the total market creation of a new - functionality technology goes far beyond the market itself for the final product. Also an industrial value chain can be used to identify all the kinds of technologies necessary to an industrial value chain in order to satisfy customer needs derived from the resources provided by nature. Moreover, the science bases relevant to industrial value chains can then be traced from these technologies.

CASE STUDY (CONTINUED):

Innovation in the Steel Industry

The technologies relevant to the industrial value chain for steel production begin with mining and then move to smelting.

Mining T

echnology for Coal

Iron ore must first be found and mined as the material input to steel production. Coal mining technologies divide basically into (1) underground mining and (2) surface strip mining. Underground mining technology was historically an early process, and underground mining of any ore was restricted to depths no deeper than the water table. In the eighteenth century, the innovation of the steam-engine-driven water pump by Neucommin in the eighteenth century allowed mining to go deeper than the water table. In the nineteenth century, the innovation of dynamite by Alfred Nobel allowed miners to fragment rock and ore more efficiently.

From then until early in the twentieth century, the sociotechnical system of coal mining required coal miners to descend on foot (or in lifts) into the depths of the earth and blast their way in tunnels to coal seams, then pick and blast coal into chunks for loading into cars on rails, pulled by men (or mules or electrical trains) to 182 INTEGRATING TECHNOLOGY AND BUSINESS STRATEGY

the surface. Innovations in the coal mining technology included drills to fragment the ore, hoists and conveyors to lift the ore, and machines to rip the face of the ore.

Later innovations in other industries (notably the construction machinery industry) made possible an alternate technology of open-strip mining. Open-strip mining involves the removal of surface soil with large tractors and shovels and then the systematic removal of ore. This process reshapes the landscape, leveling and terracing hills and digging large holes in the earth and pollution of streams. The landscape after strip mining requires extensive reconstruction for environmental recovery. By 1963, the ratio of ore output to labor was a factor of over four times higher for open pit mines to underground mines (Gold et al. 1984, p. 339).

Coking and Ore Beneficiation Technologies

In modern steel processes, both coal and iron ore receive a processing step before going directly into iron refining. For coal, this is coking—a partial burning to rid coal of impurities like sulfur. For iron ore, this is beneficiation (processes for fragmenting ore to smaller particle size and concentrating their iron content and agglomerating these concentrated particles into ore pellets).

The early coking process was called beehive coking and occurred at the mine head. In 1887, a new innovation, the Koppers oven, was used to capture the waste gases. Koppers ovens were boxlike structures in which the coal was partially burned to coke. Vent pipes captured the coal gas and delivered it to recovery plants for electrical production and for chemical production use. (Later, petroleum and natural gas would be substituted for coal gas as chemical feedstocks.) Sometimes a stimulus for technological innovation in one part of an industrial value chain occurs when other industrial value chains find uses for the byproducts of the first value chain.

Iron ore beneficiation made possible the use of ores from different sources in the same refining process and reduced the cost of transporting iron ore from the source to the refiner. Innovations in concentration included washing, magnetic concentration, gravity and froth methods.

Sometimes innovations both in the technologies of processing natural resources and in the technologies of using natural resources are both required in order for a new form of a natural resource to find economic utility.

Ore Transportation Technology

In iron-and-steel-making, the transportation of iron ore from the mines to the furnaces required loading the ore on railroad cars and then into ore ships. Incremental innovations in the transport ships focused on loading and unloading procedures.

INDUSTRIAL VALUE CHAIN 183

Unloading equipment was built into the ship, as hoppers emptying onto conveyor belts. This technology was particularly useful for loading and unloading benefici-ated and pelletized iron ore.

In the acquisition and transportation of natural resources, the quality and location of the resources provide technical challenges and stimulate technical change. Once a technical solution has been found (and until better solutions are invented), the technological system will be stable.

As the quality and quantity of natural resources decline, technological innovation will be again required.

Iron Refining Technology

We look next at innovation in the iron- and steel-making firms of the value chain.

Steel making is a two-step process. First, iron is refined from iron ore and/or scrap, and then steel is made from the iron. In both processes, the core technology is the furnace. In iron making, the technology of the blast furnace provided the principle transformation of iron ore to pig iron, consisting of the following seven areas: 1. Raw materials handling and storage facilities

2. Equipment for moving materials into the furnace 3. The blast furnace

4. Facilities for heating and blowing air into the furnace 5. Equipment for drawing the molten iron and slag from the furnace 6. Equipment for removing waste gas from the furnace top and reducing emissions

7. Cooling equipment requiring large quantities of water The blast furnace as a basic technology was unchanged for nearly two hundred years, but the details of the blast furnace plant system was continuously improved by incremental innovations. From 1890 to 1960, output per person-hour increased six times. Performance improvements came from the incremental innovations and from economies of scale in production capacity. Many of the incremental innovations in the blast furnace system centered on improving the control of the operation.

Control of the furnace system is very important for both productivity and safety. For example, a stoppage of the air blast to the furnace immediately stops the operation. When the furnace is shut down, it will be out of production for a long period. If there is a delay in drawing off the molten metal or slag, the materials may pile up beyond the melting zone of the furnace, seriously damag-ing the furnace.

Innovations were also made in other parts of the furnace technology system.

One important example was as the mechanization of materials-handling and charging of the furnace with materials. At first, raw materials were shoveled by 184 INTEGRATING TECHNOLOGY AND BUSINESS STRATEGY

manual labor into wheelbarrows and taken to the top of the platform and manually dumped into a hopper (which then discharged into the furnace). The innovation of a skip-hoist next conveyed materials directly from the dock to the furnace. This mechanization of operations standardized the composition of the charge into the furnace, permitting better quality control.

Another part of the furnace technology system is the means of removing the iron from the furnace. Hot iron is tapped and removed from the top of the furnace, and innovations in drilling through the clay plug in the tap hole (an automatic, high-speed drill) and in removal of final product also improved performance.

These innovations in the mechanization of the loading and removing material from the furnace permitted the enlargement of the size of the furnace for economies of scale. The scaling up of the size of furnaces provided major gains in profitability by improving energy efficiency and decreasing labor requirements. Furnace diameters increased from 13 feet in 1900 to over 30 feet by 1935.

Improvements in many parts of a technology system may also make possible economic benefits from economies of scale.

After iron is produced from iron ore, it must next be transformed into steel.

The carbon content of iron must be adjusted into the right range to become the stronger structure of steel. Hot iron from the blast furnaces, along with cold iron from scrap and alloying metals and fluxes, are put into the steel-making furnace.

The steel-making furnace transforms these into steel. The quality of the steel varies according to the proportions of the materials put into the furnace and the details of the process of transformation within the furnace. In steel making from 1798

to 1970, there were three different basic technologies: the Bessemer furnace, the open-hearth furnace, and the basic oxygen process furnace.

Prior to the middle of the nineteenth century, steel had been made in crucibles.

Henry Bessemer invented a new process. The basic equipment was a large pear-shaped vessel that could be tilted along a horizontal axis. Hot iron was placed into the vessel and air was blown into it through tuyeres in the bottom of the vessel.

This blowing of air into the iron was the inventive idea that distinguished the new technology from the older crucible method. It provided the first technique for the mass production of steel to specific metallurgical standards.

Although the Bessemer furnace was long used, after a hundred years it began to be displaced by another furnace—the open hearth, first proposed by C. W. Siemens in 1861. Pierre Martin and Emil Martin obtained a license and began production in 1864. The open hearth used heat in the waste gases from the furnace to preheat air and gas fuels and to increase the temperature. This enabled it to use inputs of scrap and other cold metal in addition to the molten iron. It consisted of a rectangular container and two sets of brick chambers through which the waste gases were alternately blown in order to heat them. The air and fuels are blown into the hearth through the preheated chambers. Steel produced in the open hearth was uniformly stronger, and higher-quality, and maintenance was easier.

INDUSTRIAL VALUE CHAIN 185

There also came a third competing basic furnace, the electric furnace. The electric furnace was actually thought of as early as 1889, but the high cost electricity at that time made it not commercially practical. Later the electric furnace was used for production of calcium carbide, aluminum, and precious metals. During and after the Second World War, it began to be used for steel making—taking advantage of the availability of steel scrap.

In the 1920s, another technological innovation in another industry provided increased supplies of oxygen and decreased its price, the Linde-Frankl process.

Injecting oxygen with air into the open hearth improved the steel-making process.

However, it was not until the mid-1950s that the new process began replacing the older processes. Experimentation to learn to control the process began in Europe in the late 1940s. Problems involving rapid destruction of the furnace linings had to be solved, by developing a process to blow the oxygen onto the center surface of the hot metal bath to avoid contact of the oxygen with the furnace linings.

In an industrial value chain, technical progress can occur not only incremental in basic processes in parts of the chain but also as discontinuous, new basic processes.

Finally, after molten iron has been converted to molten steel, the next transformation step is to form the steel into its product forms, which include girders, rail, pipe, bars, wire, and sheet. There are alternate processing paths for raw steel into steel product. First, a blooming mill or a slabbing mill shapes the steel into blooms, billets, or slabs. From these, a hot rolling process produces the final shapes:

• From blooms are produced structural shapes, and rails and tube rounds are formed (with the tube rounds subsequently shaped into seamless pipe).

• From billets are produced bars and wire rods, which may then be further shaped into cold-drawn rods (by cold drawing) or wire (in a wire mill).

•

From slabs are produced sheet and strip-steel or plates or skelp (which when further processed in a pipe mill may become welded pipe or tubes).

The basic technologies for shaping of produced steel are rolling processes, wire-drawing processes, or die and tube extrusion processes. These basic technologies have not changed, but many incremental improvements have provided significant progress in them. These improvements included size of the equipment, power sources, control, and sensing.

In the United States, the total market growth for steel products ceased after 1950, due to the substitution of other materials such as non-ferrous metals, plastics and composites.

New substituting technologies may make obsolete a given technology for a given application.

186 INTEGRATING TECHNOLOGY AND BUSINESS STRATEGY

TECHNOLOGY STRATEGY

The list of industrial sectors and technical knowledge is a long one, and technical strategies for innovation must be conceived in technical detail. Technical opportunities occur at a level of much detail and complication — in different sectors of different industries, and in different aspects of technologies in industrial sectors.

We will now look what kinds of technologies are relevant to the different sectors.

Technology improvement in a given sector depends on the kind of research performed in an industry. Such research effort is now called industrial research and development (R & D).

Technologies in the Resource - Extraction Sector The extraction industry firms provide raw materials to their customers, and the availability and quality and cost of these materials influence sales to customers.

For resource - extraction industries, the most important knowledge assets are access to sources of raw materials and technologies for extraction of raw materials. Five competitive factors especially discriminate among firms in this sector: 1. The effectiveness of resource discovery techniques 2. The magnitude and quality of their discovered resources 3. The efficiency of the extraction technologies 4. The capacity for extraction and utilization of capacity 5. The cost and efficiency of transportation for moving resources from extraction to refinement

Innovative firms in the resource extraction sector thus focus their R & D upon improving three techniques:

1. Techniques for resource deposits discovery 2. Techniques for

resource extraction

3. Techniques for transportation or modifying the resource for transportation Technologies in the Materials Refining/Synthesis/Shaping Sector In the materials refining sector of an industrial value chain, materials products are produced from the raw materials. The materials products are processed and shaped into materials products that can be used in parts manufacture or fabrication. For this sector, the most important knowledge assets are plant and synthesis/processing technologies and new - application technologies. Competitive factors for firms in this sector include the following:

• Patents and proprietary knowledge about the creation of materials products

• Patents and proprietary knowledge about the processes and control in production processes

TECHNOLOGY STRATEGY 187

• Quality of produced materials

• Cost of produced materials

• Timely delivery of produced materials

• Assisting customers in developing applications for material products Firms thus focus their R & D on five areas:

1. The invention of new materials products 2. Modification and variation of materials products 3.

Invention of new unit production processes and for producing materials products

4. The improvement and integration of unit production processes 5. The development of new applications for materials products Technologies in the Parts/Components/Subsystems Sector In the parts, components, and subsystems industry, the parts and components or subsystem assemblies are manufactured and fabricated for the producers of major device systems. The parts and components industries constitute the greatest numbers of firms in the manufacturing sectors of industry and the greatest diversity. Generally, they divide into electronics production and mechanical production. Electronic parts suppliers include IC chip manufacturers and other electronic parts, printed circuit board manufacturers and assemblers, and electronic subsystem designers and assemblers. Mechanical parts suppliers include mold and die makers, fabricators, and subsystem designer and assemblers.

For the parts, components, and subsystems industry, the most important knowledge assets are proprietary knowledge of part design, production control, and unique equipment for part production. Competitive factors for these firms include the following:

•

•

Patents and proprietary knowledge about the design of products and components Patents and proprietary knowledge about the processes and control in production processes

• Quality of produced materials

• Cost of produced materials

• Timely delivery of produced materials

• Assisting customers in developing applications for material products

• Concurrent design capability with customers

Firms thus focus their R & D on four areas:

1. Invention and design of improved parts and components and subsystems 2. Variation and redesign of lines and generations of parts and components and subsystems

188 INTEGRATING TECHNOLOGY AND BUSINESS STRATEGY

3. Invention of new production processes of parts, components, subsystems 4. Improvement and integration of unit - production processes Technologies in the Major - Device Sector Firms in the major - device systems integrator industry include the automobile manufacturers, and also the airplane manufacturers, ship builders, building construction industries, weapon systems manufacturers, computer manufacturers, and so on. Businesses in the major - device sector are the major product - system integrators — utilizing parts and components and subsystems from suppliers in the chain. Such firms sometimes vertically integrate into parts and materials to gain cost and competitive advantages and sometimes do not. Vertical integration or vertical deintegration depends on costs of capital, place of technology change, and management capability. The most important knowledge assets in the major - device sector are: proprietary knowledge of design and production control, production facilities, and brand name and access to market distribution channels. Competitive factors for these firms include the following:

• Patents and proprietary knowledge about the design of the major - device system and about key competitive components and subsystems

• Patents and proprietary knowledge about the processes and control in production processes

• Performance and features of major - device system

• Costs of purchase and maintenance of major - device system

• Dependability and cost of repair of major - device system

• Availability and cost of distribution channels and timely delivery

• Availability and nature of peripherals to complete a major - device system for an application

•

Assisting customers in developing applications systems around the major device system

Firms thus focus their R & D on the five areas: 1.

Modification, variation, and redesign of lines and generations of major device - systems

2. Modification, variation, and redesign of key competitive components and subsystems

3. Invention of new production processes and for producing parts 4. The improvement and integration of unit production processes 5.

Assisting customers in developing applications systems around a major

-

device system

TECHNOLOGY STRATEGY 189

Technologies in the Equipment Sector

In the equipment industrial sector, equipment is produced that is essential for production processes and capability — for the other producing sectors of an industrial value chain. Competition between firms in the equipment industry focuses on performance capability and capacity of the equipment, price, and production - system integration. The most important knowledge assets and competitive factors for these firms include the following:

• Patents and proprietary knowledge about the design of the production equipment and tools

• Proprietary knowledge about control in production processes

• Performance and features of equipment and tools

• Costs of purchase and maintenance of equipment and tools

• Dependability and cost of repair of equipment and tools

• Availability and cost of distribution channels and timely delivery

• Availability of peripherals to complete production equipment into a production process

• Assisting customers in automating and controlling production processes Firms thus focus their R & D on five areas:

1. Variation and redesign of lines and generations of production equipment and tools

2. Understanding and control of industrial processes for which a piece of equipment is central

3. Development of specialized equipment customized to a customer ’ s production process

4. Development of software for planning, scheduling, and controlling production processes

5.

Development of software for generic product design and integration with production control

Technologies in the Information - Technology Sector In the information technology industrial sector, businesses provide (1) computer equipment, (2) software, and (3) outsourcing of business services. Competition between firms in this sector focuses on technical performance, integration, system completeness, and price. The most important knowledge assets and competitive factors for these firms include the following:

• Patents and proprietary knowledge about the design of computers, software, networks

190 INTEGRATING TECHNOLOGY AND BUSINESS STRATEGY

• Proprietary knowledge about network integration

• Performance and features of computers, software, and network equipment

• Costs of purchase and maintenance of equipment

• Dependability and cost of repair of equipment and lack of bugs in software

• Availability and cost and responsiveness of services

• Availability and nature of peripherals to complete information and communication systems

• Assisting customers in automating and controlling production processes

• Provision of outsources services for business processes Firms thus focus their R & D on three areas:

1. Modification, variation and redesign of lines and generations of equipment and software

2. Understanding and control of network architectures and processes 3. Effective software and center services for business outsourcing Technologies in the Retail and Service Sectors Firms in the retail and service industries divide principally into retail firms and service delivery firms. Advances in service technologies of logistics, computerized information, and communications have greatly impacted competitive capability.

Retail firms are either physical or virtual or a combination of both. WalMart principally has physical retail stores, while Amazon is Internet. Competition among and between retail and wholesale firms focuses on location, Internet access, prices, brand product lines, and customer service. The most important knowledge assets and competitive factors for firms in the retail service industry include the following:

• Location

• Brand franchises

• Inventory control and logistics capability

• Price - sensitive advertising

• After - sales service

• Point - of - sale information systems

• Internet communication with customers

Retail thus firms focus R & D on three areas:

1. Development of customer service applications 2. Development of logistics and scheduling technologies 3. Development of Internet communication with customers TECHNOLOGY STRATEGY 191

Service firms include airlines, bus and railroad lines, transportation firms, telephone and communication firms, for example. Information and control integration firms include engineering design firms, information integration firms, and so on. The major professional - services firms are medical and legal businesses and engineering - consultant businesses. The application system for a customer is designed and/or acquired and control for the system developed. Competitive factors for firms in the applications - systems - integrator industries include the following:

• Proprietary tools for systems design

• Patents and proprietary interconnect hardware and software

• Patents and proprietary sensing and control hardware and software

• Professional expertise

Service firms thus focus their R & D on five areas: 1. Tools to improve applications system design capability 2. Interconnect hardware and software

3. Sensing and control hardware and software 4. Tools to improve consulting services capability 5. Tools to improve logistics and scheduling CASE STUDY:

Samsung Electronics

Our illustration of the iron/steel industry ended its value chain in the materials refining and shaping sector. Accordingly, for an illustration of technological innovation of the parts and components industrial sectors, we need look to a different industrial value chain. We can look at computers and particularly to Samsung Electronics. Laptop computers and their parts supply provide a good illustration of the technical progress in parts and industrial value chain.

The decreasing size of computer components and increasing power made possible several product lines of computers, the smallest being the laptop computer.

To make laptop computers possible, technical progress in computer parts required three things:

1. Liquid crystal displays

2. Miniaturization of disk drives

3. Low-powered IC chips

The first laptop computer was marketed by Tandy as the model 100 and was designed and produced by Kyocera, without a disk drive and weighing 3 pounds. Next 192 INTEGRATING TECHNOLOGY AND BUSINESS STRATEGY

a new company grid produced a laptop with disk drives in 1982, weighing 10 pounds.

In 1986, Zenith won a government contract to supply 15,000 laptop computers to the Internal Revenue Service. In the same year, IBM introduced a 12-pound laptop (which was unsuccessful and was withdrawn in 1989) and Toshiba introduced a 10-pound laptop (which was soon redesigned into a 7 pound laptop that became successful).

By 1990, the laptop market had grown to 832,000 units annually, over two and half million by 1993, and onward. The U.S. firms of Intel in computing chips (CPU) and Microsoft in operating system software dominated personal computers. Japanese firms attained about 40 percent of the total world market in laptop computers. Next on the scene came new competitive parts suppliers from South Korea and China.

In 2001, the South Korean company of Samsung Electronics made significant progress in becoming a global parts supplier and in moving up the personal computer industrial value chain: “Last summer [2001] Chin Dae Je, the head of Samsung Electronic’s digital media division, sent a laptop to Michael Dell. The two men had never met, but Samsung had recently signed a multi-year $16 billion deal with Dell computer to provide components to the Texas PC maker. The laptop—thinner and lighter than a Sony Vario—made an impression on Dell. . . . Now Samsung is making a similar model that will soon be sold in the U.S. under the Dell label. And Dell himself travels with a Samsung-made laptop” (Holstein 2002, p. 89).

The ambition of Samsung Electronics had been to become a global laptop competitor and not merely a parts supplier. Earlier, Samsung had bought chips from its competitors and were not getting the cutting edge of technology. Chin commented: “Five years ago we had to buy chips from Sony or Matsushita, so we were always behind. Now we can be number one. There’s no doubt in my mind” (Holstein 2002, p. 89).

The challenge of firms in the parts supply industry is to be both technically competitive and profitable. In 1970 Samsung Electronics had begun making inexpensive, small black-and-white televisions sold by the Japanese firm Sanyo. Over the years, Samsung developed research capability. In 2001, Samsung Electronics ranked fifth in the world in patents. Samsung also became profitable that year, selling $24.7 billion in products with a profit of $2.2 billion. It manufactured products in fourteen countries and generated 70 percent of its revenues on foreign sales. The case of Samsung Electronics illustrates a vertical integration upward from a parts supplier toward a major device producer.

TECHNOLOGY AND APPLICATION SYSTEMS

Industries are organized in a chain toward creating an ultimate utility of a technology — product/service/process. This utility is realized in an

application

of value to a customer. Technological progress is commercially innovated in the high - tech products/services that provide new functional capabilities to customers —

functional value - added products/services. What is the system of application in which the customer uses a high - tech product or service? We can call this a customer application system . It is an ordered set of useful tasks having functional value to the TECHNOLOGY AND APPLICATION SYSTEMS 193

customer. Accordingly, it is important to distinguish between three kinds of systems relevant to technological progress:

1. Technology systems

2. High - tech product systems

3. Customer application systems

It is the application system for a technological innovation wherein lies economic value . The purpose to which a technology is put is called its application . We recall that the personal computer industry really grew by the innovation of IBM ’ s 16 - bit chip upon which two applications could run (1) word processing software (e.g., Letter Perfect) and (2) an integrated database spreadsheet (e.g., Lotus).

The ability to do something for that application is called its functional capability .

How well it does that ability for the application is called its performance . Until the performance of a new technology is sufficient for a customer ’ s application, the technology is not yet ripe. The critical performance measure of a technology is the minimum performance necessary for the technology to do an application for a customer. We should think about technological progress in terms of function, customer, product, application, and critical performance. As technologies occur in different system configurations, the selection of the system configuration depends on how the technology is used, its application.

As shown in Figure 9.3 in the Venn diagram representation of a national innovation system, the product connects a business to a customer for performing tasks National innovation system

Price

Nature

University

Government

Value

reasearch

research

Industrial

Industrial

research and

value-adding

development

structures

Research in

transforming

nature to utility

Market

Business

Customer

Product

Task

Application

system

Figure 9.3 National innovation system for transforming nature to utility 194 INTEGRATING TECHNOLOGY AND BUSINESS STRATEGY

in the customer ’ s application system. We further saw that the value to the business of the product was the price of the product, whereas the value to the customer of a product was its performance of tasks in an application system.

CASE STUDY:

Motorola’s Technology Roadmaps

A formal process for integrating technology strategy with business strategy was used by Motorola in the 1990s, which they called a technology roadmap. This term became popular and widely used thereafter. Willard and McLees described their process: Motorola is a technology-based company and has, for each of its many businesses, a strategic plan which is often based on the anticipated advance-ments that will be made in certain technologies. Our products involve the application of scientific principles, directed by our people, to solve the problems of our customers, to improve their productivity, or to let them do things they could not do otherwise (Willard and McClees 1987, p. 13).

Motorola’s systematic approach to technology and product planning arose from the growing technological complexity of their products and processes: “Because our products and processes were becoming much more complex over the years, we realized there was the danger that we could neglect some important element of technology. This potential danger gave rise to corporate-wide processes we call the ‘Technology Roadmaps’” (Willard and McClees 1987, p. 13).

The formal outputs of the process were documents which recorded the information generated in the process: “The purpose of these documents is to encourage our business managers to give proper attention to their technological future, as well as to provide them a vehicle with which to organize their forecasting process” (Willard and McClees 1987, p. 13).

Their technology roadmaps facilitated the orientation and internal communication among managers in engineering, manufacturing, and marketing. Together they could track the evolution and anticipations in their technology/product/market planning. Their purpose in the roadmap process was threefold: 1. Forecast the progress of technology.

2. Obtain an objective evaluation of Motorola’s technology capabilities.

3. Compare Motorola’s capabilities to competitors, currently and in the future.

Each of Motorola’s technology-roadmap documents was composed of eight sections:

1. Description of Business;

2.

Technology

Forecast;

3. Technology Roadmap Matrix;

4.

Quality;

TECHNOLOGY AND APPLICATION SYSTEMS 195

5. Allocation of Resources;

6.

Patent

Portfolio;

7. Product Descriptions, Status Reports & Summary Charts; 8. Minority Report (Willard and McClees 1987)

The “Description of Business” section included:

• Business mission

• Strategies

• Market share

• Sales history and forecast

• Product-lifecycle curves

• Product plan

• Production-experience curve

• Competition

The business mission statement provided an answer to the question of: “What is expected for the business to achieve greater success?” The strategies part summarized the timing and application of resources to a product line to generate a competence distinctive from competition. The market share part summarized the shares of worldwide markets by Motorola and major competitors and trends of change. The sales history and forecast part displayed forecasts based on strategic assumptions. The product-lifecycle curves expressed the anticipated length of time from conception of a product through development to market introduction, growth, decline, and withdrawal from the market. (They used the product-lifecycle histories from past products upon which to base educated guesses about current or planned products.) The product plan part summarized the milestones for product development and introduction and redesign. The production-experience curve projected the planned rate-of-decline-of-production-costs over the volumes and lifetime of the product. The

competition part emphasized understanding competitors’ current strengths and weaknesses and directions of their technological development.

(Motorola summarized that information in a competitor/technology matrix.) For technology forecasts, Motorola used the technology S-curve format. Motorola summarized its product plan as sets of milestones and product characteristics.

In a 1989 interview with George Fisher, then CEO of Motorola, Fisher discussed Motorola’s technology planning process:

The basics of the business are not all that complex, although the technical details are quite demanding. In portable equipment . . . there are four critical forces: size, weight, current drain (driving function of the size and weight of the battery), and cost. So in our formal technology planning, for particular products, we develop five-year and ten-year technology roadmaps of those forces (Avishai and Taylor 1989, p 110).

A technology roadmap is useful for incremental innovations but not for radical innovation.

196 INTEGRATING TECHNOLOGY AND BUSINESS STRATEGY

PRODUCT - LINE PLANNING

As we noted, the integration of technology strategy with business strategy occurs in the planning of a business ’ s future product lines. Business strategy thus focuses technology progress into future products — product - line planning . A product - line is an assortment of product models and services that can be applied by different groups of customers in market for the business. Steven Wheelwight and W. Earl Sasser suggested mapping out the evolution of product models of a line of products (Wheelwright and Sasser 1989). They pointed out that product models evolve from a core product, such as one can branch enhanced models for a higher price line or strip a model of some features off a price - reduced line. Also, they argued that from the core product, one should plan its evolution in generations of products. The core product system will express the generic technology system, and higher - or lower - priced versions will differ in the subsidiary technologies of features. Next - generation products differ in dramatically improved performance or new features, or improved technologies in features or dramatically reduced cost. We can diagram the planning of a line of product models as branched product models and as generations of products. Wheelwright and Sasser called this a New Product Development Map . Such a map is particularly useful for the long - term product development process, when the anticipated technical goals for product performance and features are listed in the product boxes.

Product - family planning is especially important to deal with competitive conditions of shortened product life cycles in rapidly changing technologies — radical innovation. Short product life cycles can decrease profits. For example, Christoph -

Friedrich von Braun charted shortening product lifetimes from 1975 to 1985 in Siemens with an average of 25 percent shortening (von Braun 1990) and (von Braun 1991). With shorter lifetimes usually comes smaller cumulative product volume sold, and hence less return on investment from the R & D to introduce the product.

This makes the idea of the product family important by extending product lifetimes over a variety of product models and product model improvements. The key to efficiently designing and producing a product family is to develop common technology platforms for the core technologies of the product. Common product platforms that can be modularized can efficiently be varied to adapt the product line to different market niches.

A technology roadmap is a technique for expressing incremnental technology strategy within a future business strategy, a way to express the interaction of technology progress and new product planning. It is set of documents summarizing technology plans, product plans, and manufacturing plans of a business. Those documents include the following:

1. Technology foresight and market forecast 2. Product and competitor benchmarks and manufacturing competitive benchmarks

3. Product life - cycle estimates

4. Divisional profitability gap analysis

PRODUCT - LINE PLATFORMS

197

5. Product development maps

6. New product project plans

7. Manufacturing improvement project plans

8. Divisional marketing plans

9. Divisional business plans

PRODUCT - LINE PLATFORMS

In technology strategy for planning development of a product line, an important idea is that of a next - generation technology product - line platform . For example, Susan Sanderson and Mustafa Uzumeri studied Sony ’ s success in its Walkman product line. They concluded that the idea of product platforms as a base on which to vary product models was a key to Sony ’ s long - term success in dominating that product line of portable cassette recorders and players. They wrote: “ Success in fast cycle industries (e.g., consumer electronics) can depend both on rapid model replacement and on model longevity. Sony was as fast as any of its chief competitors in getting new models to market, an important explanation for the wide variety of models . . .

is the greater longevity of its key models ” (Sanderson and Uzumeri 1995b).

The key models provided common technology platforms from which to vary models. In general for any product family, Sanderson and Uzumeri have stressed the importance developing several generations of product platforms for successive generations of product families, used to (1) vary models for covering market niches with a full product line and to (2) advance the performance of generations of product lines to stay out ahead of competitors (Sanderson and Uzumeri 1995b). Marc H. Meyer and James M. Utterback also have also emphasized the concept of product platforms to provide the basis for individual product design (Meyer and Utterback 1993).

In hardware, the strategy for keeping product families high - tech and competitive are next generations of product

-

line platforms for product model

families — based on

radical innovation of a next generation technology.

However, when the twenty - first century began, the cassette player Walkmans became obsolete — replaced with MP3 players. Apple would innovate an integrated MP3 player, the iPod, and an Internet - based music downloading service, iTunes.

This Internet - enabled and integrated hardware and software service would become the new product for the delivery of music. Sony apparently failed to envision the new technical world of the Internet and the new business opportunities it would make possible — a failure of technology and business strategy.

New basic and substituting technologies may obsolete planning for next - generation product lines based on an older technology about to be made obsolete.

198 INTEGRATING TECHNOLOGY AND BUSINESS STRATEGY

As a footnote, there are linguistic differences between hardware and software development terminology. In hardware development, the term hardware platform indicates a common core - technology used in all models of the product line. Whereas software developers use the term product platform to indicate a software system upon which different application software can be run. (We will explore the differences between innovation in hardware and software in a later chapter.) KEY IDEAS

In the cases of:

• Steel industry

• Samsung

• Motorola ’ s roadmaps

We have examined the key ideas of:

• Industrial value chain

• R & D in industrial sectors

• Application system

• Technology roadmaps

• Product - line planning

• Next - generation technology product - line platforms 10

INVENTING TECHNOLOGY

INTRODUCTION

We have seen that technological progress can be anticipated after the basic technology has been invented. But how is a technology invented in the first place? What is the cognitive activity of invention? How is an invention conceived? The questions we will address include:

• How are the potentials of nature used in technology?

• What is the logic of transformation in a technology invention?

• How do the physical forms of nature get used in a technology invention?

• What types of scientific technologies are invented?

• How is a next - generation technology invented?

As shown in Figure 10.1, we look in depth at the activity of invention in the innovation process.

Act of invention

Research

Invent

Commercialize

Market

Nature

Science

Technology

Economy

Utility

Exist

Discover

Manipulate

Embed

Use

naturally

nature

nature

nature

nature

The world as material

The world as money

Figure 10.1 Innovation process

199

200

INVENTING

TECHNOLOGY

CASE STUDY:

Inventing the Electron Vacuum Tube

Recall that the distinctive feature of the modern age is scientific technology —

technological invention made upon a base of scientific knowledge. This is nicely illustrated by the invention of the electron vacuum tube in 1909. The electron vacuum tube began electronics . Before electronics, there were electrical technologies entirely dependent on the crude manipulation of electricity — telegraph, electric light, telephone (and even these were limited by distance). The electron vacuum tube (and its later replacement, the transistor) provided the electrical control of electrical devices. Electronics first made possible long - distance telephony, then radio, and later television, and even later computers and then the Internet, and so on. Electronics is a set of technologies for the precise control of electricity.

The electron vacuum tube was a scientific technology, based on the new science of electromagnetism discovered in the 1800s. The working of the device requires the manipulation of electron flow within the vacuum of a glass tube, as sche-matically illustrated in Figure 10.2 . Within a glass tube, the air is pumped out to

GLASS TUBE WITH AIR EVACUATED

Nagatively charged electrons

Accelerating from cathode to anode

Electric Field

؊

؉

Cathode

Grid to which a controlling

Anode

negative

voltage can be applied

positive

voltage

to accelerate or retard

voltage

the electron flow

Glass Envelope

Plate (anode)

Fillament (cathode)

Grid

Figure 10.2 Physics of the path of the flow of electrons in a vacuum tube

INTRODUCTION

201

create a vacuum inside the tube. A metal plate at one end of the tube is connected to a negative voltage terminal on a battery; this is called the cathode of the tube.

Another metal plate at the other end of the tube has been connected to a positive voltage terminal on a battery and is called the anode of the tube. Some of the metal atoms on the surface of the cathode can lose electrons into the vacuum pushed by the negative voltage on the cathode (and pulled by the positive voltage of the anode).

In electricity, opposite charges attract and like charges repel. The oppositely charged voltages (potentials) on the two plates of the cathode and anode create an electric field in the space between them. It is this electric field that accelerates the electrons — electrons that have boiled out from the metal cathode and accelerated toward the anode. Upon striking the anode, the electrons are pulled into the atoms of the anode and then flow out into the connecting wire to a battery. Then from the battery electrons flow back to the cathode, where some boil out again. This provides a continuous stream of electrons flowing through the vacuum of the tube.

At first, the flowing electrons were called cathode rays . It took many years for science to determine they were electrons and the constituents of atoms. The origin of cathode rays goes back to 1857, when Heinrich Geissler invented a glass tube (the “ Geissler tube ” ) to demonstrate electrical discharge.

Heinrich Geissler (1814 – 1879) was born in Thringia, Germany. He was trained in the art of glassblowing, and in 1850 he established a workshop in Bonn for constructing chemical and physical equipment, which he sold to scientists in Bonn University. With Julius Plucker, he published a scientific paper on studies in thermometry (means of measuring temperatures).

Julius Plucker (1801 – 1868) was a German mathematician and physicist, born in Elberfeld, Germany. He studied at universities in Bonn, Heidelberg, and Berlin.

In 1828, he became a professor of mathematics in the University of Bonn and in 1847

a professor of physics.

In 1858, Plucker published research on the action of a magnet on ea lectric discharge in gases glowing in a tube. It would later be shown that the glow was produced by cathode rays.

Julius Plucker

( http://en.wikpedia.org 2007)

202

INVENTING

TECHNOLOGY

After the Geissler tube, in 1867, the cathode tube was invented by the physicist, Karl Ferdinand Braun. As did the Geissler tube, the cathode ray tube has a cathode and anode,, but in addition it had a heater to warm a cathode (which causes more electrons to boil off the metal of the cathode than an unheated cathode) and also a fluorescent screen at the end of the tube with the anode.

As in Geissler ’ s tube, the anode has a positive voltage relative to the cathode, attracting the electrons pulled along by the electric field. Then the electrons strike the fluorescent screen. There an electron hits an atom in the fluorescent screen, which in turn ejects a photon (light) from the screen. Two magnetic coils around the cathode tube focus the beam of electrons into a small spot on the fluorescent screen and deflect the spot in the direction of a voltage applied to the deflecting coils. In this way, a spot on the screen can be seen to follow the deflecting voltage applied to the deflecting coil. In this form a cathode tube is called an oscilloscope, an instrument very important both to scientific studies and to electrical engineering. (Later the cathode ray tube would become the technological basis for television tube displays, when television was invented in the 1930s.)

Anode

Deflecting coils

Control grid

Fluorescent screen

Header

Electron

Cathode

beam

Focusing coil

Cathode Ray Tube

( http://en.wikpedia.org 2007)

Karl F. Braun (1850 – 1918) was born in Fulda, Germany. In 1872, he obtained a Ph.D. from the University of Berlin. In 1874, he discovered that a semiconductor crystal could change alternating electric current into direct electrical current (an invention not to be useful for another fifty years). In 1895, he became a professor of physics at the University of Strasbourg, andin 1897, he invented the oscilloscope.

Braun also worked on wireless telegraphy, inventing a crystal diode detector for radio signals (around which the first radio receivers were later developed). In 1909, Braun shared the Nobel Prize for physics with Guglielo Marconi for the development of wireless telegraphy.

INTRODUCTION

203

Karl Ferdinand Braun

( http://en.wikpedia.org 2007)

Despite the existence of the scientific instrument the cathode ray tube the real nature of the “ cathode rays ” was still not known. The next event in the scientific progress in the field of electromagnetism would be to determine the nature of the rays. This would move the knowledge base not only (1) for the invention of electronic technology, but (2) also of the investigation of the nature of the atom.

It would turn out that the cathode rays were streams of electrons, and electrons were component particles of atoms.

We recall that the physicist, J. J. Thomson, had proposed a plum pudding model of an atom, which Rutherford demonstrated was incorrect. In 1884, J. J.

Thomson had proved that the cathode rays in the cathode - ray tube were streams of elementary particles, which he called electrons. In Thomson ’ s first experiment on the cathode ray tube, he tried to see if a negative charge could be separated

−

+

Thomson ’ s First Experiment on Cathode Rays

( http://en.wikpedia.org , J. J. Thomson 2007)

204

INVENTING

TECHNOLOGY

from the cathode rays by means of magnetism. He had a cathode ray tube ending in a pair of cylinders with slits in them, with the slits connected to an electrometer (which could record an accumulating negative charge). He deflected the cathode rays from the slit with a magnet and found that no negative charge accumulated on the electrometer. He concluded that the negative charge was inseparable from the cathode rays. The cathode rays carried the negative charge.

In Thomson ’ s second experiment, he studied whether the cathode rays could be deflected by an electric field, which is a characteristic of charged particles.

He inserted a set of metal plates midway between the cathode and anode of the tube and found that a voltage applied to the plates did deflect the cathode rays due to the electric field the voltage produced between the plates.

He concluded that the cathode rays were composed of a stream of charged particles . He called these corpuscles and asserted they were coming from the atoms in the metal plate of the cathode. They were constituent elements of the atom, electrons.

ϩ

ϩ

D

Ϫ

C

A

B

E

Ϫ

Thomson ’ s Second Experiment

( http://en.wikpedia.org , J. J. Thomson 2007)

Thus, by 1894, Thomson had demonstrated that cathode rays were streams of negatively charged particles, which he named electrons . The science base for using electron streams in vacuum tubes was established.

Next to happen was the application of the science to technology, scientific technology. Two devices were invented, first the diode electron vacuum tube and then the triode electron vacuum tube.

The first device, the diode electron vacuum tube, was invented in 1904

by John Ambrose Fleming. He patented an electron vacuum tube with two electrodes, a cathode (negative voltage plate) and an anode (positive voltage plate). Its application was to change voltages from alternating current to direct current.

John Ambrose Fleming (1849 – 1945) was born in England and was educated at University College School in London and in University College London. After graduation, he lectured there and at other English universities on electrical engineering. He consulted for Marconi Wireless Telegraph Company and later for Edison Light Company.

INTRODUCTION

205

John Ambrose Fleming

( http://en.wikpedia.org , John Ambrose Fleming 2007) Next, in 1906, the electron vacuum tube was invented by Lee de Forest as a triode vacuum tube. De Forest added a third electrode to Fleming ’ s diode vacuum tube to control the flow of electrons between cathode and anode (control grid). This allowed for amplification of voltage signals applied to the control grid by larger voltage signals between the cathode and anode that exactly followed the control grid signal pattern — signal amplification. He called his invention the audion tube .

Audion Tube in 1908

( http://en.wikpedia.org , Lee De Forest 2007)

Lee De Forest (1873

–

1961) was born in Iowa. In 1893, he went to Yale

University, graduating with a bachelor ’ s degree in 1896 and a Ph.D. in 1899. His doctoral dissertation was on radio waves. He joined the faculty of the Armour Institute of Technology (now a part of the Illinois Institute of Technology), where

206

INVENTING

TECHNOLOGY

he invented his audion tube. He filed for a patent in 1908. Later, he was charged with fraud over claims about the patent. He won the trial but with large legal fees, he was nearly bankrupt. He sold his patent to AT & T for $ 50,000. AT & T produced the first practical triode vacuum tubes and used them in electronic circuitry to amplify long distance telephone calls. De Forest continued to invent and had other lawsuits over patents. In 1919, De Forest filed a patent for a sound - on - film process. In 1931, he sold a radio - manufacturing firm he had established to RCA.

In 1922, He received a Medal of Honor from the Institute of Radio Engineers, and in 1946, he received the Edison Medal of the American Institute of Electrical Engineers. The Institute of Electrical and Electronic Engineers named one of the prestigious awards after him, the Lee De Forest medal.

Lee De Forest

( http://en.wikpedia.org 2007)

TECHNOLOGY

AND POTENTIALS OF NATURE

Electrons occur in nature as components of atoms, orbiting a positively charged atomic nucleus of protons. In a metal slab, metallic atoms arrange themselves as an orderly matrix; and if a thin metal slab is used as an anode in a vacuum tube, some of the electrons of the surface atoms “ boil off ” (emerge) into the space of tube.

These can then be accelerated down the tube by an electric field — when a positive voltage is applied to the distant anode.

So the physics of nature is operating within the arrangement of the vacuum tube.That arrangement is constructed by the inventor for the purpose of using (manipulating) that physical nature. Thus, for the inventor this arrangement itself (of the vacuum tube with its negatively charged cathode and positively charged anode) is an idea — an inventive idea. The inventor supplies a kind of “ logical idea ” in which to construct the electron vacuum tube (its order as cathode to anode with intervening grid).

TECHNOLOGY AND POTENTIALS OF NATURE 207

The inventor adds a logic to a nature — a logical process in which to order a natural physical process.

Why does the addition of a logic to the natural process make such a difference?

Why does it make a technology? We recall that technology can be defined as a manipulation of nature for a human purpose. The logic in the invention guides this manipulation . This is a subtle but essential idea to understanding what kind of logic guides technology manipulation of nature.

We will call this a transformational logic — a logic that guides a natural transformation in nature.

In a popular book from the 1970s, an author (based not in technology but in the humanities) tried to understand what a transformational logic does to nature — but without this precise terminology. He was John Pirsig, and he used the term of a ghost of nature for this idea that nature can be potentially manipulated by a transformation logic created by an invention. Pirsig ’ s book was titled Zen and the Art of Motorcycle Maintenance (Pirsig 1974) , Pirsig used the examples of both motorcycle maintenance and of Zen Buddhism as ways to communicate the relationship between nature and logic in a technology. Pirsig wrote: “ Not everyone understands what a completely rational process this is, this maintenance of a motorcycle . . . A motorcycle functions entirely in accordance with the laws of reason, and a study of the art of motorcycle maintenance is really a miniature study of the art of rationality itself ” (Pirsig 1974).

By the art of rationality, Pirsig emphasized that the design and operation of any technological device (like a motorcycle) is an implementation into matter of a system of ideas:

That ’ s all the motorcycle is, a system of concepts, worked out in steel. There ’ s no part in it, no shape in it, that is not out of someone ’ s mind . . . Shapes, like this tappet, are what your arrive at, what you give to the steel. Steel has no more shape than this old pile of dirt on the engine here. These shapes are all out of someone ’ s mind. That ’ s important to see. The steel? Hell, even the steel is out of someone ’ s mind. There ’ s no steel in nature. Anyone from the Bronze Age could have told you that. All nature has is a potential for steel! There ’ s nothing else there. But what ’ s potential? That ’ s also in someone ’ s mind! . . .

Ghosts (Pirsig 1974).

A system of concepts worked out in matter is a kind of functional logic. Technology is about the potentials in nature, the potential of logic to order nature — the ghosts of nature.

Technological invention arranges to bring the potentials of nature into existence.

208

INVENTING

TECHNOLOGY

This humanistic fascination with the power of technology to shape nature has long been a part of human culture. In the earlier story of the invention of the computer (see Chapter 3 ), T. A. Heppenheimer cites the ancient Greek myth of Prometheus, the giver of the gift of fire from the Titans to the humans. The Titans punished Prometheus by chaining him to a mountain, exposed to the elements of nature. In that metaphorical manner, all technological invention has been a kind of Promethean activity:

• Taking power over nature from the domain of the Titans

• Giving that power to human control.

What any of these metaphors about technology (like ghosts of nature ’ s potentials or Promethean inventions ) really mean is that in every technology, one should look for a kind of logic and a kind of natural form:

1. The human act of first creating a new mapping of the logic of function to a natural form is invention of a physical technology.

2. The human act of first creating a new mapping of the logic of function

to the control of a task is an invention of a software technology.

Physical technological invention consists of imagining (1) the selection or construction of a particular physical structure and the arranging for the sequences of physical process in the structure, in order to (2) correlate with the sequences of logic set of functional transformation. Thus, the secret of any physical technology is the invention of a physical structure whose physical processes can be mapped in a one -

to - one manner with a logical operation of a functional transformation. There are two important sides to this mapping:

1. Transformational logic . A kind of spirit, a purpose in the mind of the inventor, a ghost of nature as a potential state of physical nature.

2. Physical process

. Physical structures and processes, a physical mechanism in nature.

CASE STUDY:

Inventing the Transistor

As extraordinary as the electron vacuum tube was, still it had technical limitations. The tubes were relatively big and used a lot of electricity to heat the anode (to boil off the electrons). By the middle of the twentieth century, there was a need for an electronic control - device better than an electronic vacuum tube. The research laboratory that took on the task of replacing the electronic vacuum tube was AT & T Bell Laboratories ( “ Bell Labs ” ) in the United States.

TECHNOLOGY AND POTENTIALS OF NATURE 209

Early in the twentieth century, AT

&

T was the centralized U.S. telephone

company, providing long - distance telephone service to the nation. AT & T used electron vacuum tubes for amplification of phone conversations, after engineers at Bell Labs had innovated their first mass production. In the 1930s (only twenty years after the innovation of the electronic vacuum tube), some of Bell Lab ’ s scientists were thinking of replacing the electronic vacuum tube by a better device.

But what? This was the challenge that Mervin Kelly of Bell Labs undertook in 1936. Kelly had been appointed director of research of the Bell Labs and established a research group to search for a way to replace the electron vacuum tube (Wolff 1983).

Kelly had gone to work for the Western Electric engineering department, after he had received a Ph.D. in physics from the University of Chicago in 1918. He had been born in 1894 in Princeton, Missouri, where his father was principal of the high school in that small town. When Kelly graduated from high school, he enrolled in the Missouri School of Mines and Metallurgy. There he decided that physics and mathematics were more interesting to him than mining. In 1914, he graduated and enrolled for graduate work at the University of Kentucky. He also taught for two years to support himself, leaving with a master ’ s degree in physics and a new bride. In 1916, he went to the University of Chicago, where Kelly worked as an assistant to Professor Robert Millikan. Millikan was the first to accurately measure the electrical charge of a single electron. Kelly assisted in this experiment and received his doctorate. Later, Millikan received a Nobel Prize in physics for the experiment.

When Kelly graduated, the director of the engineering department in Western Electric was Frank Jewett — who had earlier been a student of Millikan. Jewett hired Kelly. In 1925 when Bell Labs was established, Jewett became its first director, and Kelly was also transferred to the new lab. In 1936 Kelly succeeded Jewett as director of Bell Labs.

As Kelly was trained as a physicist, he had been following the exciting developments in physics of that decade of the new quantum mechanics (Bardeen 1984).

Kelly saw a technological opportunity in quantum mechanics, as it was providing theory to explain the behavior of electrons in materials. Kelly envisioned that the new theory applied to certain semiconducting materials, germanium and silicon, might provide a way to invent something to replace the electron vacuum tube.

Earlier when radio was being developed using the electron vacuum tube, one of the other inventions then was the discovery that germanium could be used to detect radio signals. Also another discovery had been that silicon crystals could convert light into electricity and change alternating current to direct current. Both these interesting materials had been called

semiconducting

materials, because

their electronic conductivity was between the high conductivity of metals and the nonconductivity of dialectic materials.

In 1936, Kelly created a new research program in Bell labs in solid

-

state

physics — to explore the further use of the materials of germanium and silicon, understanding these with the new theory of quantum mechanics. One of the new hires Kelly made for his new program was William Shockley (1910 – 1989).

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Born in London to American parents and raised in California, Shockley received a bachelor

’

s degree from the California Institute of Technology and in 1936

and he received his Ph.D. in solid - state physics from MIT: “ Kelly gave me an eloquent pep talk . . . . He pointed out that relays in telephone exchanges caused problems . . . . He felt electronics should contribute . . . . Dr. Kelly ’ s discussion left me continually alert for possible applications of solid - state effects in telephone switching problems ” (Wolff 1983, pp. 72 – 73).

Kelly ’ s support and Shockley ’ s alertness paid off — but not until after World War II. (During the war, Kelly was responsible for 1,200 military research programs performed at Bell Labs.) After the war, Kelly reestablished the solid - state physics research program and invited Shockley back to Bell. (Shockley had spent the war at the Pentagon, working on military problems.)

Kelly also appointed James Fisk as assistant director of physical research at Bell and told him to make a major and broad research effort in all aspects of solid - state physics and the underlying quantum mechanical theory. Fisk set up three research groups: physical electronics, electron dynamics, and solid - state physics. William Shockley and Stanley Morgen led the solid - state physics group.

They hired John Bardeen and Walter Brattain, also new solid - state physicists, into the group.

John Bardeen (1908 – 1991) was born in Madison, Wisconsin. He obtained a bachelor ’ s degree in electrical engineering from the University of Wisconsin -

Madison. In 1936, he had obtained a doctorate in mathematical physics from Princeton University.

Walter Brattain (1902

–

1987) was born to American parents in China and

was raised in Oregon and Washington. In 1924, he obtained a bachelor ’ s degree in physics at Whitman College and a master ’ s degree from the University of Oregon in 1926. In 1929 he obtained a doctorate in physics from the University of Minnesota. He joined Bell Labs in 1929.

Shockley

’

s group learned how to alter the conductivity of germanium by adding other atoms (doping) to the germanium crystal during its growth. Next Shockley proposed an artifact (transistor) to control the current in doped

-

germanium crystals — by applying an external electric field perpendicular to the direction of current in the crystal. This was to work similarly to how a voltage applied to the grid of an electron vacuum tube controls the flow of electrons in the tube.

The reason Shockley thought this was practical for germanium was that he had used the new quantum mechanics theory to calculate the expected effect of the controlling field on the electron flow. His calculations showed that it would be large enough to provide amplification. Amplification meant that the electron flow through germanium would closely follow the increases and decreases of the controlling field, thereby mimicking its form and with larger amplitude.

Shockley made a sample, but it didn ’ t work — it didn ’ t amplify. Shockley asked Bardeen . . . to check his calculation. Bardeen did so, and suggested that perhaps electrons were being trapped at the surface of the crystal, which would prevent them from seeing the calculated effect of amplification.

TECHNOLOGY AND POTENTIALS OF NATURE 211

Next, Brattain performed experiments on the artifact that confirmed Bardeen ’ s explanation. They were on the right track. They needed to play with the structure of the artifact, trying other configurations. Finally, one configuration worked, but provided only a small amplification. A new kind of electronic valve was almost invented, but the amplification was too small. The group continued to invent alternative configurations.

Another configuration placed two closely spaced gold strips on the surface of a dope germanium crystal and an electrical connection on its base. Relative to that base, one gold strip had a positive voltage (called the emitter) and the other a negative voltage (called the collector). They applied a signal between emitter and base. Voila! There was sufficiently large amplification in the voltage between the collector and the base! They called this a transistor. It could do everything the old electron tube could do and would eventually replace it.

( http//:en.wikipidia.org , Transistor 2009)

AT &

T acquired a basic patent for the transistor and made transistors for its own use. AT & T licensed others to make them for other uses. At that time, AT & T

was a legal telephone monopoly and prevented from entering other businesses.

The electron vacuum tube had begun the first age of electronics, and the transistor began the second age of electronics. (The invention of the semiconductor chip would begin the third age of electronics — all within the first seventy years of the twentieth century.)

That change from tubes to transistors is an illustration of a technology

discontinuity — an invention that provides a very dramatic improvement in technical performance of a technology.

This change was made by inventing a new device to perform a similar technical function as an older device but upon a new physical phenomenon (electron

212

INVENTING

TECHNOLOGY

transport in solid - state materials as opposed to electron transport in a vacuum).

Shockley, Brattain and Bardeen were awarded the Nobel Prize in physics in 1956.

Later, Kelly became chairperson of the board of Bell Telephone Laboratories, retiring in 1959. In that same year, he was awarded the John Fritz Medal for

“ his achievements in electronics, leadership of a great industrial research laboratory, and contributions to the defense of the country through science and technology. ” The medal is awarded annually by the American Association of Engineering Societies. Recipients have included Edison, Marconi, Wright, and Westinghouse.

In 1953, Shockley left Bell Labs to work at Beckman Instruments, where he headed a new semiconductor laboratory. From Beckman he went on to establish a transistor company, the Fairchild Company in Palo Alto, California. In 1961, he became a professor of Engineering and Applied Science at Stanford University.

In 1951, Bardeen went on to teach at the University of Illinois. In 1957, he developed with collaborators (Leon Cooper and John Schrieffer) the theory of superconductivity. He received a second Noble Prize for this theory in 1972

(shared with Cooper and Schrieffer).

William

Shockley

John Bardeen

Walter Brattain

( http://en.wikipedia.org 2008)

NEXT - GENERATION TECHNOLOGIES

In the invention of the transistor as a next - generation of technology to replace the earlier electron vacuum tube technology, the inventive change was in using different nature — motion of electrons in semi - conductor materials (transistor) rather than the motion of electrons in a vacuum (electron vacuum tube). This is one way to improve technologies — change the nature used in the technology, i.e., use different natural phenomenon. There are also other ways that NEXT-GENERATION

TECHNOLOGIES

213

technology invention can address the limitations of current technologies by inventing improvements:

1. Substitute new physical phenomenon in a new technology for the physical phenomenon in an earlier technology (e.g., solid - state physics for the transistor substituting for the earlier electron current in a vacuum tube) 2. Alter the configurations of the physical morphology (e.g., helicopter lift -

rotators for fixed - wing aircraft.)

3. Improve the schematic logic of the system (e.g., implementing a electronic circuit on a semiconductor chip).

CASE STUDY:

Inventing the Silicon Transistor

The germanium transistor suffered from a serious technical performance problem: sensitivity to temperature. What good would transistorized electronics be if their circuit performance changed dramatically from the heat of day to the cool of night, from the hot of summer to the cold of winter? So the historical setting of this case study is about ten years after the original invention of the germanium transistor.

Many electronic engineers appreciated the new technology but needed a more reliable, less - temperature sensitive version of the transistor. Most researchers felt that the obvious route was to try to make a transistor not from germanium but from its sister element silicon.

One of the groups looking for a silicon version of the transistor was based in a then - small U.S. company, Texas Instruments (TI). In 1952, Patrick Haggerty was president of TI, and the company was a maker of seismographic detection instruments, sold to oil companies for use in oil exploration. Their instruments used electron vacuum tubes but needed to be portable and rugged and use little power. It was obvious to Haggerty that transistors would be a desirable replacement for tubes in TI ’ s products.

Haggerty had assigned a research project to one of his employees, Mark Shepard, to develop a germanium transistor that could be sold for U.S. $ 2.50 at that time. Shepard developed it; TI produced a pocket radio with it in 1954. But TI did not follow through with this product, and SONY very soon introduced its own independently developed germanium transistor and pocket radio and proceed to commercially exploit this new product.

Still, Haggerty knew the germanium transistor needed to be replaced. He hired a physicist from Bell Labs, Gordon Teal, who had been researching silicon.

Haggerty told Teal and another researcher, Willis Adcock (a physical chemist) to develop a silicon transistor. Many other research groups were seeking the silicon transistor; and it was not an easy artifact to make. It was brittle and difficult to purify as a material for making transistors. But Teal and Adcock did it.

In May 1954, Teal took their new silicon transistor to a professional conference, where he listened to several speakers tell of their difficulties in trying to make a 214

INVENTING

TECHNOLOGY

silicon transistor. What he heard made Teal happy, for as yet no one else had succeeded. When Teal ’ s time to speak came, he stood before the group and announced:

“ Our company now has two types of silicon transistors in production . . . I just happen to have some here in my coat pocket ” (Reid 1985, p. 37). Teal ’ s assistant came onto the speaker ’ s stage carrying a record player. This player used a germanium transistor in the electrical circuit for the amplifier. The tiny germanium transistor had been wired visibly outside the record player with long leads. Teal plugged in the player, put on a record and started it playing music. Next the assistant brought onto the stage a pot of hot oil and set on the table beside the record player.

Teal picked up the connected germanium transistor and dramatically dunked it into the hot oil. Immediately, the music stopped as the germanium transistor failed in the oil ’ s hot temperature. Then Teal picked up one of the silicon transistors that he had earlier taken from his pocket and placed on the demonstration table. He took a soldering iron (which the assistant had also supplied) and replaced the germanium transistor with the new silicon transistor. The music from the record player sounded again. Then Teal picked up the silicon transistor and dunked it into the pot of hot oil, as previously he had with the germanium transistor. The music did not stop. It continued!

The silicon transistor could stand the heat! The meeting exploded in excitement. Texas Instruments had done it. Finally, a useful transistor — the silicon transistor.

INNOVATION PROCESS OF NEXT - GENERATION - TECHNOLOGY

PRODUCTS

The phenomenal base of a technology is the nature manipulated by the logic of the technology. And next - generation technologies can be invented by changes either in the phenomenal base or in the logic scheme. In the above illustration, the silicon transistor used a similar logic schema to that of the germanium transistor (base, emitter, and collector) but a different phenomenon — silicon matter substituted for germanium matter.

Earlier we saw that radical innovations are innovated in a linear process and incremental innovations in a circular process. Next generation - technologies invented and implemented into products/services in a combination of circular and linear processes, as sketched in Figure 10.3 .

• In the cyclic innovation process, there should be a stimulation of need for a new technology that fosters the vision of a next - generation technology in the linear innovation process.

• The technical feasibility prototype in the linear innovation process provides the grounds for anticipation of new technology in the cyclic innovation process.

• The functional prototype in the linear innovation process provides the information for acquiring new technology in the cyclic process.

• The implementation phase in the cyclic process designs the engineering prototype in the linear process (with concurrent engineering practice fostering both manufacturing prototypes and pilot production).

INNOVATION PROCESS OF NEXT-GENERATION-TECHNOLOGY PRODUCTS 215

Acquire

Product

Anticipate

development

Implement

cycle

Stimulate

Exploit

Volume

Technology

production of

feasibility

next-generation

Functional

prototype

prototype

product

Vision

of

next-generation

Engineering

Manufacturing

Pilot

technology

prototype

prototype

production

process

system

Figure 10.3 Discontinuous innovation — Next - generation technology Volume production in the linear process for a next - generation product provides the opportunity for the exploitation of the new technology in the cyclic innovation process.

By establishing technical feasibility, the linear innovation process anticipates in the cyclic innovation process when a new generation of a technology is possible.

Once such possibilities can be demonstrated, the next problem is estimating the timing of the introduction of new technology. This is critical. Timing of innovation in advance of competition is one of the most important competitive factors in technology. The step of functional prototyping in the linear process establishes this timing — for a next - generation technology to be technically ready to be designed into a new product. The design of the engineering prototype of that new product allows the product - development - process in a strategic business unit to begin implementing the new technology in new products. In the product development cycle, the critical step is exploiting a new technology. When one produces and markets the new product, the critical events in timing are: design, production, and quality.

CASE STUDY:

Inventing the Semi - conductor Integrated Circuit Chip

The transistor progress from germanium to silicon altered the physical morphology of transistor circuits (from germanium to silicon) but not the logic. This case study illustrates how progress in a technology can occur from altering the logic scheme of the technology without altering the phenomenal base.

The historical context was the middle of the twentieth century after the invention of the transistor, which made possible much more complex circuits thanks to 216

INVENTING

TECHNOLOGY

the smaller size and power requirements of the transistor compared to the older electronic tube. In the late 1950s, electrical engineers were using the new silicon transistor for many advanced electronic circuits. The transistors were so useful that a new problem arose in electronics. The new complex circuits engineers could now dream up required so many transistors that it was physically impossible to wire them together with the technology of soldering transistors into printed circuit boards. So a new natural limit was on the horizon for transistorized electronics — the number that could be physically wired together. In 1958, this limit was on the mind of two researchers: Kilby at Texas Instruments and Noyce at Fairchild.

Jack St. Clair Kilby had grown up in Kansas. As a boy he loved technical things and built a ham radio set using electron tubes (Reid 1985). In September 1941, he went to college at the University of Illinois to become an electrical engineer. But three months later, the United States was at war with Japan, and Kilby joined the U.S. Army. He ended the war as a sergeant, assigned to an Army radio repair station on a tea plantation in India. He returned to college, graduated as an electrical engineer, and went to work at a firm named Centralab, located in Milwaukee.

One evening he attended a lecture at nearby Marquette University given by John Bardeen, one of the inventors of the transistor. The new technology astonished Kilby, and he began reading everything he could find on the solid - state artifice.

In 1951, Bell Labs announced the licenses, and Centralab purchased one for the $ 25,000 fee. They sent Kilby to Bell Labs for a five - day course in semiconductors.

From 1951 to 1958, Kilby designed transistorized circuits for Centralab. As these circuits increased in complexity and numbers of transistors, Kilby saw the need for technical breakthrough beyond hand - wiring transistors together.

But Centralab did not have the research capability that Kilby would need to tackle the problem. In 1958, he sent his resum é to several larger firms, one of which went to Willis Adcock of Texas Instruments. Adcock hired Kilby, since TI was also worried about the integration problem of transistors. Adcock assigned Kilby to a

“ micro - module ” research project that was already underway at TI to approach the problem. But Kilby didn ’ t like their approach. One thing was clear to him: since TI led in silicon technology, a good solution to transistor integration should use silicon technology — if it could. So he had this idea: If transistors were made of silicon, could not the other components of a circuit, such as resistors and capacitors, also be fabricated on silicon? Why not make the whole circuit on a silicon chip?

It was an inventive idea, for at the time the technology for fabricating resistors used carbon as the phenomenal material and plastics and metal foil for capacitors. On July 24, 1958, Kilby wrote down in his lab notebook what he called a monolithic idea: “ The following circuit elements could be made on a single slice (of silicon): resistors, capacitors, distributed capacitors, transistor ” (Reid 1985, p. 38). He entered rough sketches of how to make each component by properly arranging silicon material.

Kilby then showed his notebook to Adcock, who said if he could demonstrate the idea worked, an R & D project to develop it would be authorized. Kilby carved out a resistor on one silicon chip and then carved a capacitor on another and wired the two chips together. They worked, showing resistance and capacitance TYPES OF TECHNOLOGIES 217

in the circuit. Adcock gave his approval to build a whole circuit on a chip and Kilby chose to build an oscillator circuit — one that could generate a sinu-soidal signal.

On September 12, 1958, Kilby was ready. A group of TI executives assembled in the lab for a demonstration. The circuit on the chip was hooked to an oscilloscope which would display the form of the signal from the circuit. Before the circuit was turned on, the oscilloscope displayed a flat line indicating no signal output. Kilby switched on the new integrated circuit on the silicon chip and it worked — a wiggling line appeared on the screen, the sine - wave form of an oscillator circuit: “ Then everybody broke into broad smiles. A new era in electronics had been born ” (Reid 1985, p. 39).

Sometimes a basic invention is made independently at about the same time by another inventive mind aware of the technological needs and opportunities.

This happened with the invention of the semiconductor integrated circuit (IC) chip. In that same summer of 1958, Robert Noyce, who was a physicist and then president of Fairchild Semiconductor, was also worrying about the problem of transistor integration. Fairchild Semiconductor was a new company to manufacture transistors, begun by one of the inventors of the transistor, Shockley, after he left AT & T. That following winter, on January 23, 1959, Noyce conceived a solution and wrote in his lab notebook: Writing in a lab notebook is necessary as a record for establishing priority in applying for a patent. “ It would be desirable to make multiple devices on a single piece of silicon, in order to be able to make interconnections between devices as part of the manufacturing process. ” (Reid 1985, p. 41).

Both Texas Instruments and Fairchild Semiconductors filed for the patent on the IC chip. Years of legal argument followed about who had the right to the patent. In 1966, several semiconductor chip - manufacturing firms met together.

TI and Fairchild agreed to grant licenses to each other and share in inventing the device. Later, Kilby and Noyce both received (in addition to many other prizes) the National Medal of Science for inventing the semiconductor IC chip.

`

We see in this illustration that the key inventive idea of the IC chip was in the form of the logic of arrangement of the technology and not in the form of change in the physical phenomenon. The transistors on the IC chip were still made of silicon. The inventive idea was to make and connect other electrical components also on silicon. This change in logic was powerful, as was the previous change in phenomena in the invention of the silicon transistor where the phenomenal base was changed.

TYPES OF TECHNOLOGIES

What kinds of technologies are there in the world? There are many, but they can all be classified into four types: material, biological, power, and information, as shown in Figure 10.4 . One can use two universal dichotomies to create this taxonomy of technologies: Substance (or form) and inanimate (or animate). Since all matter is 218

INVENTING

TECHNOLOGY

Inanimate

Animate

Matter

Material

Biological

technologies

technologies

Power

Information

Mind

technologies

technologies

Figure 10.4 Types of technologies

either inanimate or animate, all physical technologies manipulate either inanimate or animate material nature as:

material technologies or biological technologies .

Technologies can also manipulate the forms of inanimate or animate nature as: power technologies or information technologies .

The philosophical dichotomy of inanimate

-

animate (nonliving and living) is

relatively self - explanatory. But the philosophical dichotomy of substance and form requires some background in philosophy. In any activity or event, substance indicates the content (or material), and form indicates the structure (or processes) about the content. For animate technologies, form consists of the ideas of the technology, and for inanimate technologies, form consists of the energy of the processes. For example, information technologies manipulate the ideational forms of communication, whereas power technologies manipulate the energetic forms of power.

It is useful to think generically about all technologies, since the management of innovation differs among these types. Innovation of information technologies (e.g., software) requires different techniques than innovation in material technologies (e.g., plastics). For example, now innovation in biological technologies is requiring an ever - deeper understanding of the science of biology. Innovation in power technologies is requiring an ever

-

greater challenge of inventing nuclear

fusion technologies. Moreover, much of the recent advances in radical new technology are crossing between technologies. For example, nanotechnology crosses between material and biological technologies. Robotics crosses between biological and information and power technologies. Current advances in power technologies cross with information technologies.

This typology can be used for an innovation team to discuss technology strategy, when several different types of technologies are involved. Neither the team leader nor any of the experts as team members will know all the technical details of all the technical disciplines needed to compose the team. But discussing technology generically will enable the leader and the team to converse together and think concretely about the technology strategy in the project.

The importance of thinking about technologies generically is to help a technology manager lead an innovation team composed of different disciplinary technical experts.

KEY IDEAS 219

KEY IDEAS

In the case study of:

• Invention of the electron vacuum tube

• Invention of the transistor

• Invention of the silicon transistor

• Invention of the integrated circuit semiconducting chip We have examined key ideas about technological innovation and economic development as:

• Technology and potentials of nature

• Physical morphology of a physical technology invention

• Innovation process for next - generation technologies

• Transformation logic of a technological invention

• Types of technologies

11

TECHNOLOGY SYSTEMS

INTRODUCTION

We have looked at basic invention of new technologies; but once invented, how can a technology be improved? Can one anticipate further progress in a technology? How can continuing progress be anticipated, foreseen, forecasted—planned?

Technology planning is possible because all technologies are systems, and one can identify opportunities within the system for doing research to improve the system.

We address the following questions:

• What is the general pattern in which a technology is improved?

• What are the differences in the patterns of technological progress between physical and software technologies?

• How can continuing technological progress be planned?

As shown in Figure 11.1, we look at how improving the utility of a technology can stimulate the anticipation for technical progress and then how technical progress can be planned

Anticipating and Planning Technical Progress

Research

Invent

Commercialize

Market

Nature

Science

Technology

Economy

Utility

Exist

Discover

Manipulate

Embed

Use

naturally

nature

nature

nature

nature

The world as material

The world as money

Figure 11.1 Innovation process

220

INTRODUCTION

221

CASE STUDY:

Wright Brothers’ Flyer

One can see a technology as a system in the invention of the airplane. The first successful powered flight was made in 1904 by the Wright brothers, Wilbur and Orville, who are credited with inventing the airplane. They were Americans, born in Indiana and Ohio, respectively, and they spent most of their life in Dayton, Ohio. They performed their critical experiments on the airplane on the Atlantic coast at Kitty Hawk, North Carolina.

Wilbur Wright (1867–1912) had planned to attend Yale, but an injury changed his mind and he remained in Dayton. Neither brothers attended college. In 1889, Orville left high school to start a printing business, building a printing press with Wilbur’s help. In 1892, they quit the printing business and opened a bicycle repair and sales shop.

Wilber Wright

Orville Wright

http://en.wikipedia.org 2007

In the 1890s, bicycles were newly popular in America and in Europe. In 1896, the Wright brothers built their own brand of bicycle, and the bicycle business financed their next mechanical interest—inventing an airplane. As ardent bicy-clists, they appreciated the control problem of balancing and steering a bicycle.

The spinning wheels of a bicycle act physically in motion, as do gyroscopes; the impact of a force on a gyroscope changes the direction of motion perpendicular to the force, not in line with it. Thus, to steer a rapidly moving bicycle one does not turn the handles but shifts balance. This subtlety about system control was to help them solve the challenge of airplane control.

Also in the 1890s, the brothers had seen articles about gliding by Otto Lilienthal in Germany, who made large gliders. In 1896, Samuel Langley flew an unmanned steam-powered model aircraft; and Octave Chanute made gliders and tried gliding along the shore of Lake Michigan and in Germany. In 1896, Lilienthal was killed in a gliding accident. In 1899, Wilber and Orville Wright decided to build an 222

TECHNOLOGY

SYSTEMS

airplane, beginning with building gliders. The invention was not simple, because the airplane as an entity was complex and the process of flight tricky.

The first essential component of the airplane system is the wings. The curvature of the upper surface of an airplane wing (as opposed to a flat underside) directs airflow up and over the wing, creating an area of lower air pressure above the wing than below the wing. This difference in air pressure of the two surfaces produces lift, an upward force to raise the wing up into the air. The amount of this lift was critical to the Wright’s invention, because the lifting force had to be enough to lift not only the airplane structure but also the weight of passengers and weight of the airplane motor.

The shape of the upper wing surface is that of a chord, gradually curving upward from the front surface of the wing to a high point and then more gradually descending to the back surface of the wing. How high the high point and where the high point was located front to back (nearer the front than the back) were the critical features of the wing in determining the lifting force.

For their first glider in 1900, the Wright brothers had used calculations on the height and centering of the wing’s chord (which had been published by Lilienthal on how to build a glider’s wing). They tried gliding that summer in 1900 at Kitty Hawk, a coastal island off North Carolina, where a steady breeze blew in from the ocean for flight and sand dunes made launching and landing possible. But they found the wings did produce sufficient lift, and, clearly, Lilienthal’s wing calculations were not technically accurate.

Back in Ohio, the Wright brothers turned experimenters and built a wind tunnel in their shop. They built small models of wings with different chords and measured the weight they could lift in the wind tunnel. From this experiment, they constructed a more accurate table of chord shape versus wing-lift potential.

This was a part of the Wright brothers’ experimental approach to invention. They experimented with building a glider, and they experimented with different wing shapes to optimize lifting force of a wing.

They understood that the system component of the wing had a process relationship to the environment of the airplane—as airflow over the shape of the wing produced lift.

For the next summer they constructed a glider with two wings, and in trials at Kitty Hawk, the new glider did provide sufficient lift to carry a person in the air.

But the glider did not fly stably. Next, they had to tackle the next system problem of the airplane—control.

Tom Couch described the Wright brothers’ glider control: When completed and tested on the 1902 Wright glider, this system consisted of a fore-elevator for pitch control, a wing-warping mechanism that provided a helical twist across the entire wing in either direction for roll control, and a rudder [originally linked to the wing-warping system] for control in the yaw [nose right/nose left] axis. . . . [The pilot’s] hips rested in a narrow

INTRODUCTION

223

cradle, which was shifted from side to side to raise or lower either wing tip. His right hand gripped an upright lever that controlled the forward elevator. The control responses would be as simple and natural as those required to ride a bicycle. The control system had to control flight in three dimensions—forward, up-down (pitch), left-right and roll (Crouch 1986).

Next, in 1902, Orville added a rear rudder to assist in controlling turning.

Design of a Wright 1899 Kite

Wright 1902 Glider

(http://en.wikipedia.org, Wright Brothers 2007)

The Wright brothers had to add another component to the system of the airplane—power to the glider. They built a gasoline engine and designed propellers for the engine to blow air past the wings. Again, for the design of the propeller, they used their wind tunnel to get the twisting shape of the propeller just right to push the air efficiently onto the plane. From wind-tunnel experiments, the Wright brothers had designed both efficient propellers and wings so the propelled air could provide wing lift.

On December 7, 1903, the Wright brothers made the first successful powered flight in the world—39 meters (120 feet) for 12 seconds. They had invented the airplane!

Wright 1903 Flyer

(http://en.wikipedia.org, Wright Brothers 2007)

224

TECHNOLOGY

SYSTEMS

The later history of the Wright brothers is a business history. They established the first airplane manufacturing business and sold their flyers to military customers. Only eleven years later, in 1914, the early airplanes were to play a major role in the First World War, as observation platforms for military intelligence.

We can see in this illustration how the airplane should be thought about as a technology system, involving several components: (1) a structural architecture of the plane and wings, (2) engines for power, and (3) control apparatus. And these components are connected together in the process of flight through an atmospheric environment.

After the invention of the airplane, many people began improving on the invention. The Wright brothers had used cables to twist the wingtips for left-right control of the roll of the airplane. Curtis invented wing flaps at the backsides of the wings to provide improved control of roll. The Wright brothers had used wheels for takeoff and landing on land. Curtis replaced them with pontoons for water takeoff and landing. Sikorsky even had the idea to replace the wings with rotors and invented the helicopter.

So many improvements occurred in that first decade that during the First World War in Europe from 1914 to 1918, the airplane became a dependable and deadly front-line observer and as engaging in fierce plane-to-plane combat. Progress in aviation was linear throughout the 1920s and 1930s, until the performance limit of propeller-driven aircraft was reached in the early 1940s in World War.

In flight, the speed of propeller-driven airplanes increased between 1904 and 1946 from about 70 miles an hour to about 250 miles an hour for level flight.

But in World War II, German aircraft designers innovated a jet-propelled fighter reaching a speed of nearly 500 miles per hour (833 kilometers per hour); fortunately for the Allied forces of that war, Germany did not produce many of the new fighters. The jet engine had passed the physical limitation on speed of the propeller driven aircraft.

TECHNOLOGY

SYSTEM

The concept of a system means to look at a thing, an object, with a view to seeing it as a dynamic totality—displaying change, and encompassed in an environment.

A description of a thing as a system captures its totality with dynamic changes

represented as transformations of system states within a relevant environment.

The system is a very general concept that can describe anything in the natural world as a totality with dynamic change. There are two general forms of systems, closed and open systems (Betz 1968). A closed system does not have significant inputs from its environment and may not have significant outputs. A closed system TECHNOLOGY

SYSTEM

225

is principally described by transformations between internal states of the system. An open system has both inputs from its environment and outputs to its environment.

Any technology can be represented as open system—a functional totality, transforming inputs from its environment into outputs to its environment. All systems have a boundary and an environment and internal states. Within the boundary of the system, there will be a process—structured as parts and connections between parts that perform the state-to-state transformations of the system process. There will always be at least two subsystems within the system, a transformation process subsystem and a control subsystem.

A system is defined by its dominant process or processes.

The boundary of an open system consists of the points at which inputs are received and outputs exported from and to its environment.

The functionally dominant process of the system is the overall transformation that converts inputs to outputs.

Recall that we defined technology as knowledge of a manipulation of nature for human purpose. And for a physical technology, one finds both a physical process and a functional transformation in the technology: 1. Natural phenomenon—physical states and processes 2. Manipulation of natural states according to the logic of a functional transformation

3. Human purpose defining the functional capability of the technology All physical technologies are based on natural phenomena. For example, in flight, it is the natural phenomenon of the physics of airflow over an overhead propeller (helicopter) or past a horizontal wing (fixed wing) that provides lift. The physics of helicopter lift is simply action-reaction, whereas the physics of airplane wing-lift is the differences in air pressure as the air flows over the curved upper-part of the wing and over the flat lower part of the wing. Different ways to manipulate natural states can produce different versions of a technology (e.g., helicopter and airplane versions of flight technology).

In addition to being based on nature, all technologies are also based on human purpose. The reason one manipulates nature in a technology is to achieve a purpose, to achieve a human goal. For example, the purpose of flight technology is for transportation through the air. In technology, human purpose is achieved through a capability due to a manipulation of nature by means of a process of logic for the purpose of a functional transformation.

Any physical technology can be described as a specific process ordering a physical morphology (physical form as structure and process). For example, the powered flight technology of an airplane can be described as the process of air flowing over the shape of the wing—creating a differential pressure above from below the 226

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wing surface—to provide a lifting force for flight. Any software technology can be described as a specific process ordering a sequential performance of logical operations. For example, the technology of the CPU chip of a computer can be described as a Von Neumann computer architecture processing input data into output, according to a stored program as system control.

Figure 11.2 illustrates any technology as an open system with its environment being an application of the technology. In an airplane, the application environment is the air and sky; in a computer, the application environments are the use of the computer for analysis, control, communications, etc.

The technology-system process transforms the states of the system according to the transformation logic of the process to render outputs from inputs. For example, in an airplane, the system process is the pressure of airflow to create the output of lift for flight; in a computer, the system process is the clock cycling of the logical operations of the Von Neumann architecture.

The technology-system control maintains the system process within bounds of functional performance. In an airplane, the system control process consists of the manipulation of flight controls of the wings and rudder; in a computer, the system control is the stored program instructing the computational operations.

The technology-system application control alters the system control according to application performance. In an airplane, the application control is a pilot operating the flight controls; and in a computer, the application-control is the selection of a program to operate by the person using the computer.

A specific configuration for a technology transformation is a given technology system. For example, fixed-wing configuration and rotating-wing configuration provide two different configurations of the technology of flight—airplane and helicopter technologies. In the case of a computer, the specific configuration of a computer differentiates computer products as laptops or servers or routers or super-computers.

A technology system is a configuration of parts that operate together to provide a functional transformation for a given application. The boundaries of a technology system are the points of the system that receive inputs and export outputs into the application environment.

Application environment

Technology system

Input

Output

process logic

-natural states-

Information

System

feedback

control

Application

control

Figure 11.2 Configuration of a technology system TECHNOLOGY

SYSTEM

227

For example, take the familiar automobile, which can be viewed as two connected technology transformations—power and motion. The technology system of energy conversion in the automobile can be viewed as an open system with four boundary points of the energy system in the automobile:

Inputs: The fuel tank inlet and the air filtration input Outputs: Exhaust and radiator

The motion transformation of the automobile can also be viewed as an open system with five boundary points of the motion system in the automobile: Input: Four tire contact points with the ground

Outputs: Air space immediately in front of the automobile CASE STUDY:

Illumination

We next look at an example of the historical progress for incremental progress in a physical technology over time—illumination technology, as plotted in Figure 11.3

This chart shows the improvements of energy efficiency (lumens per watt) of (1) the incandescent lamp technology; and one notes that the shape looks like a lazy kind of “S”—a technology S-curve. In the early years of the technical progress from 1889 to 1909, the rate of improvement in technical performance was exponentially increasing with time. From 1909 to 1920, further progress was linear with time. Then finally, the rate of technical progress turned into an asymptotic region, approaching a finite limit to progress, during the period 1920 to 1960.

New technology:

Conducting organic

polymers

100

Technology

performance

Fluorescent

parameter

lamps

att

10

Incandescent

Scientific

lamps

phenomenon

Lumens per w

Scientific

mechanism

11880 1900 1920 1940 1960 1980

Year

Figure 11.3 Technology S-curves for lighting technologies 228

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The first point on the graph is at 1 lumen/watt in 1889, the efficiency of either Edison’s or Swann’s bulb. As we noted earlier, this first lighting technology of the electric light bulb used a carbon filament, and this was invented independently by the American, Thomas Edison, and the Englishman, Joseph Swan (1828–1914).

Also shown is a second technology S-curve (2) for fluorescent lighting. And there is shown a composite technology S-curve for both incandescent and fluorescent light technologies. We recall that GE improved the incandescent light technology by inventing the tungsten filament at 11 lumens/watt. The intrinsically higher efficiency of fluorescent lamps (80 lumens/watt) over incandescent lamps was due to the difference of the physical phenomena in the two technologies.

The recently new technologies of light-emitting diodes (LEDs) and organic light-emitting diodes (OLEDs) were innovated with higher lighting efficiencies.

These technologies developed as solid-state electronic technologies progressed in the second half of the twentieth century. A composite S-curve for all technologies extends the performance efficiencies for lighting technologies to higher levels, with LED efficiency as high as 150 lumens per watt.

In each of the four lighting technologies, different physical phenomena were used as the scientific bases: (1) conduction of electrons through metals, (2)

conduction of electrons through gases, and (3) conduction of electricity in semi-

conducting materials and along conducting organic polymers.

Accordingly, the technical efficiency differed for each technology due to the intrinsic phenomena of generating light from electronic passages. In 2010, a new lighting technology was technically envisioned, using quantum-dot scientific phenomena ( Economist 2010)

PHYSICAL TECHNOLOGY S-CURVE

In general for any physical technology, one can plot the rate of change of a performance parameter over time and will likely see a kind of S-shaped curve, such as in Figure 11.4. This common pattern has historically occurred for physical technologies and is called the technology S-curve. It has been used as a basis for extrapolative forecasts of technology change (Twiss 1980, Martino 1983, and Foster 1982). In a technology S-curve, there are three periods of the rate of progress of technology: 1. An early period of new invention, exponential in temporal form 2. A middle period of technology improvement, linear in temporal form 3. A late period of technology maturity, asymptotic in temporal form A technology S-curve is a common pattern of progress in a technology’s principal performance parameter over time, with an initial exponential growth, intermediate linear growth, and an eventual asymptotic limit.

PHYSICAL TECHNOLOGY S-CURVE 229

Natural limits

echnology performance parameterT

New

Technology

Technology

invention

improvement

maturity

period

period

period

Figure 11.4 General form of the technology S-curve for physical technologies The exponential portion of the physical technology S-curve occurs in the beginning of technology development, just after the basic invention. Then when some people see the new technology, they immediately have ideas how to improve it. In any physical technology, the first invention is crude, unsafe, and limited in performance. Immediately, many other inventors have ideas to improve the new technology. This is the beginning exponential portion of the physical technology S-curve.

This temporal part of the S-curve is mathematically an exponential form, because such initial growth consists of events that have a growth rate proportional to the currently existing state.

The mathematical expression of an exponential growth in the beginning of a technology S-curve assumes that new ideas for improving a new technology will be proportional to an existing set of ideas in the beginning of the new technology.

But since the creation of new ideas is not necessarily proportional to the quantity of preexisting ideas, one must take the technology S-curve formula not as a model of technological progress but as a historical analogy. However, it is true that for a brand new technology, when ideas are new they stimulate new thinking and thus other new ideas. This analogy of the technology S-curve states that there likely will be an explosion of new thinking, which can roughly be described as exponential.

When the ideas of a radically new technology are so exciting and intriguing, then many new people start to think about how to improve it.

The linear portion of the technology S-curve occurs when incremental improvements in technology are being made. Historically, many incremental improvements gradually improve the performance of the technology. Since all physical technology 230

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is based on natural phenomenon (in which a sequence of natural states is selected in manipulating nature), the ultimate limit of the technology’s performance will be determined by nature (in terms of the highest performing natural state reachable).

This is the natural limit of any physical technology. In the technologies of illumination, the limit for progress in technical was fixed by the natural states of electrons passing through metals and through gases. This is an important point because it points to a limit to technology.

Technology neither creates nor destroys nature but only alters nature; and the potential of any technology is ultimately limited by nature.

In using a technology S-curve to forecast the rate of technological progress, the most important parameter to try to estimate is the natural limit. And the ability to estimate the natural limits to a technology lies in the detail of understanding of the natural phenomenon of a technology—the science base of the technology.

Science provides detail through the quantitative modeling of the physical processes of the phenomenon that a technology uses. A scientific model allows prediction of behavior of a phenomenon. To obtain forecasts, a manager of technology needs to consult the research community studying the science base of a technology.

Underlying any physical technology is a physical phenomenon. Underlying the Edison lamp bulb was the physical phenomenon of electron conduction through metals (which generates heat and light from collisions with the metal atoms). The intrinsic limit to technical performance of a physical technology is determined by the natural physical phenomenon of the technology. Technical progress in a physical technology based on one physical process will show the continuity of form along an S-curve shape. However, technical progress that occurs when a different generic physical process is used will jump from the original S-curve to a new and different S-curve.

A physical technology S-curve is expressive of the underlying physical phenomenon.

The linear portion of a physical technology progress is due to improved manipulation of the mechanisms of the phenomenon.

TECHNOLOGY PERFORMANCE PARAMETERS

Technical progress can be measured in the improvements of its performance—

technology performance parameters. These parameters depend on the application for which the technology is used. The application of a technology identifies the activity to be performed by a customer in a context. For example, the electric TECHNOLOGY PERFORMANCE PARAMETERS 231

lamp had a lighting application. And the electronic control devices of the electron vacuum tube and transistor were initially applied to communications, telephone, and radio. An application will require certain capabilities of the technology.

There are seven ways of considering a technology’s performance for an application:

1.

Capability

2.

Performance

3.

Efficiency

4.

Quality

5.

Safety

6.

Resources

7.

Potential

The capability of a technology identifies the functional transformation of the technology that transforms inputs to the technology system into outputs. For example, the application of the tube and transistor to radio communications required the capability of these devices to provide signal amplification, signal detection, and signal oscillation generation.

The performance of a technology expresses how well the technology performs its capability of functional transformation. For example, one important performance measure of the electron tubes and transistors was their frequency response, the range of frequencies over which capabilities such as amplification could be used.

The efficiency of a technology is the amount of resources it consumes to attain a specified performance an application

The quality of a technology expresses how dependable is the technology in use.

Electron tubes were notoriously undependable, as the metal grids corroded over time, making the tube fail. The germanium transistor was undependable because its performance was temperature sensitive.

The safety

of a technology expresses how much danger arises from the use of the technology. For example, both electron tubes and transistors are relatively safe to use, since neither bursts into flames during use.

The resources used by a technology express what kind and how many supplies are consumed by the performance of the technology. In the example of the electron tubes, they required a heater current to stimulate electrons to leave the cathode that consumed electrical power. Transistors did not require such a heater element and so consumed much less electrical power in use.

The potential of a technology expresses the possibility of future improvement of the technology. For example, the size of the electron tube was reducible from the size of an apple to the size of a peanut but little further. In contrast, the size of the transistor was reducible from the size of a raisin down to that of a bacterium (microscopic) and even smaller.

In a functional description of a physical technology, one can describe the different ways of performance, and measuring one of these provides a performance 232

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parameter of the technology. A change in physical phenomena (e.g., change from electron conduction in a vacuum to electron conduction in a solid, going from the tube to the transistor) changes the functional parameters. Also, the structure of the physical morphology (e.g., npn or pnp structure of a transistor) affects the performance parameters.

Physical technology performance parameters express the functionality of the technology system based on a physical morphology of the technology.

SOFTWARE TECHNOLOGIES

The strategy for improving physical technologies has been to go smaller on a physical scale. This going smaller creates technical progress in the linear realm of the technology S-curve, until finally a limit of nature is achieved. What about improvement in software technology? Software does not depend on physical processes (physical nature) but on cognitive processes (social nature). Software cannot be improved by an analogy to physical technology—by going smaller in physical nature. For this reason, software technology reaches its natural limit when the logic in the software is completed. We can illustrate this comparison in Figure 11.5.

While progress in physical technology shows an S-curve in progress over time, progress in software shows no S-curve. Instead, progress in software technologies occur as in the shape of a “step-function.” The reason for this step approach to Physical technologies

Natural limits

Linear portion in

physical

Phenomenon and mechanisms

technologies is

of physical technologies

due to the ability

to go to smaller

scales in

mechanisms

Information technologies

Information

technologies (IT)

progress by addition of

echnology performance parameter

features and

T

Logic and function

integration of

programs

New

Technology

Technology

No linear portion of it

invention

improvement

maturity

progress—only

period

period

period

completeness of

function

Figure 11.5 General form of the technology progress, comparing physical with informational technologies

SOFTWARE TECHNOLOGIES 233

software technologies progress is that the underlying nature of software is in the natural domain of language and logic. The underlying nature in physical technologies is the mechanism of nature—physics and chemistry. In software, the nature is that of the mind, of ideas—expressed in language and in logic. Progress in the software technologies does not depend on physical nature but human mental nature.

Progress in logic and language applications in software is due correctness of logic and completeness of logic. When software captures the logic of a thinking process correctly and completely, then the software progress is over—the step function is complete. Correctness and completion of the logic in software is the primary criteria for good software design.

Completeness, correctness, functional additions, and programs integration are the forms of progress in software technology.

CASE STUDY:

Moore’s Law

This case illustrates the first instance in the history of technical progress whereby the rate of progress occurred as a sequence of exponential phases, as technical progress occurred first in IC chips then through next-generation technologies. The historical context is the period from 1960 to 1990, during the early years of technical progress in production of IC chips. New manufacturing processes were devised to achieve increasing densities of transistors on a chip. Gordon Moore, then an executive at Intel, estimated a continuing pace of chip progress as a doubling of performance every two years. And Moore’s Law did come to pass—not as a natural law, but as a social law, when the chip industry planned the technological progress as next-generation technologies in chip production.

Figure 11.6 shows the density of transistors achievable on an IC chip over time. The technology performance parameter is the number of transistors that could be fabricated on a chip. Since transistors work as electronic valves and at least one valve is usually needed in a circuit for one logic step, increases in the numbers of transistorized valves on a chip correlate with increases in complexity of functionality for which a chip can be used. The graph is plotted on a semi log scale, and we note that this is not the form of the technology S-curve. The vertical axis of technical performance is on a log scale, so that the whole chart is in semi-log format. A straight line of a semi-log chart indicates exponential increase on the vertical scale. This means that progress in transistor density has proceeded in a succession of exponential stages—next generations of chip-processing technology.

Mathematically, a chart plotting a function as linear on a semi-log scale indicates that the function is exponential on a linear scale. We can see this by taking the logarithm of an exponential function f(t) ϭ ekt; or lnf(t) ϭ lnekt ϭ kt. Thus, 234

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1011

G

Physical

1010

D

technology

109

Series of

exponentials

F

108

E

Phenomenon—

107

C

providing processing

106

B

mechanisms

for smaller scale

105

104

Number of transistors per chip

103

A

Logical functions—providing added functionality

102

101

Information

technology

1960

1970

1980

1990

2000

2010

2020

Year

Figure 11.6 Technology progress in semiconductor chip transistor density lnf(t) ϭ kt. Plotting lnf(t) thus scribes a straight line in the temporal variable t with a slope of k.

The different slopes of the lines show different slopes k of technical progress in producing chips of higher transistor density. Although the rates of progress from 1959 to 1972 and from 1972 to 1984 were both exponential, the rates of exponential increase differed in the two periods. Therefore region A is one exponential region of increase in chip density. Region B is a second exponential region.

And region C is also an exponential region. Initially, from the invention of the chip in 1960 until 1972, the growth rate was such that on the average the density-of-transistors-on-a-chip doubled each year. However, in the second period from 1972

to 1984, while the growth was still exponential, the rate was such as to achieve an average doubling each 1.5 years.

The five data points at 1960, 1972, 1979, 1980, and 1981 are actual historical data and show that technical progress in IC chips occurred through next-generation technology innovations rather than incremental innovations. In 1970, the feature size of transistors was about 12,000 nanometers wide. (One nanometer is one billionth of a meter; by comparison, a human hair is about 100,000 nanometers wide.) In 1980, feature size was down to 3,500 nanometers, and in 1990, 800 nanometers. In 1997, feature size was down to 300 nanometers. This reduction in feature size was allowing the production of chips with greater densities of transistors.

The historical era when hundreds of transistors could be placed on a chip by 1970 was called the time of “middle scale integration (MSI).” After 1972, densities of transistors on a chip leapt to thousands of transistors on a chip, large scale integration (LSI). By 1981, densities of transistors increased to tens of thousands of transistors on a chip, very large scale integration (VLSI). During the 1980s, chip densities increased to hundreds of thousands. As the 1990s began, chip densities were in the millions of transistors on a chip—ultra large scale integration (ULSI).

What would be the natural limits of silicon chip technology? In the 1983 meeting PLANNING TECHNOLOGY DISCONTINUITIES 235

of the International Electron Devices, James Meindl, then of Stanford University, predicted: “It could happen by the turn of the century” (Robertson 1984, p. 267).

At that meeting it was anticipated that the density limits could be in the billions of transistors per chip. And later in 2009, the anticipated date for reaching that limit was estimated by Intel officials to be about 2020.

Historically, it is interesting that this estimate was presented in 1983 by Dr.

James Meindl (at that time, director of Stanford University’s Center for Integrated Systems) at a research meeting composed of expert researchers in chip technology (the third annual International Electron Devices meeting, which was held in Washington, D.C., in December 1983):

Projection of the future course of semiconductors has become a liturgical requirement of integrated circuit meetings since Gordon Moore of Intel Corporation . . . in the mid 1970s . . . observed that the number of transistors on a chip had been roughly doubling each year since Texas Instruments and Fairchild Semiconductor independently developed the integrated circuit in 1959 (Robinson 1984, p. 267).

This chart for chip progress (Moore’s Law) was not just accurate technology forecasting but also industrially committed technology planning. Even as late as 2010, chip executives were anticipating the density of transistors in silicon to level off after 2020.

PLANNING TECHNOLOGY DISCONTINUITIES

Technical progress in IC chips had occurred in jumps as technology discontinuities, with each discontinuity planned by the industry. Progress was in new generations of chip processing technologies that jumped the density of transistors on a chip by an order of magnitude (ten times density of transistors) from one IC chip generation to the next. Every technology S-curve depends on a phenomenal base, and when a new phenomenon is substituted, a new technology S-curve begins. The discontinuous technology S-curves for changes in the phenomenal bases of technology are some of the most important information that technology forecasting can provide for business and competitive strategies.

Historically, most technologically based failures of businesses have occurred at such technology discontinuities; and conversely, most successful new high-tech businesses have been started at such technology discontinuities.

Therefore, there are two kinds of technical progress. The first is incremental progress along an S-curve as improvements are made in a technology utilizing one physical process. The second is discontinuous progress, jumping from an old 236

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S-curve to a new S-curve as improvements in a technology use a new and different physical process.

When progress in innovation has occurred through a substitution of a new underlying physical phenomena for the technology, then a chart of the performance parameter’s rate-of-change will jump onto a new S-curve. A technological discontinuity occurs as progress in the form of “next generations of technology.” Incremental technological progress in physical technologies always involves progress using the same underlying physical phenomenon. Discontinuous technological progress in physical technologies involves progress though replacement of one underlying physical phenomenon with another one. The commercial implication of incremental and discontinuous technological progress is in how these differently affect the competitive structure of an industry.

Incremental technological progress reinforces an industrial competitive structure; but discontinuous technological progress disrupts an industrial competitive structure (competitive discontinuities).

The series of jumps in progress in transistor-density of IC chips was possible because the production processes for producing the chips was planned as a system.

Both incremental and discontinuous (radical) technology progress can be planned because all technologies are systems.

Viewing a technology as a system is the key to planning technological progress.

TECHNOLOGICAL PROGRESS

Continuing improvements in a technology occur by way of further inventions to improve the technological system. This requires identifying potential or actual opportunities for technological advances in either the logic schematic of the functional transformation or in progress in the physical phenomenon and physical morphology.

Technology advance may occur in three ways:

1. Extending the logic schematic

2. Alternate physical morphologies for a given schematic 3. Improving performance of a given morphology for a give schematic by improving parts of the system

Technical progress may occur by adding features and corresponding functional subsystems to the logic. Technical progress may occur in alternate physical processes or structures or configurations for mapping to the logic. Technical progress may occur in improving parts of the system, its subsystems or components or connections TECHNOLOGICAL

PROGRESS

237

or materials or energy sources or motive devices or control devices. Thus viewed as a system, technical progress can occur from changes in any aspect of the system: 1. Critical system elements

2. Components of the system

3. Connections of components within the system

4. Control subsystems of the system

5. Material bases within the system

6. Power bases of the system

7.

System

boundary

A technology system cannot be innovated until all the critical technology elements for the system components—connections, control, materials and power—already exist. For example, the steam engine was a necessary critical element before the railroad could be innovated. The gasoline engine was a critical system element before the airplane could be innovated. The vacuum electronic tube was a necessary element before radio and television could be innovated. The vacuum tube and the ferrite core memory were necessary elements for the general-purpose computer.

Critical elements are the building blocks for a system’s components or connections or control or material or power bases.

Technical progress in a system may occur from further progress in the components of the system. For example, over the early thirty-year history of the computer technology, rapid and continual progress in the central processing units, in memory units and in storage units drove the technological progress of the computer.

Technical progress in a system may also occur in the connections of the system.

For example, in the telephone communications industry, fiber optic transmission lines began to revolutionize that industry in the 1970s. As previously noted, for computers, in the 1980s the development of local area network connections and protocols and the fiber optic transmission systems also contributed to the continuing revolution of the computer industry.

Technical progress in a system may also occur in the control systems of a technology. For the automobile industry in the 1980s, technical progress was primarily occurring in developing electronic control systems. In computers, major technical progress had occurred in developing assembly languages, higher-level cognitive languages, compiler systems, interactive compiling systems, and artificial intelligence languages.

The material bases in the framework of a system, in the devices of a system, in the connections of a system, or in the control of a system are also sources of technical change and progress. An example of technical progress in the frame was the beginning in the 1980s of the substitution of organic composite materials for metals in the construction of airplanes. An example of advances in materials in a device was the substitution of silicon for germanium in the new transistor in the 1950s, and in the 1990s in the attempts to substitute gallium arsenide for silicon in the semiconductor chips.

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Finally, energy and power sources may provide sources of advance in a technology system. The classic example is the substitution of steam power for wind power for ships in the nineteen century, and in the twentieth century the substitution of nuclear power for diesel power in the submarine. In the twenty-first century, emphasis was in expanding sources of wind and solar-powered energy as well as more nuclear energy.

The practical use of these ideas provides a viewing of any technology as a system of manipulation. Invention of a technology requires the creation of a functional scheme mapped to sequences of states of a natural phenomenon. Technological progress in a technology requires improvements in the system of the technology.

Technology systems are innovated as the basic system ideas in the design of product systems, production systems, operations systems, information systems, communication systems, capital systems, or customer application systems.

SYSTEMS ANALYSIS FOR ALTERNATIVE TECHNOLOGY

CONFIGURATIONS

Technology strategy can be formulated by examining alternative physical configurations of a technology system. This case is an example of a formal technique in technology-push planning called a morphological analysis. We recall that the term morphology literally means the “science of form,” as morphe is the Greek term for form. However, we have been using this term of physical morphologies to indicate the mechanical structures and processes of the physical phenomenon of the configuration of a technology system. We have been using the term for historical reasons, dating back to 1948 when F. Zwicky proposed his technique that he called morphological analysis (Zwicky 1948). Zwicky wrote that one can systematically explore technology-push sources for technical advance in a system by logically constructing all possible combinations of physical alternatives of the system.

Zwicky said that first a clear statement of the function of the technology must be made. A general logic scheme of the functional transformation must be designed.

Then a list can be made of all the physical processes mapable into the logical steps.

Morphological alternatives of possible alternate configurations of a technology system can then be searched by all conceivable variations on the functional architecture of the system and of all possible physical substitutions in the functional transformational steps. This requires an abstraction of the physical configuration of the technology system in terms of components, connections, materials, control, and power. One then makes a list of all conceivable alternatives of each of these as different physical processes. One then makes a list of all possible combinations for alternative constructs.

Zwicky used the example of alternative configurations for jet engines, identifying eleven major system elements:

1. Intrinsic or extrinsic chemically active mass

2. Internal or external thrust generation

SYSTEMS ANALYSIS FOR ALTERNATIVE TECHNOLOGY CONFIGURATIONS 239

3. Intrinsic, extrinsic or zero-thrust augmentation 4. Positive or negative jets

5. Nature of the conversion of the chemical energy into mechanical energy 6. Vacuum, air, water, earth

7. Translatory, rotatory, oscillatory, or no motion 8. Gaseous, liquid, solid state of propellant

9. Continuous or intermittent operation

10. Self-igniting or non-self-igniting propellants (Jantsch 1967, 176) Zwicky computed that all possible combinations of these features would be 36,864 alternative combinations. Many of these alternatives would not be technically interesting, so Zwicky performed this evaluation in 1943 with fewer parameters and reduced his alternatives to only 576 combinations. (Zwicky’s procedure in its full combinatorial form is cumbersome.)

Technology-push research opportunities can be planned through a morphological analysis of possible alternative physical configurations of a technology system. To summarize the procedure for conducting a morphological analysis of the physical aspects of a technology system, first the functional context analysis of a technology system be examined:

1. Identify the major generic applications of product/production/service systems that embody the technology system.

2. From the applications perspective, identify desirable improvements in performance, features, and safety that would be recognized by the users (final customers) of the product/production/service systems.

3. Determine the prices that would expand applications or open up new applications.

4. Determine the factors that facilitate brand loyalty when product/production/

service systems are replaced in an application.

Next, abstract the principal parameters of the desired functional transformation to meet the functional requirements for use of the technology. Different logical combinations of these parameters can be examined (that are mutually physically consistent): 1. Begin with an existing boundary and architecture that provides the functional transformation defining the technology.

2. Use the applications system forecast to classify the types of applications and markets currently being performed by the technology system and also to envision some new applications and markets that could be performed with improved performance, added features and/or lowered cost of the technology system.

3. Identify the important performance parameters and their desirable ranges, desirable features, and target cost to improve existing applications and to create new applications.

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4. These criteria then provide constraints and desired ranges under which alternative morphologies of a technology system can be bound.

5. Examine critical elements of components and connections whose performance would limit the attainment of the desirable performances, features and cost.

6. The performance of critical elements that do not permit desired system performance can then be identified as technical bottlenecks.

7. Examine alternative base materials and base power sources for alternative morphologies, again selecting only those that would fulfill desirable system performance.

8. Examine alternative architectural configurations and alternative types of control that might attain the desired higher performances.

9. Combining and recombining various permutations of critical elements, base materials and power, and alternative architectures and control systems provide a systematic way of enlarging the realm of possible technology developments.

KEY IDEAS

In the cases of:

• Wright brothers’ flyer

• Illumination Technologies

• Moore’s Law

We have examined the key ideas of:

• Technology system,

• Technology S-curve

•

•

•

Natural phenomena underlying a technology

Differences in hardware and software technology progress Systems analysis of alternative configurations

12

PRODUCT SYSTEMS

INTRODUCTION

We have looked at invention and systems of technology. Let us now look at products—

products systems. Product systems divide into hardware product systems and software product systems. They differ, because technology in hardware is primarily physical, whereas technology in software is primarily linguistic. It is important for a manager of innovation to understand these technical differences for successfully managing product innovations—either as a good or as a service (hardware or software).

First, we look at hardware innovation and ask:

• How can one describe a product as a system?

• What is the relationship of a technology system to a product system?

Second, we look at software innovation and ask:

• What is the science base of information technologies?

•

•

What kinds of logics and languages are used in information technologies?

Why are there bugs in software design, and how does a software developer find them?

Hardware and software innovations differ from their respective science bases in physical processes and in linguistic processes. In Figure 12.1, we indicate how the innovation of a product system focuses both on science and on technology in the stages of the innovation process.

SIKORSKY’S HELICOPTER

We described the basic invention of powered flight by Wilber and Orville Wright.

Afterward, many other inventors began improving the airplane. But Igor Ivanovich Sikorsky invented a completely different configuration of a flight system—a 241

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Innovation in hardware and in software

Research

Invent

Commercialize

Market

Nature

Science

Technology

Economy

Utility

Exist

Discover

Manipulate

Embed

Use

naturally

nature

nature

nature

nature

The world as material

The world as money

Figure 12.1 Innovation process

new product—the helicopter. This was an example of technological progress in adapting the invention to a new application—vertical takeoff and landing of powered flight. The difference between the Wright brothers’ fixed-wing airplane the Sikorsky’s rotating-blade helicopter nicely illustrates the idea of a technology as a system, and of different configurations of a system designed as different products.

Sikorsky was born in Kiev, Russia, in 1889, and as a child, he had showed the technical bent of an inventor (Wohleber 1993). At the age of fourteen, Sikorsky entered the Imperial Russian Naval Academy in St. Petersburg. After three years, he decided he did not wish to become a naval officer and quit to enroll in the Kiev Polytechnic Institute. In the summer of 1908, he visited Germany and learned of Count Zeppelin’s dirigibles and of the powered flights of Orville and Wilber Wright.

Then the Wright brothers were touring Europe with their amazing demonstration of powered flight. Sikorsky determined that his life’s work was to be flight. Flight was truly a new functional capability for humanity.

Igor Sikorsky

(http://en.wikipedia.org 2008)

SIKORSKY’S HELICOPTER 243

From the beginning, Sikorsky wanted to do something different from the Wright brothers’ type of airplane. He resolved to design a flying machine that would be capable of “rising directly from the ground by the action of a lifting propeller.” His idea came from a childhood memory of his mother’s descriptions of Leonardo’s DiVinci’s designs for flying machines. In a German hotel room in 1908, he put a four-foot propeller on a small engine and found it could achieve a lifting force of eighty pounds for each horsepower the engine could provide.

Back in Kiev, he built two helicopter prototypes, but the French-built, three-cylinder 25 horsepower engine he had purchased did not provide sufficient power to lift the helicopter. He turned then to constructing fixed-wing aircraft. With his fifth prototype, he got one to fly. He then built a sixth prototype, a three-seater bi-wing plane that flew at 70 miles an hour while carrying a pilot and two passengers. He built the world’s first four-engine aircraft; and in May 1913, he flew the four-and-a-half ton plane above St. Petersburg. He then built a refined, larger version of this. And when the First World War began, he was put in charge building military versions for long-range reconnaissance and bombing raids. By 1917, he had produced seventy planes of his Il’ya Muiromet.

Il’ya Muiromet’

(http://en.wikipedia.org, Sikorsky 2008)

In 1917, the Czar of Russia, Nicholas II, abdicated his throne. Communists seized power in Russia. Many Russians were forced to flee the communist dictatorship, and Sikorsky left in February 1918 (leaving behind the money he had made). He traveled to France and presented the French with a plan to build bombers. The French government accepted them, but the war ended before Sikorsky could begin production.

In March 1919, Sikorsky traveled to the United States. For the next few years, he barely earned a living as a teacher. In 1923, he started a new airplane firm and built an all-metal passenger plane with two 300 horsepower engines. (It was eventually sold to a film maker to be used as a prop; disguised as a German bomber it went down in flames in a Hollywood film.) In 1928, Sikorsky built a large amphibious plane, the S-38, which sold well. The largest customer was Pan American Airways,

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which flew 38 of these on their Latin American routes. Pan American called these the American Clipper. Sikorsky then built larger versions, and the S-40 was the first to cross the Pacific. Sikorsky sold his company to United Aircraft in 1929.

Sikorsky’s Pan Am Clipper

Sikrosky’s Helicopter

(http://en.wikipedia.org 2008)

In 1938, Sikorsky went back to his first technical love and began working on a design for a helicopter for United Aircraft. He patented a design that used a larger horizontal propeller for lift and a small vertical one on a tail to control torque. The technical problem then was to provide control. (The fixed-wing aircraft, properly designed, provides relatively stable flight dynamics, compared to the helicopter.) It wasn’t until 1940 that Sikorsky was able to develop a prototype that could hover steadily. Then the U.S. Army became interested in the project. On May 6, 1941, Sikorsky demonstrated a new prototype, the VS-300, to reporters and military representatives. Sikorsky took the helicopter to a stationary hover and remained there for one hour and thirty-two minutes. But the helicopter still flew forward too slowly. Yet it was enough for a military R&D contract. The Army wanted a higher forward speed and insisted that all control be in the main rotor and that there be an enclosed cockpit with a passenger seat.

In 1942, Sikorsky demonstrated a military version, the XR-4, which had a 165

hp engine and could hover and travel forward at a speed of 75 mph. Then Sikorsky began producing helicopters for the Army as model R-4, R-5, and R-6. By the end of World War II, four hundred military helicopters had been produced. In March 1944, an R-4 was used for the first military rescue mission, lifting an American pilot and three wounded British soldiers from a crash site in a rice paddy in Burma.

Later during the Korean War, helicopters would be extensively used for rescue.

And later during the Vietnam War, helicopters would also be used to deploy ground troops under combat conditions.

Sikorsky retired from Sikorsky Aircraft in 1957 at the age of sixty-eight. But he remained a consultant for the continuing development of helicopters until he died in 1972.

For the innovation of the helicopter, the following conditions were required: (1) the prior invention of the gasoline engine, (2) the prior invention of fixed-wing HARDWARE PRODUCT SYSTEM 245

powered flight, (3) the development of sufficiently light and powerful engines to power a helicopter, (4) the dedication of an inventor to solve the practical problems of controlled hovering and flight, and (5) the sponsorship of a military customer to fund the R&D required to perfect a practical prototype for application.

All technology systems require a control subsystem. For example, a helicopter control subsystem consists of the lifting, steering, acceleration, flight, decent.

Controlling actions consist of a planned route, ignition, starting, flying, and landing. To accomplish this location transformation, the helicopter has two separate and parallel control subsystems that need to be coordinated at all times of operation: the energy transformation process and the power-applied and flight processes.

As the helicopter is lifted, hovered, flown, and landed, the energy transformation must be controlled in exact coordination as fuel and air are injected into the engine, ignited, and provide torque for the lifting propeller and the lateral-stabilizing propeller.

HARDWARE PRODUCT SYSTEM

We see in the example of the helicopter as a technology that the physical structure and processes are complex; whereas the control is relatively simple. The physical structure requires a turbine engine for high-speed rotation of top-mounted large propellers. Smaller side-mounted propellers turn to provide a counterforce against the helicopter’s tendency to counter-rotate to the top-mounted propellers rotation.

And the top-mounted propellers must be capable of being tilted—to provide dynamic control for the helicopter to lift-and-hover or to fly forwards. And this control is made by a pilot operating a joystick type handle to fly the helicopter. In the control of the flight of a helicopter, the complicated logic in the control is in the pilot.

Hardware systems are complicated in physics (physical forms and processes), but relatively simple in logic (control).

However, as automation for operations is designed into a hardware system, then logic in the software for sensing and control becomes also complex.

As intelligence is designed into hardware systems for sophisticated control, then “smart hardware” becomes complex in both physical forms (physical morphology) and in logical forms (software).

Just as a technology is a system, so too is a product a system (and software a system). And the integration two systems, products and software, is itself a system—a service system. This is getting complicated: (1) technology as a system, (2) product as a system, (3) software as a system, (4) service as a system. But understanding this complexity—these different kinds of systems in which technology is implemented—is essential to properly manage technological innovation.

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The systems concept is essential to think clearly about “complexity” in the ideas of technologies, products, software, and services.

Innovation in the design of new products (or software or services) embodies new technology into new economic goods/services.

We recall that technology invention for physical technologies requires mapping logical transformations to physical processes—is the heart of technological invention—mapping function to form. But this mapping of function to form is also a central point of hardware-product design.

The configuration of the technology system indicates only the principal necessary for the functional transformation of the technology. But the configuration of a hardware-product system, embodying the technology, must add (1) secondary steps and processes needed to fill in the details and fully connect the principal logic steps and (2) other secondary technology systems to complete the product.

The difference between a technology system and a product system are (1) in the detail and fully connected steps of the core-technology in the product system, (2) in completing the product system with necessary secondary technologies.

Technical progress implemented in designed products/service can progress not only by improvements in physical form (morphology) but also by new functional logics (to handle increased functional complexity made possible by progress in morphology).

The more information technology designed into a hardware product, the more complex becomes the logic embedded in the hardware product—smart hardware.

SYSTEMS ANALYSIS IN HARDWARE-PRODUCT DESIGN

To embed a technology system into a hardware product design requires a system analysis of the product functioning. The product must be viewed as an engineered system with a functional logic mapped into a product morphology. And in engineering jargon this has been called a “function/form” challenge in design. The function is the logic of performance in a hardware product. The physical form are the materials and physical parts and their physical connections in the design.

A systems analysis for a product /service design lays out (in a flow diagram) the logical transformations as a series of both parallel and sequential operations of the product functioning. In that analysis one can identify the locations of technological innovation within the system as:

• Boundary

• Architecture

• Subsystems

SYSTEMS ANALYSIS IN HARDWARE-PRODUCT DESIGN 247

• Components

• Connections

• Structural materials

• Power sources

• Control

Accordingly, design targets for implementing new technology into the product system should be expressed in terms of an engineering systems analysis of the product system, identifying loci of innovation and estimating the effects on the functional performance parameters of the product system.

For example, airplane design is an example of a product wherein systems analysis has been systematically used. The principal technology system physical form (physical morphology as the physical structure and physical process) is partitioned into airframe and engine. The configuration specification states operating parameters for the plane system: load capacity, top speed, fuel efficiency, rate-of-climb, range, acrobatic stress limits, damage tolerance, maintainability, and so on. These configuration specifications then provide both targets and constraints for (1) the airframe design and (2) the engine design. In the airframe design, choices are available to the designer about the shape of the airframe, its structural reinforcement, and the packaging of its exterior surfaces. In the engine design, choices are available to the designer about power, weight, features and dependability. Once the airframe configuration and engine design have been made for a plane design, then the next systems design problem is integrating the two by adding controls. This integration may lead to revisions of either design for final requirements for sizing and weight and performance (payload, speed, agility, stealth, etc.).

The six system questions for technical innovation in hardware product design include:

1. What are the loci in the product system for embedding the technical progress?

2.

What is the improvement in functionality and/or performance the technical advance will provide?

3. How will the customer recognize value in the applications for this improvement in functionality/performance?

4. How important will that be to the customer?

5. What are the technical risks of innovating that technical advance in the product system?

6. What is the appropriate tradeoff between technical advance and cost of that innovation which will be appreciated by the customer?

The important commercial question as to whether a given innovation is visible and important to a customer of the product.

Technical innovations in a product that are invisible to or unused by a customer produce no competitive advantage.

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CASE STUDY:

Early Programming Languages

We now turn to software-product innovation. Instead of having a science base in the physical sciences, software has a science base in logic and languages. We saw that the invention of the computer required the development of an algorith-mic logic, called the Von Neumann Architecture. (Heppenheimer, 1990) Next, progress in computational technologies also was created by the invention of new languages—programming languages. We now describe the case of the first high-level programming language—Fortran.

After the commercial innovation of the stored-program, electronic computer in the 1950s, programming on the machines required writing the programs directly in “computer language”—in which sentences are expressed in the binary mathematics of one and zero. For example, Lohr commented about those early days before the first high-level programming language, Fortran, was invented: Professional programmers at the time worked in binary, the natural vernacular of the machine then and now—strings of 1’s and 0’s. Before Fortran, putting a human problem—typically an engineering or a scientific calculation—on a computer was an arduous and arcane task. It could take weeks and required special skills. Like high priests in a primitive society, only a small group of people knew how to speak to the machine (Lohr 2001, p. 20).

With our human minds, we had to talk to those computers with their machine minds—but in a language computers understood. The idea of a “higher-level” language in which to write programs was a good idea, a language closer to the human mind than to the machine mind. The computer could compile the higher-level language program into a binary language program. In the early 1950s, John Backus, an IBM employee, suggested a research project to improve computer programming capability: “In late 1953, Mr. Backus sent a brief letter to his boss, asking that he be allowed to search for a “better way” of programming. He got the nod and thus began the research project that would eventually produce Fortran” (Lohr 2001, p. 20).

Backus put together a team to create a language in which to program calculations on a computer, using a syntax more easily learnable and humanly transparent than binary programming:

They worked together in one open room their desks side by side. They often worked at night because it was the only way they could get valuable time on the IBM 704 computer, in another room, to test and debug their code. . . . The group devised a programming language that resembled a combination of pidgin English and algebra. The result was a computing nomenclature that was similar to the algebraic formulas that scientists and engineers used in their work (Lohr 2001, p. 20).

SPECIALIZED LANGUAGES IN SCIENCE 249

This was the essential point of Fortran—that it look to the human mind of the scientist or engineer sort of like a familiar algebraic computation. Learning to program in Fortran could use a familiar linguistic process that the scientists/engineers had learned as students—how to formulate and solve algebraic equations.

For example, writing a program in Fortran first required a scientific programmer to define the variables which would appear as operands in the program—scalar variable ( A), vector variables ( Ai), or matrix variables Aij). Then an algebraic operation could be written that looks like an equation (e.g., Ai ϭ Bi ϩ Ci). Any algebraic operation could be automatically repeated by embedding it in repetition loop (called a “DO” loop in Fortran—e.g., For i ϭ 1 to 10, Do Ai ϭ Bi ϩ Ci).

One can see that the successful trick of developing computer programming language lay in making it more familiar to humans. Fortran was the first high-level language developed specifically to talk to computers. Many others followed with several computer programming languages invented from 1957 to 1995.

Fortran was released in 1957 as a syntactical computer language to write for-mulae for performing calculations. ALGOL was invented in 1958 as was FLOW-MATIC. In 1959, a syntactic language for linguistic inferences, LISP, was invented to assist writing artificial intelligence programs. Pascal was invented in 1972 to improve the structuring of programs. The first object-oriented program language (to further improve structuring of programs) was Smalltalk in 1980. In 1985, Cϩϩ

was a modification to C to provide a widely used object-oriented programming language. Visual BASIC was invented to add graphics to the BASIC language in programming. Java was invented in 1995 for performing operations independent of computer platform (and transmittable across the Internet).

All programming languages are specialized in linguistic syntax to facilitate communication between the human mind and the computer mind.

SPECIALIZED LANGUAGES IN SCIENCE

The invention of the computer created a new wave of development in languages, in addition to programming languages. It also created a new wave of development in logic. We saw in Fortran and other early programming languages that the invention of the computer stimulated the evolution of human languages beyond the normal languages of English, Arabic, French, German, and so on. Earlier, we also saw how that computer invention also fostered the evolution of human logics—such as the algo-rithmic form of computational logic in Turing’s Machine—implemented as the Von Neumann computer architecture. So science and technology together have fostered the development of both new logic and languages language in the human culture.

We next look at how science and technology have evolved different kinds of specialized languages. An early classification of types of specialized languages in science was developed by Charles Morris in distinguishing between syntax, 250

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semantics, and pragmatics. In his book, Foundation of the Theory of Signs, Morris argued that words were “signs” that stood for “thoughts,” stating that, “Every thought is a sign” (Morris 1938). In language, syntax is the form of an expression, semantics is the content, and pragmatics is the purpose for the expression.

Charles Morris (1903–1979) was born in Denver, Colorado. He gained a bachelor’s degree in engineering from the Northwestern University. He obtained a Ph.D.

in philosophy at the University of Chicago. He belonged to the Pragmatism school of philosophy, which included John Dewey.

In 2003, the author used these distinctions to identify different kinds of specialized formal languages developed in both science and technology by adding the idea of control languages to Morris’s earlier types of semantic languages and pragmatic languages (Betz 1993). Languages specialized by typology are shown in Figure 12.2.

A syntactic specialized language is a formal language focused on aiding the form of expression. In science, mathematics provides the syntactic language for quantitative expression of physical theories. For example, in Isaac Newton’s creation of mechanics, he had to invent the new mathematical language of differential calculus to express quantitatively the idea of instantaneous velocity ( v ϭ dx/dt).

Syntactic specialized languages enable a sophistication of communication about the forms of communication—such as mathematics provides forms for expressing the quantitative aspects of phenomenon.

A semantic specialized language is a formal scientific language focused upon aiding the elucidation of a field of experience. The complete set of terms (such as mass, energy, place, velocity, or acceleration) and theories (such as F ϭ ma).

Newtonian mechanics can all be viewed together as a language specialized to express the content of physical nature. And these semantically specialized terms can

Content

Form

Semantic

Syntactic

Experience

language

language

Pragmatic

Control

Value

language

language

Figure 12.2 Specialized languages in science SPECIALIZED LANGUAGES IN SCIENCE 251

be further written in mathematical syntactical forms. For example, the physical concept of mass is expressed mathematically as a scalar number m. The physical concept of velocity is expressed mathematically as a vector v—needing to be expressed quantitatively (syntactically) with three scalar numbers, v 1 and v 2 and v 3

as: v ϭ ( v 1, v 2, v 3). The scientific discipline of physics is written in two specialized languages—semantic in physical content and syntactic in quantitative mathematical expression.

Semantic specialized languages enable sophistication of communication about things in an experiential field of existence—such as things of physical phenomenon, chemical phenomenon, or biological phenomenon.

A pragmatically specialized language is a formal language focused on aiding an area of purposeful activity. Any computer software program developed for an application is constructed as a pragmatically specialized language. Familiar examples are word processing programs (e.g., MS Word) or spreadsheet programs (e.g., Excel) or graphics programs (e.g., Power Point). An application program creates specialized linguistic terms (functional terms) for the tasks in an application and is programmed as operational commands. For example, in a word processing program, linguistic terms—such as “highlight,” “cut,” and “paste”—enable one to move script around in a text as an editing task.

Pragmatic specialized languages enable sophistication of communication about valuable and purposeful tasks—such as the task of writing as aided by an application software word-processing program.

A control specialized language is a formal language focused on expressing the writing of algorithms to control a process or system. For example, a computer operating software program (such as Unix) is constructed as a special control language for controlling computational operations in a computer. Another example is the programming language of Algol, developed in the 1950s to facilitate the programming of algorithms in controlling processes and procedures.

Control specialized languages enable sophistication of communication about the control of valuable and purposeful systems of activities—such as modeling and simulation programs.

The value of looking at some aspects of the progress in science and technology as a kind of development of formal languages is this: It makes clear the relationships between scientific disciplines. Some disciplines, such as mathematics, emphasize syntax. Other disciplines, such as physics, emphasize content. Other disciplines, such as computer science, emphasize pragmatics or control.

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CASE STUDY:

Xml and Ontology

As the Internet evolved, the computer programming language for Internet Web pages was “hypertext mark-up language” (html), which became the standard language for communicating text on the Internet. However, the communication of data as quantitative variables required extending mark-up language so that it was able to handle quantitative variables in formatted forms. This was called an extended mark-up language (XML). For example, in XML, variable data would be written in form (ϽvariableϾ47ϽvariableϾ), where the term within the brackets is the name of the variable and the number between the bracketed term is the quantitative value of the variable. The form of (ϽvariableϾ . . . ϽvariableϾ) provides a format for inserting quantitative data as different values of the variable.

To communicate the form and meaning of variables between two computers connected to the Internet, both computers must have access to the form and meaning of the variable. This requires a shared dictionary of the variables in XML, or a common registry. Since there cannot be a completely universal dictionary for all the possible variables in the world, registries must be constructed and maintained by a community of computers who wish to share information using a particular variable. Such a registry becomes a kind of shared ontology for that community of XML users. This term “ontology” is an old philosophical term that used to mean a science of “being” or existence. But for computer scientists it began to be used to mean the shared content, or semantics, of a sharing community. For example, as Henry Kim commented: “Shared understanding about a community—information that its members possess—is always applied in solving problems in that community.

The terminology used by a community’s members can be codified as a community’s (ontology). Ontologies, as ‘explicit representations of shared understanding’ can be used to codify (the community’s) . . . semantics” (Kim 2002, p. 52).

Kim explained that a large community’s use of XML will require that shared registries of an ontology of variables be developed: “For example, it must be assumed in using XML that the author and reader ofϽfooϾ7ϽfooϾ have the same understanding of what ‘foo’ means. . . . Ontologies can be adopted in situations where the capability to represent semantics is important . . .” (Kim 2000, p. 52).

What is interesting about this case of the emergence of XML as a standard for communicating formatted data on the Internet is the recognition by computer scientists that a community’s semantics—registries of ontology—would be required for XML to be widely used by a large community.

COMPUTER-ASSISTED NETWORKS

We have emphasized that the radical innovation process begins with scientists study nature, discovering natural phenomena and creating theory. Next engineers use scientific theory to assist in the invention of technology (scientific technology) and design such into products. Managers in the business then produce such products COMPUTER-ASSISTED

NETWORKS

253

Operating-system software: Control languages

Programing languages

Modeling software

syntactic languages

C1

semantic languages

Scientist

S1

T1

Nature

Observational measurement:

syntactic languages

Scientific

Theory:

discipline:

semantic

paradigm

languages

Model prediction:

semantic languages

Scientist

S2

T2

Theory

Engineering

Product

principles:

Networking

simulation

semantic

system:

software:

and

control languages

semantic

pragmatic

Software design aides:

languages

languages

pragmatic languages

Engineer

E1

P1

Product

Enterprise

Application

Schedualing

Production

integration

software:

C2

software:

control

software:

pragmatic

control

software:

pragmatic and

languages

languages

control

control

languages

languages

Manager

B1

M1

Revenue

Marketing software:

pragmatic languages

Figure 12.3 Radical innovation information network and sell them into a market to obtain revenue. In the modern world of information technology, different computer-assisted languages now facilitate the communications between scientists and engineers and managers. This is pictured in Figure 12.3 as a computer-assisted information network.

First look at the lines of the scientists to computers (the connection of S2 to C1); there one can indicate the importance of software for programming, programming languages, for humans (such as a scientist) to communicate with a computer. In the connection within a computer (C1 to C1), an operating system software—the operating system language—is necessary for the internal working of the computer’s mind. In the connection of one computer to another (C1 to C2), a networking system software—networking system language—is necessary. For an operating system or a network system to be shared and widely used, standards must be established for the syntax of the system, as indicated in the engineering to computer connection (E1

to C1). The connection of humans and human organizations (such as a business) to computer networks (B1 to C2) requires another kind of software, a search capability, as, for example, a browser language. Thus, several kinds of software languages are necessary for computer communications: programming, operating, networking, standards, and browser languages in specialized software.

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Semantic Specialized Language

Look vertically at the connection between things of nature (T1 and T2) as expressed in scientific theories of nature, and such theory is expressed as a semantically specialized language. The manipulative connection between things of nature and products (T2 and P1) can be described in semantically specialized languages of technology.

The value connection between market and products (M1 to P1) can be described in semantically specialized languages of utility.

Syntactic Specialized Languages

Look again at the information model, the observation connection between scientist and natural things (S1 to T1) is facilitated by syntactic languages, such as mathematics which enables the expression of the quantitative aspects of things.

The programming connection between scientist and computer (S2 to C1) is facilitated by syntactic programming languages, such as Fortran, Basic, Cϩϩ, etc. The industrial standards connection between engineering and computer (E1 to C2) is facilitated by syntactic standards in computerized communications. The search connection between business and computer (B1 to C3) is facilitated by syntactic browser languages.

Pragmatic Specialized Language

The disciplinary connection between scientist and scientist (S1 to S2) or the professional connection between scientist and engineer (S2 to E1) can be facilitated in communications that build disciplinary and professional knowledge—such as professional conferences, archival scholarly journals and textbooks. These activities are now being facilitated by specialized application software programs that facilitate conferences, the publication of online journals, and distance education. The enterprise connection between engineering and business (E1 and B1) is being facilitated by enterprise integration software and/or intranet software that utilizes application specialized languages.

Control Specialized Languages

Look at the prediction connection between science and nature (S2 to T2) and models enabling a quantitative prediction function are constructed in control specialized languages. So too is the design connection between engineering and products (E1

to P1) aided by design software that is constructed as a kind of control specialized language. The business to market (B1 to M1) sales connection is aided by production process control software to produce products and by customer relations software to assist in the sales of product, both of which are constructed as control specialized languages. In the vertical connections of the Figure 12.3 between computers (C1 to C1 and C1 to C2), the operating system connection and the network system connection are facilitated by software written as control languages.

LOGIC AND LANGUAGE IN SOFTWARE DESIGN 255

LOGIC AND LANGUAGE IN SOFTWARE DESIGN

Implementing new technologies in software development can be facilitated by generic strategies that think first about the language and logic basis of the proposed software

The first software design decision involved how much the language in the software is intended to process these different kinds of activities: inference, perception, planning, design, etc. We have seen that language for software can be categorized as principally syntactic, semantic, control, or application:

• Software that is linguistically syntactic focuses upon forms

• Software that is linguistically semantic focuses upon content

• Software that is linguistically control focuses upon processes

• Software that is linguistically application focuses upon tasks The first software-design decision in innovating new technology in new software is to decide the focus of the software—as principally on forms, content, processes, or tasks.

This focus will set the linguistic domain of the software:

• Syntactic software must be complete in all the forms of communication and internally consistent among the forms.

• Semantic software must be nominally and relationally complete in all the general things and their relationships in the field of existence.

• Control software must be complete in decision criteria for control of a process and temporally coherent in the sensing of the process.

• Application software must be complete in the tasks of an application and properly coordinated in task sequencing.

Also logic plays the important formal role of structuring reasoning in a language; and reasoning in a language requires proper logical forms for structuring:

• Grammar, inference, and standards

• Perception and analysis

• Planning and operations

• Design and transformations

The second software-design decision in innovating new technology in new software is to identify the kinds of logical structuring necessary for the software.

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There are several choices that can be made about which kinds of logic should structure the reasoning within a software design:

• Grammar, inference, and standards

• Perception and analysis

• Planning and operations

• Design and transformations

Implementing new technology in new software is a design challenge requiring design-decisions about the use of appropriate specialized languages and logics to be used in the software.

DESIGN BUGS

In comparison to hardware development projects, software design development projects have their own forms of technical risk. For example, Casper Jones studied of the distribution of project time in the different tasks in software creation (Jones 2002). In the actual coding for writing software, the time spent was usually less than a quarter of the whole project time. Beginning and up-front conceptualization of the software project also took on the average a quarter of the time. This means that design and coding together took about half of the time spent on developing the software. It also means that debugging the software took the remaining half of the time. In a survey of software projects in 1994, 8,000 projects were reviewed in 350 companies and only 16 of these were delivered on time and on budget (Leishman and Cook 2002).

Michael Evans, Alex Abela, and Thomas Beltz listed seven common reasons for failure to complete a software project on time and on budget: 1. Failure to apply essential project management practices 2.

Unwarranted optimism and unrealistic management expectations (not recognizing) potential problem areas

3. Failure to implement effective software processes 4. Premature victory declarations due to pressure to deliver timely software products

5. Lack of program management leadership willing to confront today’s challenges to avoid tomorrow’s catastrophes

6. Untimely decision making, avoiding making time-critical decisions until too late

7. Lack of proactive risk management to contain failures (Evans et al 2002, p. 16) Thus, it is due to the complexity in logic and languages that bugs occur in software design. All new innovative software will encounter technical problems—bugs.

And therefore in software design and development it is very important to deal with DESIGN

BUGS

257

technical bugs. For example, Douglas Clark described designing the Digital VAX

8800 family of mid-range computers announced in 1986: We simulated the VAX 8800 design extensively before building the first prototype . . . Engineers simply marked a centrally posted sheet with a tick-mark for each bug they found . . . We regarded finding and fixing hardware design bugs as our most important activity . . . over 96 percent of the total reported design bugs were found before the prototype was built (Clark 1990, pp. 26–27).

Clark also emphasized the importance of including bug identification and fixing bugs as a formal process within the design process, arguing that debugging (the track-ing down and fixing bugs) is a normal and necessary part of a design process. Software designers have added the jargon of bug to our lexicon to indicate any problem with a new product that was not anticipated and correctly designed to avoid. In software, bugs are logical errors in the design. In hardware, bugs can either be logical errors or physical problems. Debugging is important to both software and hardware design.

Design bugs arise from the complexities of a technology and of a product system.

The more complex the system, the more difficult it is to fully think out and consider all the aspects of the system and the interactions within a complex system. Therefore, bugs surprise the designer because the full implications of a designed complex system may only be understood in testing out the system. This is particularly true of designs that embody new technology.

The more radically new the technology, the more likely there will be bugs—not from lack of judgment but from lack of experience with the new technology.

Therefore, bugs are inherent in new technology, and when innovating new technology into a product design, the design procedures should include debugging as normal to design and not simply regarded as a “failure” of a designer. The important thing in managing technological innovation in product design is to find the bugs early.

The later in the product development process that bug is discovered and fixed, the more costly its solution and the slower will be its development.

In order to identify bugs early, it is useful to design and test products in a modular fashion. Also, the ability to simulate a product design in a computer is very useful to early identification of design bugs. Three areas in the design that are most likely to produce bugs include:

1. Areas where complexity is greatest

2. Interfaces in subsystems when subsystems have been designed by different people or groups

3. Parts of the design that are more rarely used in the product functioning 258

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The procedures for product testing are also important to the early identification of bugs. The traditional approach to product testing has been to make a list of tests that test each subfunction of the product, and then to perform these tests in sequence.

And that is a proper way to begin testing, but it may be insufficient, particularly for a complex system. Making bug finding an active, deliberate and focused part of the software design and development process improves the capability of innovating new technology in a new product design. Clark suggested complementing testing design with a “bug-centered approach” on the part of the product designers (Clark 1990).

For example, the product design team may set aside some periods (a day a week or a month, perhaps) as deliberate bug-finding days. The idea is that finding bugs is a positive contribution to design and not a negative contribution. Prizes might even be given to the team member discovering the most or most-challenging bugs.

Keeping a chart of the number of bugs discovered per week during the design is also a useful procedure to managing innovation in design. Confidence in the design should be determined not by the number of bugs found but by the rate of finding bugs. There will never be a guarantee that all design bugs have been found in a complex product system. However, when the rate of finding bugs decreases toward zero, particularly in a design process that deliberately and continuously seeks to find bugs, then the design is approaching robustness and will be ready for final testing. It is also helpful to review the types of bugs being found in the design. For example, is a bug indicative of a more general problem with the design? Why did not the test methods catch the bug in the first place? Could the method used to find that bug find more bugs? And so on.

COMPARING SOFTWARE AND HARDWARE INNOVATION

We can look again at how the technology progress curves differ in physical and software technologies and understand the difference—as shown again in Figure 12.4.

We recall that while physical technology progress shows an S-curve in progress over time, progress in software shows no S-curve. Instead, progress in software technologies occurs as a step-function. The reason for this step approach to software technologies progress is that the underlying nature of software is in the natural domain of language and logic. The underlying nature in physical technologies are mechanisms of nature—physics and chemistry. In software, the nature is that of the mind, of ideas—expressed in language and in logic.

Progress in the software technologies does not depend upon physical nature but human mental nature. Progress in logic and language applications in software are due correctness of logic and completeness of logic. When software captures the logic of a thinking process correctly and completely, then the software progress is over. Correctness and completion of the logic in software is the primary criteria for good software design. When a software design is completed in logic, then software progress for that particular type of software is finished. When the logical structuring of a process carried out by a software program is correct, then progress in that type of software is finished.

KEY

IDEAS

259

Physical technologies

Natural limits

Linear portion in

physical

Phenomenon and mechanisms

technologies is

of physical technologies

due to the ability

to go to smaller

scales in

mechanisms

Information technologies

Information

technologies (IT)

echnology performance parameter

progress by addition of

T

features and

Logic and function

integration of

programs.

New

Technology

Technology

No linear portion of it

invention

improvement

maturity

progress—only

period

period

period

completeness of

function.

Figure 12.4 General form of the technology progress—comparing physical with information technologies

Technology progress in hardware occurs either by changing the physical phenomena underlying a technology or by going smaller in the physics of a phenomenon.

Technology progress in software occurs either by developing new languages or logics—for processing information, control, and communication.

KEY IDEAS

In the case studies of:

• Sikorsky’s helicopter

• Fortran

• XML and ontology

We have examined the key ideas of:

• Hardware product systems

• Systems analysis in product design

• Specialized languages

• Computer-assisted networks

• Software strategy

• Software design bugs

13

SERVICE SYSTEMS

INTRODUCTION

Modern history, as we have emphasized, is very much a story of new technologies changing economic history. The twentieth century was revolutionized by physical technologies—the new industries of automobiles, airplanes, telephones, radio, polymers, and so on. Now in the twenty-first century economic history is being revolutionized again, but principally by information technologies coupled with communication technologies—computers, Internet, cellular communications. These have been and continue to, change everything! These are focusing businesses toward providing Internet delivered services. Information and communication technologies are integrating business strategies between manufacturing and software—toward the delivery of services.

Hardware products connected to the Internet are now designed to provide platforms upon which to download and run software applications for services—

hardware as a service platform.

The questions we will address here include the following:

• What is the transactional logic in delivering services?

• How does innovation make service delivery more competitive?

As shown in Figure 13.1, we look at the challenges of managing innovation in services.

SERVICES SCIENCE

In the first decade of the twenty-first century, the importance of delivering services on the Internet had stimulated some information technology firms to advocate the beginning of a new intellectual field of “services science”—in analogy to the 260

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261

Services Innovation

Research

Invent

Commercialize

Market

Nature

Science

Technology

Economy

Utility

Exist

Discover

Manipulate

Embed

Use

naturally

nature

nature

nature

nature

The world as material

The world as money

Figure 13.1 Innovation process

earlier growth of computer science as a science/technology field. For example, IBM used the name of Service Science, Management, and Engineering (SSME) to describe service science (www.ibm.com/developerworks/spaces/ssme). Also, in response to industrial demand for graduates with a service science focus, many universities began establishing educational programs. Examples include an SSME

program at the University of California at Berkeley and an MBA track for services in the Business School at North Carolina State University (http://en.wikipedia.org, Service Science).

Additionally, research funding for the new field was advocated in the European Union by the NESSI Group (Networked European Software and Services Initiative) (http://www.nessi-europe.com). NESSI’s mission statement read: Over the last couple of years, workshops have been organized by corporations and academic institutions including EU-IST, HP, IBM, NSF, UC

Berkeley, US Department of Commerce, and others, on multiple aspects of the sciences of systems and services . . . The Working group envisages a multi-disciplinary (e.g., economics, computational and mathematical sciences, management sciences, social-policy sciences, and others) effort for tackling some of the challenges in understanding services and deriving principles for harnessing services for providing appropriate value. . . . The challenge is to establish attainable expectations that services systems will function according to their specifications, at predicted costs, throughout their intended lifetimes (http://www.nessi-europe.com).

Service delivery using information and communication technologies have become a major economic thrust across the globe.

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CASE STUDY:

Google’s Phone

Originally, Google was principally an Internet service business—providing search engine services—and making big money from advertising. But in 2010 Google also introduced a software operating system for cellular phones and then decided to market a hardware product—a cellular phone. Why? As Miguel Helft described:

“Two titans of the tech world, Google and Apple, may soon be engaged in hand-to-hand combat, or more precisely, handset-to-handset. Google plans to begin selling its own smart phone early next year . . . a move that could challenge Apple’s leadership in one of the fastest growing . . . technologies . . . Google’s new touch-screen Android phone . . .” (Helft 2009, p. B1).

We recall that an industrial value chain has suppliers of parts and tools selling to customer-businesses in the value chain. This introduction of a phone would have put Google into competition with some of its customers in the cellular-phone industrial value chain: “The introduction of a Google phone—manufactured to its specifications by and Asian maker of handsets—would be an important and risky departure for Google. Until now, it has made software to power cellphones that are built and marketed by partners, and it has largely avoided selling hardware” (Helft 2009, p. B1).

Why would Google risk competing with its business customers down the industrial value-chain of cell phones? According to Helft: “The apparent shift (in Google’s strategy) underscores the fact that mobile phones are quickly becoming the biggest technology battleground of the future, as customers increasingly rely on their phones to browse the Internet and perform other computing tasks” (Helft 2009, p. B1).

However, Google then decided not to market the hardware phone but only provide the software platform: “We launched Nexus One in January with two goals in mind: to introduce a beacon of innovation among Android handsets, and to make it quick and easy for people to buy an Android phone. We’re very happy with the adoption of Android in general, and the innovation delivered through Nexus One. Already, a lot of the innovation that went into creating Nexus One has found its way into numerous Android handsets, like the HTC Evo 4G from Sprint and the Verizon Droid Incredible by HTC. But, as with every innovation, some parts worked better than others. While the global adoption of the Android platform has exceeded our expectations, the web store has not. . . . Once we have increased the availability of Nexus One devices in stores, we’ll stop selling handsets via the web store, and will instead use it as an online store window to showcase a variety of Android phones available globally.” (http://googleblog.blogspot.com/2010/05/nexus-one-changes-in-availability.html) (1) Proper hardware platforms improve the efficiency of (2) good software applications in providing (3) effective services to customers.

Also, a financial reason for business services is that a service can provide continuing sales and revenues—in contrast to a hardware purchase, which is predominantly a one-time sale.

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LOGIC OF SERVICE TRANSACTIONS

A central idea in the vision of any service business is the logical steps in the delivery of a service:

1.

Service referral. A customer must arrange for a service by contacting the service deliverer, as for example, selecting a doctor or opening a bank account or going to a retail outlet.

2.

Service scheduling. Service delivery must be scheduled, as for example, making an appointment and visiting the doctor’s office or writing a check or making a deposit or withdrawal from the account.)

3.

Diagnosis and selection of service application. The appropriate application in the service must be selected, as for example, diagnosis by a doctor of the patient’s illness or recording and accounting a banking fund transaction.

4.

Service application. The selected application in the service must be provide, as for example, prescribing a drug or performing surgery or transferring of banked funds electronically or in cash.

5.

Payment for service. Payment must be received by the service provider from the customer, as for example, billing a patient’s insurance company or billing a client’s bank account.

There are many types of services, including: transportation services, retail service, financial services, legal services, medical services, repair services, construction services, power and water services, business consulting services, information services, communication services, and so on. Each kind of service will use different technologies and products in delivering services. But there is this common logic of a service transaction for a service delivery.

Changes in information and other technologies for service operations and service transactions are both essential to strategic improvement of service systems.

CASE STUDY:

Apple’s Ipod & Music Businesses

In the year 2000, a dramatic example of the commercial success of integrating product and service was Apple’s iPod product and iTune music service. This combination revived Apple’s fortunes. We recall that Steve Jobs co-founded Apple Computer in 1979 and later left Apple and sold his shares. After Jobs left, Apple’s commercial fortune declined. Finally in 1997, the Apple board of directors asked Steve Jobs to return as CEO to save the company. Jobs began improving Apple’s Mac personal computers, introducing laptop versions. Then in 2000, Jobs innovated together (1) a digital music player on MP3 technology 264

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as the iPod and (2) an Apple music download service over the Internet to supply music to the iPod. This was an example of integrated hardware and software for a service business.

The iPod and iTunes together provided Apple with a commercial hit. It encouraged new sales of Mac laptops. For example, in January 2007, Apple reported quarterly revenue of $7.1 billion; iPod provided 48 percent of this revenue. But Apple had not invented either of the technologies, not for the good nor the service. The two earlier inventions were that of the MP3 digital audio player and of the Internet music-swapping software by Napster. Jobs’ commercially successful innovation was their integration as a product and service.

MP3 Player

The MP3 player was a portable music player to play digitally recorded music.

Previously portable music players, such as Sony’s Walkman players, used electronic analog signals to record music. The first patents on digital audio players were filed by Briton Kramer, expiring in 1988. Early in 1998, SaeHan Information Systems in South Korea produced the first mass-produced digital player as its MPMan, and Eiger Labs distributed them to the North American market. A few months later in September 1998, Diamond Multimedia produced its Rio PMP300 player.

On this early history of the MP3 player, Eliot Van Buskirk wrote: Ask even seasoned MP3 buffs about the first MP3 player, and they’re almost certain to name the Diamond Multimedia Rio PMP300. If they really know their stuff, they’ll even tell you that it came out in late 1998.

They’re wrong either way, although you shouldn’t be too harsh on them—

their mistake is understandable. The Diamond Rio’s false status as the first MP3 player is practically cemented in technology lore. . . . Most tech-savvy types wrongly think Diamond’s device was first because, like nearly every other major

development in digital music, the Rio brought with it a spectacular flurry of legal wrangling and the attendant media exposure.

Back in those days, you were nobody in the digital-music business unless the labels sued you (Van Buskirk 2005).

In 1998, the Recording Industry Association of America (RIAA) filed a lawsuit against Diamond Multimedia, alleging that the Rio MP3 player encouraged copying music illegally. But a federal court in California ruled that the player did not infringe any music copyrights (Clampet 1999). In the same year, an MP3 player with a hard drive to store music was innovated by Compaq’s System Research Center: “Here comes the irony. In 1998, Compaq’s engineers made the first hard-drive-based MP3 player and licensed it to a Korean company (Hango) that didn’t do much with it. In 2001, the first iPod came out” (Van Buskirk 2005).

LOGIC OF SERVICE TRANSACTIONS 265

Napster

Napster was begun by Shawn Fanning, a college student who was studying computer science at Northeastern University in Boston in the fall of 1998. Earlier he had worked summers for his uncle, John Fanning, whose company, Netgames, was developing online games. Shawn was bored by college and spent his time at his uncle’s office. He had an idea for a search engine to find and share music files over the Internet. He began programming Napster software. He and his uncle saw a commercial potential in the idea. Shawn dropped out of college and completed a test version of the software. His uncle incorporated a new company:

“Napster was an instant success. On June 1 of last year (1999), to test the beta version of the software, Shawn gave it to some 30 friends he met through online chat rooms. . . . In just a few days, Napster was downloaded by 3,000 to 4,000

people” (Bruell et al. 2000, p. 114).

With this evidence, John Fanning began to raise capital in August 1999. The demand for Napster soared and Napster hired its first CEO in Eileen Richardson, who was a venture capitalist in Boston: Napster raged across the college circuit like a forest fire. College students were discovering Napster, and they couldn’t get enough of it. At Oregon State University, Napster was taking up 10 percent of the school’s Internet bandwidth by October 1999. . . . That fall, it became clear that Napster had a whale by the tail” (Bruell et al. 2000, p. 115).

It became clear to others that the vast trading of music for free on the Internet had serious business implications. One of these was the RIAA, which had earlier sued Diamond Multimedia Systems, Inc., the manufacture of MP3 music players:

From late summer 1999, up to when the RIAA sued Napster on December 7, 1999, the two side were engaged in discussions about the service. People familiar with the situation describe the talks, which involved executives from Napster and various record companies, as a clash of cultures and monster ego. . .

. (Eventually) Napster’s relationship with the RIAA had deteriorated to the point of warfare” (Bruell et al. 2000, p. 115).

Months later, on July 26 2000, a federal judge of the U.S. District Court in San Francisco ruled against Napster for copyright violation. Next, the battle shifted to the U.S. District Court in San Francisco, where on July 26, 2000, a federal judge ruled on the RIAA’s suit against Napster for copyright violation:

“The hearing lasted about two hours. By the time it was over, Napster had been legally eviscerated. Judge Patel dismissed virtually every Napster argument, granting the injunction (against Napster). . . . The sweep and strength of her ruling stunned the Napster team. . . . Napster’s worst nightmare had come true . . . “(Bruell et al. 2000, p. 120). That suit against Napster was just one example of what would be a major strategic issue for the Internet—protection of copyrighted material.

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Apple’s iPod and iTunes

Thus, Steve Jobs knew a service providing copyright-protected music for payment could flourish over the Internet. He had his hardware engineering chief, Jon Rubinstein, assemble a team of engineers to design the iPod. On the team were Tony Fadell, Michael Dhuey, and Johahan Ive.

The iPod was to be an MP3 digital audio player with a hard drive for storage. The team developed the product in a year, and the iPod was displayed in October 2001, with a 5 GB hard drive that could store at least a thousand songs.

Apple adapted software from PortalPlayer’s. The brand name iPod was suggestd by Vinnie Chieco, a freelance copywriter, who was consulted by Apple about a name. But it turned out that this name had already been trademarked by Joseph Grasso, who then assigned it to Apple in 2005.

Apple also established an Internet service, iTunes Store, to sell music for the iPod. Songs are downloaded for a price from the Internet by a computer (e.g., Mac laptop) and loaded onto the iPod. Earlier, Steve Jobs had learned the different hardware, software, and service businesses. He innovated their integration as a means to revitalize the company he founded three decades earlier.

Jobs’s understanding of services as a business model for the Internet continued.

New products as hardware platforms for Internet services followed. In 2005, Apple entered the cellular phone market with a “smart” phone—the iPhone. In 2010, Apple introduced an iPod Touch. These new products were expected to generate at least 50 percent of Apple’s profits by 2011.

The success of the iPhone was due to applications software downloaded onto the phone. Apple provided a store in which application software could be approved for downloading. For example, Jenny Wortham reported: Apple is tightening its already firm grip on what software can run on the iPhone and its other mobile devices, as shown by its recent changes to the rules that outside programmers must follow. The company is locked in a battle with other cell phone makers, particularly those using Google’s Android operating system, for the latest and best applications that add functions to a phone (Wortham 2010).

Apple’s new hardware products were designed to provide platforms upon which to download and run software applications—a service platform.

STRATEGIC BUSINESS VISION—A HARDWARE BUSINESS

OR A SERVICE BUSINESS?

We see in this example, Jobs’s vision for a service innovation required the introduction of both an MP3 player and software for a music download service—iPhone and iTunes. As we have emphasized, both a correct technology vision and a correct business vision are together necessary for commercially successful innovation.

A HARDWARE BUSINESS OR A SERVICE BUSINESS? 267

There is a large literature on strategy theory, and in that literature, the idea of vision has been a puzzling aspect of strategy. Why is the intuitive vision of one leader judged in history as “visionary” (such as Jobs) and others who fail (such as the other Apple CEOs) are judged as “eactionary” or “laggard” in vision? Can the ability to be a visionary be learned by anyone, or are the visionary born and not made?

In the strategy literature, Henry Mintzberg pointed out that essential for a correct strategic vision is an intuition based on experience (Mintzberg 1990). Vision is based on the intuitive function of the mind, and not on the analytical function. Leadership vision is an intuitive activity of the top leadership of an organization—not simply describable in an analytical, rule-based procedure (such as that in which formal plans are expressed). Much of the problem about strategic vision of leadership in large organizations has to do with the difficulty in some cultures to provide processes that encourage intuition in a group context, as opposed to analysis in a group context.

A strategic vision for innovation needs to understand the differences between repetitive action and exploratory action—planning to go where one has been before and planning to go where no one has gone before. A repetitive action can be planned in operational detail, since one has gone there before and the route of the action has been well mapped. This is the common kind of planning performed in organizations for short-term time horizons—quarterly and yearly planning.

In cases that are unique, such as radically innovative projects, a project is more of an exploration into the unknown rather than a repetition of similar projects of the past. Such nonrepetitive project planning is a kind of adventure into unknown intellectual territory. Although one may start with a plan, much more important than the initial plan is the ability to be flexible and adaptable and capable of rapid learning in completely new environments.

In exploration, the strategic vision is more important to success than is the initial plan.

A technical vision enables one to foresee a possible technical future. A commercial vision enables one to foresee a possible future market.

CASE STUDY:

Pixar

Let us look at how Steve Jobs had gained the experience and strategic vision necessary for his second commercial success for Apple—iPod and iTunes. He learned this from previous business experiences—particularly from his purchase of Pixar and building it into a successful media service business.

We recall that in 1987 Jobs had become chairman of the board of Apple and looked for a new president to succeed him. He picked John Sculley, who had many years of marketing experience at Pepsi Co. Yet within a year, there were policy clashes in Apple’s team. Marcula backed Sculley, and Jobs resigned from 268

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Apple. Jobs then left Apple and sold his Apple stock. But under Sculley and his successors, Apple declined commercially.

Away from Apple, Jobs started a new computer company, which did not succeed.

But then Jobs bought and successfully grew Pixar into an animated movie business.

Early in the twentieth century when the new movie industry began, animated cartoons were also innovated in the movie industry. In the United States, Walt Disney pioneered in the early cartoon films. Next, sound was added in the early 1930s; and later in the 1970s, video was added to cinematic information technologies. Then in the 1980s, computer-aided graphics began to be added to the cinematic tools. During that decade, advances in computer-aided graphics information technologies were being developed by government agencies—in the military and space agencies using minicomputer platforms. And by the middle of the 1980s, researchers in information technology advances had begun applying parallel processing computers (small computers that used not one central processing unit but several within the same computer) in order to greatly speed up the picture processing by computers.

In the late 1970s film director George Lucas made a great commercial success in his science fiction film, Star Wars. He had extensively used animation in the film. Lucas next developed the first parallel-processing computer devoted to graphics and animation, which he called Pixar. His animation team used Pixar to produce animation sequences. However, Pixar was losing money, and Lucas sold it to Steve Jobs for $10 million.

Over the next five years, Jobs invested an additional $50 million in Pixar—

which at the time was about 25 percent of his total wealth, gained from selling Apple stock when he had left Apple.

Jobs had acquired an innovative team in Pixar. One key engineer was named Catmull; as a kid, he loved animated films but had little drawing talent. He studied computers and gained employment in 1975 at a vocational school, New York Institute of Technology in Old Westbury. There he teamed with some artists to try computer-assisted animation. But computers drew very slowly then, and in the 1980s, this team left New York and went to work for Lucas.

Finally in 1991, the business break for Pixar came when Disney gave Pixar a three-film contract. This was Pixar’s first venture from animation support to producing full-length animated films. The first film it produced was Toy Story, which was a big commercial success. Jobs decided to grow Pixar into a major movie studio. He raised additional capital for Pixar from investors, such as Seagram’s CEO, Edgar M. Bronfman, and investment banker Herbert A. Allen and Disney’s CFO Robert Moore, and the movie agent Michael Ovitz.

As a service business, movie production is based on art and storytelling. At Pixar, the person in charge of storytelling was not an engineer but an artist by the name of John Lasseter. Lasseter was born in Whittier, California, and had loved cartoons since childhood. He decided to become an animator as a freshman in high school after reading a book on the making of Disney’s famous children’s film, Snow White. After graduating from the California Institute of the Arts, Lasseter was at first employed by Disney. In 1984, he joined Pixar. Lasseter was credited with helping Pixar make the transition from short-subject films to full-length films.

A HARDWARE BUSINESS OR A SERVICE BUSINESS? 269

After the Toy Story movie success, Jobs revised his business alliance with Disney. Toy Story, which Pixar had produced and Disney distributed, had given Pixar only a small percentage of the profits. In 1998 with Toy Story’s success, Jobs was able to cut a better deal with Disney. For the next five years, Pixar would get an equal share of the profits with Disney (after a 12.5 percent distribution fee), and also Pixar had the assistance of Disney’s powerful marketing and distribution capabilities. Profits in the children’s movie industry came not only from film sales but also greatly from merchandising deals. In 1999, Pixar made about $53.8 million from Toy Story. But next Pixar would make more than $200

million in merchandising royalties, video sales, and box-office receipts from its next successful new movie, A Bug’s Life.

Jobs took Pixar public at the height of Toy Story’s success. The initial price of $22 quickly went to $33. It was reported that Jobs called one of his best friends, Lawrence J. Ellison, who was CEO of the high-tech database company, Oracle, to tell him he had company in the billionaires’ club (Burrows and Grover 1998).

Meanwhile, as Apple’s fortunes had continued to decline toward bankruptcy in 1996, the Apple board asked Steve Jobs to return to Apple, to save the company he had founded:

On Dec. 20 1996, Apple’s C.E.O. and chairman, Gilbert F. Amelio, announced that the company would buy Next Software for $400 million. For that price, Apple (gets an advanced operation system software and) also gets Steven P. Jobs . . . So Jobs becomes the computer era’s prodigal son: his return to Apple after more than a decade in exile is an extraordinary act of corporate reconciliation, a move laden with triumph, vindication and opportunity (Lohr 1997, p. 15).

So it happened that when the twenty-first century began, Steve Jobs was CEO

of two information technology firms, Pixar and Apple: It’

s 3:00 p.m. in Richmond, Calif., and Steve Jobs is micro-managing.

He’s sitting around a conference table at his Pixar Animation Studios with a gaggle of Pixar producers and Disney marketing types, poring over the color-coded, small-print, stunningly elaborate ‘synergy’ timeline’ for the upcoming Toy Story 2, which Pixar made and Disney will distribute. Ah, the endless promotional arcana of a $100 million aspiring blockbuster: trailers, press junkets. At 44, the Pixar chairman and Apple Computer interim-CEO-for-life finds himself a leading force in not one but two iconic late-’90s American Industries. . . . Jobs, after years spent pacing the sidelines, was suddenly at the top of both his games (Krantz 1999, p. 64).

Jobs had learned about managing a hardware company in founding Apple; and later he learned about managing a service business when he bought and developed Pixar.

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SERVICES AND E-COMMERCE

When the Internet emerged in the late 1990s, electronic commerce developed in several kinds of businesses, including:

• Internet

• Internet portal services (e.g., AOL)

• Communication services (e.g., AT&T)

• Retail

• Consumer products (e.g., Amazon, CDnow)

• Markets

• Commercial supply businesses (B2B)

• Auctions (e.g., eBay)

• Materials trading markets (commodity products)

• Finance

• Financial trading markets (stocks and bonds)

• Financial services (banking, credit, mortgages, etc.)

• Information

• Reservations (e.g., travel, hotels, etc.)

• Query and search (e.g., Google)

• Entertainment

• News, music, TV, etc.

• Education

• Higher education (e.g., UMUC)

• Executive or industrial training (e.g., IBM)

• Social networking

• Virtual sociology (e.g., Facebook, Twitter)

In each of these business applications, information strategy differed according to the customers and value-adding operations of the businesses. Using the Internet, a business model for services required identifying (1) customers, (2) customer needs, (3) products/services to satisfy their needs, and (4) channels of reaching customers.

Attracting

Information strategy is important to assist the finding or searching or exploration activities of customers to reach a website. A customer must take a positive action to find the business on the Internet. The customer may find the website through advertising or word-of-mouth or searches or references. Progress in information technology about the kind and nature of the search engine a customer uses to find sites is important.

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Informing

The next issue is to get the customer to stay at a website—informing a customer. Once the customer has found the business’s website, the website must be immediately relevant to the customer’s needs for the customer to remain and use it. The first condition of a website is that it must appear instantly interesting to the viewer. Grabbing interest is a combination of aesthetics and functional logic. An important characteristic of the Internet medium is that it combines information, entertainment, and communication.

To explore a site, logical clarity is important. This is where an understanding of the kinds of customers and their needs and desires is essential to a proper design of the organization and maneuvering paths through the site. How a site encourages a viewer to maneuver through the site must be guided by having the viewer learn how needs can be met by information at the site. The site should show the p roducts/

services available at the site. They should be presented in a way to show an obvious means to satisfy the customers’ needs—matching products and services to needs.

Adding Value

Next, a customer must find value in the site to use it and to return to it. A customer must be able to adequately inspect the goods or services offered at the site. Inspection may be easy or difficult. Progress in information technology through adding multimedia perceptive experiences—such as immersive multimedia (3D sight and sound and touching)—is valuable to the inspection of products/services sold over the Internet.

Profiting

The next challenge is to determine how profits are made from the website. Revenue sources can come from membership fees, sales, commissions, advertisements, or a combination of any of these.

The pricing of products or services sold on the Internet is an important decision.

On the Internet, prices can be easily compared. Also, costs must be determined for profitability. The e-commerce channel excels as scaling in volume—so that business strategies to increase volume of site usage are important.

CASE STUDY:

Ryanair

It is important to model the operations of a service system to identify points for innovation, and for this we review the case of Ryanair. The airline industry around the world had been regulated until the 1970s, when deregulation of the industry in some countries began altering competitive conditions, as Thomas Lawton nicely summarized: “Beginning in North America and spreading more recently to Western Europe, the airline passenger market has witnessed a growing intensity in price-based competition. This intensified competition has been facilitated by policy deregulation initiatives” (Lawton 1999).

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In the United States, the most successful of the new airlines begun this way was Southwest Airlines, and in Europe the most successful was Ryanair: The largest and most successful of Europe’s low fare airlines is the Irish operator, Ryanair. It is also the longest established, having first commenced scheduled services in June 1985, operating a 15 seater aircraft between Ireland and England. The market entry of this independent, privately owned airline, symbolized the first real threat to the near monopoly that the state-owned Aer Lingus had on the routes within Ireland and between Ireland and the U.K. (Lawton 1999).

Ryanair provided a simplified service. No meals were served, seats were not reserved, and no restrictions were placed on the tickets. To meet the competition, Aer Lingus eventually had to reduce its ticket price, and the lower prices increased the volume of passengers: “This ‘egalitarian’ scenario was in stark contrast to the more opulent, segregated, and expensive service offered by . . . Aer Lingus. . . . Ryanair’s arrival helped precipitate a growth in the total air travel market, particularly between Ireland and the United Kingdom. This growth occurred primarily in what has been described as the ‘visiting friends and relatives’ traffic” (Lawton 1999).

In the decade from 1985 to 1995, the number of air travelers between the U.K. and Ireland grew from 1.8 to 5.8 million annually. Ryanair’s leadership in low costs allowed its low fares to provide excellent profits: “Costs have fallen faster than yields within Ryanair, allowing profits to rise consistently. . . . This (has) translated into steadily increasing operating profit margins . . . going from 10.3 percent in 1994 to 17.6 percent in 1997” (Lawton 1999).

Ryanair’s strategy was not only to compete with low prices and low costs but also to open new routes. For example, it was first to offer services between Dublin and Bournemoth and between Dublin and Teeside. We can see in this example how a new-entry airline service competed against an established competitor with lower prices on existing routes, and it could do this by having lower costs. It expanded its markets by introducing new routes that had not had regular service previously. Since the cost of the major device that allowed the service—the airplane—was the same for all competitors, the lower cost leadership of Ryanair had to be focused on other aspects of the service delivery system, such as costs of food service and seat reservations.

MODELING SERVICE SYSTEMS

Product-producing businesses can be represented as an open system of value-adding operations within a marketing environment. The idea of a business as an enterprise model (an open value-adding system) was popularized in the 1980s by Michael Porter (Porter 1985). A firm’s inbound logistics purchases resources as materials and components and supplies and energy—from the market environment of the firm. Then the firm’s production operation transforms the resources into the products of the firm. Products are stored in inventory until sold. This transformational sequence connects the firm with its customers in adding economic value to procured resources. Also, Porter pointed out that

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above this transformational chain, several other functions of the firm provide overhead support to the firm, including the activities of: (1) procurement, (2) technology development, (3) human resource management, and (4) firm infrastructure.

Michael Porter

(http://drfd.hbs.edu/fit/public/facultyInfo)

Earlier than Porter, Jay Forrester had proposed a systems dynamics approach to modeling manufacturing organizations. Forrester proposed two planes of modeling: (1) a transformation plane for producing a product and (2) a control plane for information controlling the production processes.

Jay Wright Forrester (1918) was born in Nebraska. He attended the Massachusetts Institute of Technology, obtaining a bachelor’s and doctorate degrees in electrical engineering. He did research at MIT, and in the early 1950s he developed the first electronic computer with a ferroelectric stored memory technology. In 1956, he moved from the engineering school to the business school at MIT. He developed a systems approach to studying the dynamics of manufacturing, called systems dynamics.

Jay Forrester

(http://en.wikipedia.org 2008)

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Support plane

Financial management

Human resource management

Technology development

Transformation plane

Vendors’

Product

inputs

outputs

of

Purchasing

Production

Inventory

Sales

to

supplies

customers

Control plane

Information technology

Figure 13.2 Form for modeling business operations The author combined both Porter’s ideas and Forrester’s ideas to construct a general form for modeling the operations of a business, as shown in Figure 13.2

(Betz 2003).

Accordingly, any managed system (manufacturing or services) can be modeled on three planes (as shown in Figure 13.3):

1.

Activities of support of the transforming activities 2. Activities of the functional transformation

3. Activities of control of the transforming activities But applying this modeling technique to services requires that the system model must extend—beyond the firm offering a service—to the service system as a whole.

We can see this in modeling the air transport system in which Ryanair provided airplane services, as shown in Figure 13.4.

The transformation plane shows the city-to-city routes of the air transport system of a territory served by an airline. Air travelers (and freight) are inputs into the service to be flown from one city’s airport to another city’s airport. The output of the transformation of the functional capability of flight is the transportation of travelers and freight from city to city.

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Support

plane 1

Overhead activities

supporting production

Transformation

plane 2

Production and sales

Inputs

Outputs

activities

Control

plane 3

Overhead activities

controlling operations

Figure 13.3 Model of a managed system

Airplanes and

fueling and maintenance systems

Land

Passenger

Baggage

Food

transportation

Support plane 1

handling

handling

handling

systems

systems

systems

systems

Transformation plane 2

Air route

City 1

City 2

City 3

Travelers

Transportation

Air route

Air route

Flight

control

Reservation

Security

Control plane 3

systems

and scheduling

systems

systems

Hotel and

Travel agent

Governmental

ground transportation

systems

regulation

and reservation systems

of air safety

Figur

e 13.4 Model of an air transportation system Support activities for these flight transformations of geographic locale (air transportation) include:

• Airplanes and fueling and maintenance systems

• Land transportation systems for travel to and from the airports

• Passenger handling systems for passenger check-in and loading and unloading passengers onto airplanes at flight gates in the airports Baggage handling systems for accepting, loading, and delivering baggage and freight to and from the airplanes

• Food and beverage handling systems for refreshing passengers during flights 276

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Control activities for the flight transformations include the following:

• Airport flight control systems to control air traffic

• Travel agent and reservation and sales systems for passengers to purchase air tickets and make flight reservations

• Airline reservation and scheduling systems

• Airport security systems

• Hotel and ground transportation reservation systems

• Governmental regulation of air safety

Activities on all three planes are all operations essential to a service business, although not all activities may belong to a single business in the service.

SERVICES PERFORMANCE AND INNOVATION

The competition in providing a service depends on having the ability to deliver a service’s function—its performance. The following service factors provide the sources of differentiating competitive factors between service providers: Effectiveness—How well the function provision meets customers’ needs (such as Ryanair’s increasing effectiveness by adding new air routes) Efficiency—How much resources are consumed in delivering a service (such as the fuel efficiency of airplanes purchased by Ryanair and Aer Lingus) Capacity—The service provider’s capability of delivery services to many customers (such as the addition of Ryanair’s services increased the total flight traffic between Ireland and England)

Price—The service provider’s valuing of the service to the customers (such as Ryanair’s lower prices compared to Aer Lingus’s prices) Staff—The competency, dedication, and responsiveness of the service provider’s employees in serving customers (such as Ryanair’s well-trained and dedicated employees)

Costs—The cost of providing a service (such as Ryanair’s no-frills and low cost operations)

Margins—The difference between the price and costs of a service (such as Ryanair’s good margins through lowering costs even at low prices) Reputation—The customers’ perceptions of the reliability and quality of the service provider (such as the safety records and on-time services of Ryanair and Aer Lingus)

We note that Ryanair had differentiated itself from its competitor primarily through low prices, low costs, dedicated staff, and new, effective routes. Pixar had differentiated itself from Disney by pioneering computerized animation.

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IDEAS

277

A model of a service system is useful to identify points in a system’s performance, where innovations might improve service competitiveness. Innovations to increase knowledge assets of a service business can be classified under seven knowledge competencies:

1. Diagnosing the problems and needs of a client

2. Using proper devices essential to service delivery 3. Supplying and maintaining devices used in service delivery 4. Timeliness and correctness of services in a delivery 5. Development of service technologies

6. Communicating and conducting transactions with customers and suppliers 7. Knowledge competencies for controlling the activities of the service firm KEY IDEAS

In the case study of:

• Google’s Phone

• Apple’s iPod and iMusic

• Pixar

• Ryanair

We have examined the key ideas of:

• Transactional logic of service

• Service knowledge assets

• Information and business architectures

• Types of e-commerce

• Modeling service systems

•

Performance and innovation in a service system

14

BIOTECHNOLOGY SYSTEMS

INTRODUCTION

We now look at innovation in biotechnology, whose science base is biology. Here we can clearly see how important is progress to science for technological progress— scientific technology. And also here we can see why we have emphasized that the innovation process begins in nature and science and transforms this knowledge into technology through invention, so that business can use technological progress for new high-tech products/services to provide improved utility to a market—customer value.

In nature, biological systems are incredibly complex, and progress in biotechnology industry has been limited by the scientific understanding of this complexity. But biology continues to unravel and explain the biological complexities of nature. The questions we will address include the following:

• How does scientific progress in biology create new biotechnology?

• How has the complexity of biological nature impacted profitability in the developing biotechnology industry?

Figure 14.1 highlights how we will examine the science and technology interactions in the biotechnology innovation process.

Innovation in Biotechnology

Research

Invent

Commercialize

Market

Nature

Science

Technology

Economy

Utility

Exist

Discover

Manipulate

Embed

Use

naturally

nature

nature

nature

nature

The world as material

The world as money

Figure 14.1 Innovation process

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CASE STUDY:

Origin of Molecular Biology

The origin of molecular biology provided a science base for the later emergence of biotechnology. The historical setting of this case was the century of biological research from the 1870s to the 1970s to establish molecular biology from the modeling of DNA. Then in 1972, Stanley Cohen and Herbert Boyer invented the basic technique for manipulating DNA—recombinant DNA. But the scientific ideas that preceded this experiment began a hundred years earlier. These ideas were directed toward answering a central question in science: How is life reproduced? This answer required that many stages of research be performed: 1. Investigating the structure of the cell

2. Isolating and chemically analyzing the cell’s nucleus, DNA 3. Establishing the principles of heredity

4. Discovering the function of DNA in reproduction 5. Discovering the molecular structure of DNA

6. Deciphering the genetic code of DNA

7. Inventing recombinant DNA techniques

Structure of the Cell

In the early part of the nineteenth century, biology scientists used the recent invention of the microscope to look at bacteria and cells. They saw that cells, the constituent modules of living beings, have a structure consisting of a cell wall, a nucleus, and protoplasma contained within the wall and surrounding the nucleus.

In 1838, Christina Ehrenberg was the first to observe the division of the nucleus when a cell reproduced. In 1842, Karl Nageli observed the rodlike chromosomes within the nucleus of plant cells. Thus, by the middle of the nineteenth century, biologists had seen that the key to biological reproduction of life involved chromosomes that divided into pairs when the nucleus of the cell split in cell reproduction (Portugal and Cohen 1977).

Discovery and Chemical Analysis of DNA

Scientific attention next turned to investigating the chemical nature of chromosomes. Science is organized into disciplines; and the techniques and knowledge in one discipline may be used in another discipline. Chemistry and physics, as scientific disciplines, provided tools and knowledge to the scientific discipline of biology. In 1869, a chemist, Friedrich Miescher, reported the discovery of DNA, by precipitating material from the nuclear fraction of cells. He called the material nuclein, which later became known as DNA.

In 1873, A. Schneider described the relationships between the chromosomes and various stages of cell division. He noted two states in the process of mitosis, 280

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which is the phenomenon of chromosome division resulting in the separation of the cell nucleus into two daughter nuclei. In 1879, Walter Flemming introduced the term chromatin for the colored material found within the nucleus after staining. He suggested that chromatin was identical with Miescher’s nuclein, DNA.

At this time, studies of nuclear division and the behavior of chromosomes emphasized the importance of the nucleus. But it was not yet understood how these processes were related to fertilization. In 1875, Oscar Hertwig demonstrated that fertilization was not the fusion of two cells but the fusion of two nuclei.

Meanwhile, the study of nucleic acid components was progressing. In 1879, Albrecht Kossel began publishing in the literature on nuclein DNA. Over the next decades, he (along with Miescher) was foremost in the field of nuclein research; and they and Pheobus Levine finally laid the clear basis for the determination of the chemistry of DNA.

As early as 1914, Emil Fisher had attempted the chemical synthesis of a nucleotide (component of nucleic acid, DNA); but real progress was not made in synthesis until 1938. Chemical synthesis of DNA was an important scientific technique necessary to understand the chemical composition of DNA. One of the problems was that DNA and RNA were not distinguished as different molecules until 1938.

(This is an example of the kinds of problems that scientists often encounter, that nature is more complicated than originally thought.) By the end of the 1930s, the true molecular size of DNA had been determined.

In 1949, C. E. Carter and W. Cohn found a chemical basis for the differences between RNA and DNA. By 1950, DNA was known to be a high-molecular-weight polymer with phosphate groups, linking deoxyribonucleosides between three and five positions of sugar groups. The sequence of bases in DNA was then still unknown. Thus, by 1950 the detailed chemical composition of DNA was finally determined but not yet its molecular geometry. Almost one hundred years had passed between the discovery of DNA and determination of its chemical composition.

Principles of Heredity

From 1900 to 1930, while the chemistry of DNA was being sought, the foundation of modern genetics was also being established. Understanding the nature of heredity began in the nineteenth century with Darwin’s epic work on evolution and with Mendel’s pioneering work on genetics. Modern advances in genetic research began in 1910 with Thomas Morgan’s group researching the heredity in the fruit fly, Drosophila meanogaster. Morgan demonstrated the validity of Mendel’s analysis and showed that mutations could be induced by x-rays, providing one means for Darwin’s evolutionary mechanisms. By 1922, Morgan’s group had analyzed 2000

genes on the Drosophila fly’s four chromosomes and attempted to calculate the size of the gene. Muller showed that ultraviolet light could also induce mutations.

(Later, in the 1980s, an international human genome project would begin with the goal to map the entire human gene set.)

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Function of DNA in Reproduction

While the geneticists were showing the principles of heredity, the mechanism of heredity had still not been demonstrated. Was DNA the transmitter of heredity, and if so, how?

Meanwhile, other scientists were studying the mechanism of the gene, with early work on bacterial reproduction coming from scientists using bacterial cultures. R. Kraus, J. A. Arkwright, and O. Avery with A. R. Dochez demonstrated the secretion of toxins by bacterial cultures. This raised the question of what chemical components in the bacterium were required to produce immunological specificity. The search for the answer to this question then revealed a relationship between bacterial infection and the biological activity of DNA.

As early as 1892, there had occurred the identification of viruses and their role in disease. In 1911, Peyton Rous discovered that rat tumor extracts contained virus particles capable of transmitting cancer between chickens. In 1933, Max Schlesinger isolated a bacteriophage (virus) that infected a specific kind of bacteria. Next, scientists learned that viruses consisted mainly of protein and DNA.

In 1935, W. M. Stanley crystallized the tobacco mosaic virus, which encouraged scientists to further study the physical and chemical properties of viruses.

Meanwhile, in 1928, Frederick Griffith had shown that a mixture of killed infectious bacterial strains with live, noninfectious bacteria could create a live infectious strain. In 1935, Lional Avey showed that this transformation was due to the exchange of DNA between dead and living bacteria. This was the first clear demonstration that DNA did, in fact, carry the genetic information. By 1940, work by George Beadle and Edward Tatum further investigated the mechanisms of gene action by demonstrating that genes control the cellular mechanisms of gene action by demonstrating that genes control the cellular production of substances by controlling the production of enzymes needed for heir synthesis. The scientific stage was now set to understand the structure and of DNA and how DNA’s structure could transmit heredity.

Structure of DNA

There were many lines of research necessary to discover the elements of heredity (genes and DNA) and its function (transmission of heredity). Yet before technology could use this kind of information, one more scientific step was necessary—

understanding the mechanism. This step was achieved by a group of scientists that were to be later called the phage group and would directly give rise to the modern scientific specialty of molecular biology (Judson 1979).

In 1940, M. Delbruck, S. Luria, and A. Hershey, founded the phage group and began collaborating on the study of viruses. One of their students was James Watson, who studied under Luria at the University of Illinois in the United States.

Watson graduated in 1951 with a desire to discover the structure of DNA, as this then was a great goal in biology. He heard that the Rutherford Lab in Cambridge was strong in the x-ray study of organic molecules, and he knew that an x-ray 282

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picture of DNA would be necessary to see its geometric form. With a postdoc fellowship from the U.S. government, he asked Luria to arrange for him to do his postdoc at the Rutherford Lab.

Once at the Rutherford Lab, Watson found a collaborator in Francis Crick, then a graduate student working on a physics degree. Crick was a bit older than Watson because his graduate studies had been interrupted by service in the Second World War. While Watson brought a knowledge of biology and organic chemistry to their collaboration, Crick brought a knowledge of physics. Both were necessary for the job of constructing a molecular model of DNA. But a critical piece of information they needed was a good x-ray diffraction picture of a crystalline DNA.

X-ray crystallography was a technique for sending x-rays (high-energy photons) through crystals and inferring the structure of the crystals from the diffraction patterns the x-rays produced from the structure. (An analogy would be to have a line of pilings near the shore and watch a big wave come in and produce smaller waves from the pilings; and from watching the smaller waves, calculate backward to measure the spacing between the pilings.) The diffraction of x-rays by crystals was first suggested theoretically by Max von Laue in 1912. Lawrence Bragg and William Henry Bragg established experimental x-ray crystallography as a scientific technique. William Astbury applied the technique to the study of organic fibers in 1928. Dorothy Crowfoot Hodgkin published the first x-ray study of a crystalline protein in 1934. Scientific instrumentation and instrumental techniques are critical to the progress in science. Just as the microscope was essential to observe the cell and its structure, x-ray crystallography was essential to observe the structure of DNA.

The Rutherford Lab did have a researcher, Rosalind Franklin, using x-rays to try to determine the structure of DNA. Franklin was a young, bright scientist from Portugal and had joined the lab to study x-ray diffraction of crystallized DNA.

This was not easy. There were two crystalline forms of DNA, and only one of these would yield a good picture. Moreover, it had to be oriented just right to get a picture that would be interpretable as to structure. Franklin would finally get a good picture of the right form. She was an excellent scientist.

Meanwhile, Watson had learned of Linus Pauling’s and Robert Corey’s work on the structure of crystalline amino acids and small peptide proteins. From these Pauling published a structural description of a first example of a helical form of a protein. Then, Pauling was one of the most famous organic chemists in the world. Watson feared that Pauling would soon model DNA structure, robbing the young and ambitious scientist of fame and scientific immortality. Watson saw himself in a scientific race with Pauling to be the first to discover the structure of DNA. Pauling then did not know the young Watson existed. (But this is the general model of human competition, the young racing to exceed their elders; and scientists are only human.)

Watson then conjectured: what kind of x-ray diffraction picture would a helical molecule make? If DNA were helical, Watson wanted to be prepared to interpret it from an x-ray and asked another young expert in diffraction modeling for a tutorial. He was told how (if the picture were taken head-on down the axis of the INTRODUCTION

283

helix) to measure the angle of the helix. Watson was thus equipped to interpret an x-ray picture of DNA, if he could only get his hands on one.

Meanwhile, Watson and Crick had scoured the chemical literature about DNA and been trying to construct ball-and-wire-cutout models of DNA. At first they had tried a triple helix model, and it didn’t work. Finally, Watson heard that Franklin had got a good picture, but he feared Franklin would not show it to him.

Franklin was as fierce a competitor as Watson, and was not willing to show her picture before she had time to calculate its meaning. Watson sneaked a peak at the picture without Franklin’s permission. There it was! Watson saw it. Clearly, a helix, and a double helix!

Quickly, Watson measured the pattern and rushed to Crick with the information on the angle of the helix. Watson and Crick put their model together in the form of a double helix, two strands of amino acid chains, twisting about each other like intertwined spiral staircases. They used the angle for the helix, as measured from Franklin’s picture. All the physical calculations and organic chemistry fit together in the model beautifully. Without a doubt! This was the holy grail of biology—the double helix structure of DNA!

Moreover, the structure itself was informative. It clearly indicated the molecular action of DNA in the mitosis of cell reproduction. DNA was structured as a pair of twisted templates, complementary to one another. In reproduction, the two templates untwisted and separated from one another, providing two identical patterns for constructing proteins—that is, reproducing life. In this untwisting and chemical reproduction of proteins, life was biologically inherited.

In 1995, Watson and Crick and Wilkins were awarded the Nobel Prize in Biology. Unfortunately, Rosalind Franklin was not so honored, the reason given being her untimely death before the prize was awarded. (This was not a nice story for science because she should have received appropriate recognition for her essential contribution. Watson and Crick should have put her name as a coauthor in their paper. And the Nobel Prize Committee should have publicly recognized her contribution.)

Genetic Coding

By the early 1960s, it was therefore clear that the double-helical structure of DNA was molecularly responsible for the phenomenon of heredity. Proteins serve as structural elements of a cell and as catalysts (enzymes) for metabolic processes in a cell. DNA provides the structural template for protein manufacture, replicating proteins through the intermediary templates of RNA. DNA makes RNA, RNA makes proteins. A two-step biological process. DNA structures the synthesis of RNA, and RNA structures the synthesis of proteins. What was not yet clear was how the information for protein manufacture was encoded in the DNA. In 1965, Marshall Nirenberg and Philip Neder deciphered the basic triplet coding of the DNA molecule. The amino acids that composed the DNA structure, acted in groups of three acids to code for a segment of protein construction.

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Thus, in a hundred years, science had discovered the chemical basis for heredity and understood its molecular structure and mechanistic function in transmitting heredity information.

Recombinant DNA Technique

After understanding the mechanism of the gene, the next step was trying to manipulate the gene. Several scientists began trying to cut and splice genes. In 1965, Paul Berg at Stanford planned to transfer DNA into Escherichia coli bacteria, using an animal virus (Svrp lambda phage). E. coli bacteria can live in human intestines, and the SV40 virus is a virus of monkeys that can produce tumor cells in cultures of human cells. Because of the dangerous nature of the SV40

virus, Berg decided not to proceed with the experiment, publishing a design for hybridizing bacteria in 1972. Berg organized a historic meeting on safety, the Conference on Biohazards in Cancer Research in California on January 22–24

1973 (Olby 1974). This stimulated later government action to set safety standards for biotechnology research.

A colleague at the University of California responded to Berg’s request for some EcoRI enzyme, which cleaves DNA (and leaves the “sticky ends” of the cut DNA). Berg gave the enzyme to one of his students, Janet Mertz, to study the enzyme’s behavior in cutting DNA. Mertz noticed that when the EcoRI enzyme cleaved an SV40 DNA circlet, the free ends of the resulting cut and linear DNA eventually reformed by itself into a circle. Mertz asked a colleague at Stanford to look at the action of the enzyme under an electron microscope. They learned that any two DNA molecules exposed to EcoRI could be recombined to form hybrid DNA molecules. Nature had arranged DNA so that once cut, it respliced itself automatically.

There was another professor in Stanford University’s Medical Department, Stanley Cohen, who learned of Janet Mertz’s results. Cohen also thought of constructing a hybrid DNA molecule from plasmids using the EcoRI enzyme.

Plasmids are the circles of DNA that float outside the nucleus in a cell and manufacture enzymes the cell needs for its metabolism. The DNA in the nucleus of the cell is principally used for reproduction. In November 1972, Cohen attended a biology conference in Hawaii. He was a colleague of Herbert Boyer, who had given the EcoRI enzyme to Berg (and Berg’s student Mertz). At a dinner one evening, Cohen proposed to Boyer that they create a hybrid DNA molecule without the help of viruses. Another colleague at that dinner, Stanley Falfkow of the University of Washington at Seattle, offered them a plasmid, RSF1010, to confer resistance to antibiotics in bacteria so that they could see whether the recombined DNA worked in the new host.

After returning from the Hawaii Conference, Boyer and Cohen began joint experiments. By the spring of 1973, Cohen and Boyer had completed three splicings of plasmid DNAs. Boyer presented the results of these experiments in June 1973 at the Gordon Research Conference on Nucleic Acids in the United States (with publication following in the Proceedings of the National Academy SCIENCE BASES FOR TECHNOLOGY 285

of Sciences, November 1973). Cohen and Boyer also applied for a patent on the new technique.

After one hundred years of scientific research into the nature of heredity, humanity could now begin to deliberately manipulate genetic material at a molecular level—and a new industry was born, biotechnology. Boyer and Cohen would win Nobel Prizes. Boyer would be involved in the first new biotechnology company (Genentech) to go public and would become a millionaire. The days biologists to become industrial scientists had begun.

SCIENCE BASES FOR TECHNOLOGY

Science has provided the knowledge bases for scientific technology in a pattern: 1. Scientists pursue research that asks basic and universal questions about what things exist and how things work. (In the case of genetic engineering, the science base was guided by the questions: What is life? How does life reproduce itself?)

2. To answer such questions, scientists require new instrumentation to discover and study things. (In the case of genetic research, the microscope, chemical analysis techniques, cell culture techniques, x-ray diffraction techniques, and the electron microscope were some of the important instruments required to discover and observe the gene and its functions.)

3. These studies are carried out by different disciplinary groups specializing in different instrumental and theoretical techniques: biologists, chemists, and physicists. (Even among the biologists, specialists in gene heredity research differ from specialists in viral or bacterial research.) Accordingly, science is pursued in disciplinary specialties, each seeing only one aspect of the existing thing.

Nature is always broader than any one discipline or disciplinary specialty.

4. Major advances in science occur when sufficient parts of the puzzling object have been discovered and observed and someone imagines how to put it all together in a model (as Watson and Crick modeled the DNA molecule). A scientific model is conceptually powerful because it shows both the structure and the dynamics of a process implied by the structure.

5. Scientific progress takes much time, patience, continuity, and expense.

Instruments need to be invented and developed. Phenomena need to be discovered and studied. Phenomenal processes are complex, subtle, multileveled, and microscopic in mechanistic detail. (In the case of gene research, the instruments of the microscope and electron diffraction were critical, along with other instruments and techniques. Phenomena such as the cell structure and processes required discovery. The replication process was complex and subtle, requiring determination of a helix structure and deciphering of nature’s coding.) 6. From an economic perspective, science can be viewed as a form of societal investment in possibilities of future technologies. Since time for scientific 286

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discovery is lengthy and science is complicated, science must be sponsored by government as a social overhead cost for a technically advanced society.

Without support for this science overhead cost of basic knowledge creation, technological innovation eventually stagnates for lack of new phenomenal knowledge for its inventive ideas.

7. Once science has created a new phenomenal knowledge base, inventions for a new technology may be made by either scientists or by technologists. (For example, scientists invented the recombinant DNA techniques.) These radical technological inventions start a new technology S-curve. Then is the time to invest in a technological invention and begin a new industry.

8. When the new technology is pervasive across several industries (as genetic engineering is across medicine, agriculture, forestry, marine biology, materials, etc.), the technological revolution may fuel a new economic expansion.

Economic history is grounded in scientific advances that create basic new industrial technologies.

9. There are general implications for corporate management. Corporations should be supportive of university research that focuses on fundamental questions underlying core technologies of the corporation. Corporations need to perform basic research activity in the science bases of their core-technologies to maintain a window on science for technological forecasting.

CASE STUDY:

Origin of the Biotechnology Industry

We have discussed how a national innovation system has a major impact on the economy when technological revolutions are begun on scientific discoveries. This is the case of molecular biology in universities and biotechnology in industry.

The biotechnology industry was created directly from the scientific discoveries in genetics; and two of the first new biotech firms were Genentech and Cetus.

As we just noted, it was in 1973 that Cohen and Boyer applied for a basic patent on recombinant DNA techniques for genetic engineering. Subsequently, Boyer founded Genentech, and Cohen joined Cetus. In the years from 1976 to 1982, in the United States over one hundred other research firms were formed to commercialize the new biotechnology. In 1980, Genentech and Cetus both went public, and Boyer and Cohen became millionaires.

Genentech was founded by the scientist Herbert Boyer and an entrepreneur, Robert A. Swanson. Swanson heard of the new DNA technique and saw the potential for raising venture capital to start a genetic engineering firm. The story is that Swanson walked into Boyer’s office and introduced himself. He proposed that they start a new firm. In 1976 they each put up $500 and started Genentech.

Early financing in Genentech was secured from venture funds and industrial sources. Lubrizol purchased 24 percent of Genentech in 1979. Monsanto bought about 5 percent.

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Earlier, in 1971, Cetus had originally been founded to provide a commercial service for fast screening of microorganisms. In 1976 Cetus changed its business to designing gene-engineered biological products. For this, Cetus retained Stanley Cohen as one of its thirty-three scientific consultants; he later became head of Cetus Palo Alto ( Business Week 1984). Further investment in Cetus came from companies interested in the new technology. A consortium of Japanese companies owned 1.59 percent of Cetus. Standard Oil of Indiana purchased 28 percent of their stock. Standard Oil of California bought 22.4 percent. National Distillers and Chemical purchased 14.2 percent. Corporate investors wanted to learn the new technology.

Both Genentech and Cetus offered stock options to their key scientists.

Genentech and Cetus were the first of the biotechnology firms to go public.

Genentech realized net proceeds of $36 million. At the end of fiscal year 1981, it had $30 million cash but required about a million yearly for its R&D activities.

In its public offering, Cetus raised $115 million at $23 a share. Of this, $27

million was intended for production and distribution of Cetus-developed product processes, $25 million for self-funded R&D, $24 million for research administrative facilities, $19 million for additional machinery and equipment, and $12

million for financing of new-venture subsidiaries.

For new firms, it is important that early products create income. In 1982, Genentech’s product interests were in healthcare, industrial catalysis, and agriculture. In 1982, early products included genetically engineered human insulin, human growth hormone, leukocyte and fibroblast interferon, immune interferon, bovine and porcine growth hormones, and foot-and-mouth vaccine. Genentech’s human insulin project was a joint venture with Eli Lilly, aimed at a world market of $300 million in animal insulin. Genentech’s human growth hormone project was a venture with KabiGen (a Swedish pharmaceutical manufacturer), a world market of $100 million yearly. The leukocyte and fibroblast interferon was a joint venture with Hoffmann-La Roche, and the immune interferon with Daiichi Seiyaku and Toray Industries. The bovine and porcine growth hormones were a joint venture with Monsanto, and the foot-and-mouth vaccine with International Minerals and Chemicals.

In comparison, in 1982 Cetus was interested primarily in products in health care, chemicals, food, agribusiness, and energy. Its commercial projects included high-purity fructose, veterinary products, and human interferon. The high-purity fructose project was a joint venture with Standard Oil of California. In 1983, Cetus introduced its first genetically engineered product, a vaccine to prevent scours, a toxic diarrhea in newborn pigs. But product income was not rising fast enough for Cetus. In 1983, Cetus replaced its president with a new president, Robert A.

Fildes. He developed a five-year plan to provide Cetus with a positive cash flow by 1988. His plan narrowed the focus to the two major health care markets, diag-nostics and cancer therapeutics. Both monoclonal antibody and recombinant DNA technologies were employed. Also he reduced employment to 480 people (with 280 scientists). The new president then hired people experienced in developing drugs and taking them to market.

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But the path to riches for the new biotechnology industry of the late twentieth century did not go smoothly. The reason was that the complexity of nature turned out to be more than anticipated by the new biotechnology industry, as summarized by Thayer in 1996: “Fighting waves of hype and pessimism—while trying to create products and access markets—tests (biotechnology) firms’ ability to endure” (Thayer 1996, p. 13).

By 1996, the biotechnology industry had created 35 therapeutic products that then had a total annual sale of over $7 billion. These biopharmaceutical products were used to treat cancer, multiple sclerosis, anemia, growth deficiencies, diabetes, AIDS, hepatitis, heart attack, hemophilia, cystic fibrosis, and some rare genetic deceases.

But the industry was not initially as successful as early investors had hoped.

Genentech had hoped that producing a protein product called TPA would catapult them into large-firm status, but the costs of developing and proving products and the relatively small market for TPA put Genentech into a financial crisis in 1990.

To survive, Genentech sold 60 percent of its equity to Hoffman-La Roche: “Despite TPA’s success today, it took the 20-year-old company many years and many millions of dollars to prove that it had an important product” (Thayer 1996, p. 13).

In the 1990s, most of the marketing of the new biotechnology therapeutic products were through these older established pharmaceutical firms rather than the new biotechnology firms that pioneered pharmaceutical recombinant DNA technology. Genentech had partnered with Hoffman-La Roche, and Chiron with Ciba-Geigy. Other start-ups from the early 1980s were firms such as Biogen, Amgen, Chiron, Genetics Institute, Genzyme, Immunex. Biogen did pioneering work on proteins such as alpha-interferon, insulin, and hepatitis. But to support itself, Biogen licensed its discoveries so that other pharmaceutical firms market its products. Genetics Institute and Immunex were majority owned by American Home Products. The one exception to this pattern of the fate of early biotechnology firms was Amgen, which in 1995 had become an industry leader in biotechnology and an independent, fully integrated biopharmaceutical producer with sales of $1.82 billion.

PROFITS IN BIOTECHNOLOGY

Why had the early hoped-for big profits in biotechnology not occurred ( though biotechnology survived and continued to develop)? The answer to this was in biological science: “Early expectations, in hindsight considered naive, were that drugs based on natural proteins would be easier and faster to develop. . . . However, . . .

Biology was more complex than anticipated” (Thayer 1996, p. 17).

For example, one of the first natural proteins, alpha-interferon, took ten years to be useful in antiviral therapy. When interferon was first produced, there had not been enough of it available for study to really understand its biological functions.

The production of alpha-interferon in quantity through biotechnology techniques allowed the real studies and experiments to learn how to begin to use it therapeutically. This kind of combination of developing the technologies to produce PROFITS IN BIOTECHNOLOGY 289

therapeutic proteins in quantity and to use them therapeutically took a long time and many developmental dollars.

Chiron spent millions of dollars and bet everything on interleukin-2 as an anti-cancer drug but failed to obtain the United States Federal Drug Administration’s (FDA) approval to market for this purpose. Chiron was subsequently acquired by Cetus in 1995. In 1995, Chiron still only earned 4% of its revenues of $1.1 billion from interleukin-2, even though it had finally received its FDA approval in 1992. About this, George B. Rathmann commented: “. . . the pain of trying to get interleukin-2

through the clinic just about bankrupted Cetus and never has generated significant sales” (Thayer 1996, p. 17). In 2006, Chiron was acquired by Novartis.

The innovation process for biotechnology industry in the United States included (1) developing a product, (2) developing a production process, (3) testing the product for therapeutic purposes, (4) proving to the U.S. FDA that the product was useful and safe, and (5) marketing the product. In fact, the recombinant DNA techniques were only a small part of the technology needed by the biotechnology and the smallest part of its innovation expenditures. The testing part of the innovation process to gain FDA approval took the longest time (typically seven years) and was the greatest cost.

Because of this long and expensive FDA process, extensive partnering occurred between U.S. biotech firms and the larger, established pharmaceutical firms. For example, in 1995, pharmaceutical companies spent $3.5 billion to acquire biotechnology companies and $1.6 billion on R&D licensing agreements (Abelson 1996).

Also, pharmaceutical firms spent more than $700 million to obtain access to data banks on the human genome that was being developed by nine biotechnology firms.

The U.S. government role in supporting science was essential to the U.S. biotechnology industry:

The government has a very big role to play [in helping] to decrease the costs. Support of basic research through NIH [National Institutes of Health]

is very important to continue the flow of technology platforms on which new breakthrough developments can be based. (Henri Termeer, chairman and CEO of Genzyme and chairman of the U.S. Biotechnology Industry Organization, quoted in Thayer 1996, p. 19)

The scientific importance of understanding the molecular nature of biology (the discipline now called molecular biology) did prove to be the future of the pharmaceutical industry—as an essential methodology to develop new drugs. Yet the making of money from the technology of recombinant DNA was harder and took longer than expected because biological nature turned out to be more complicated than anticipated. The biotechnology industry depended on and continues to depend on new science. In turn, the technology needs of the biotechnology industry have helped drive the discoveries in biological science.

The progress of a new technology depends on the progress of understanding the complexity of nature underlying the technology.

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All the expense going into the biotechnology industry has turned out to be worth it, for biology continues to be the future of the pharmaceutical industry.

For example, in 1996, George Rathmann nicely summarized: “It doesn’t have to follow that science automatically translates into great practical results, but so far the hallmark of biotechnology is very good science and that now is stronger and broader than ever . . . The power of the science is ample justification that there should be good things ahead for biotechnology (Thayer 1996, p. 18).

RESEARCH AND COMPLEXITY OF NATURE

We have just reviewed how the application of biotechnology to medicine and to agriculture did not immediately create great wealth after the beginning of that industry—

because biological nature is complex and that nature was not well enough understood (when the twenty-first century) began to develop effective and safe medical cures or agricultural applications on a grand scale. The subtle complexities of nature are the sources of all the technology and engineering problems of physical technologies.

The more complex the nature underlying a technology, the more is the scientific research required to further understand the nature used in the technology and then engineered into a product.

Even though a scientific phenomenon has been discovered upon which to base a new technology and a concept for manipulating the phenomenon as a technology has been invented, there is still a long way before a product can use the technology in an effective and efficient performance and at a reasonable cost. We recall that this step after science and technology research is called engineering research. Engineering research further develops the technology to operate at a performance specification required by a product. Engineering research deepens and fills in the knowledge-base of science that is necessary to improve a technological invention, so that it can manipulate a natural phenomenon well enough to perform in a practical product (device or service or process). But when nature is really complex (as in the biology of organisms), then science research and engineering research become indistinguishable. Engineering progress moves directly at the pace of scientific progress and vice-versa.

CASE STUDY:

Human Genome Project

The sequences of bases in the DNA’s double helix structure could be interpreted as having a functional value to life as a “language” of instruction for DNA replication and protein synthesis. Biology needed both representations of mechanism and function to describe living phenomena. The next big project undertaken at the end of that century was to write down and decode the set of instructions for RESEARCH AND COMPLEXITY OF NATURE 291

the replication of an entire human being, the human genome. Samuel Moore reported on the project:

Sequencing the human genome is essentially putting in order the over 3

billion chemical units that encode the instructions on how to build and operate a human being. But those instructions are written in a language biology does not fully understand . . . The usefulness of the genome will emerge only after scientists have figured out how the parts go about making the machine that is the human body (Moore 2000, p. 34).

The human genome project was pursued by two independent and competing scientific groups in the 1990s. One group was an international public consortium consisting of the National Center for Biotechnology in the United States, the European Bioinformatics Institute, and the DNA Data Bank in Japan. This group was funded for fifteen years at about a total of $3 billion. The second competing group operating on private industrial funding was the company of Celera Genomics in Rockville, Maryland, U.S.A. In June 2000, both groups announced a first draft of the chemical sequences in all the DNA of the human genome. A refined version was expected to be completed by 2003.

We recall that the mechanism of transmitting heredity in animate objects (living organisms) is the structure and operation of the DNA molecule as a double-stranded chain in the form of a paired helix. The two strands are each sequences of four nucleotide units, or bases—adenine, thymine, guanine, and cystocine (A, T, G, C). These bases bind to each other in pairs, with adenine (A) needing to be across the helix from thymine (T) and cytosine (C) across from guanine (G). This base pairing in chemical attraction is the physical mechanism for structuring the DNA molecule. It is also the mechanism for reproducing the cells of an organism and for creating the proteins and operations of an organism: “. . . the two stands of DNA helix separate to form single-stranded DNA. The attraction of adenine for thymine and guanine for cytosine directs the synthesis of strands complementary to each of the separated strands to produce identical double helices. Cells use this scheme to copy all their genes before dividing so that a complete set of genes ends up in each cell” (Moore 2000, p. 35).

The success of the human genome project depended on technology, automating the sequencing of DNA: “The history of the sequencing of the set of human genes has been one of plans being overtaken by technical events. . . The original vision for the U.S. Funded Human Genome Project was a fifteen-year program begun in 1990s, the first ten years of which would be deployed in constructing compre-hensive maps of the genome and in developing a new generation of sequencing technologies” (Hodgson 2000, p. 37).

The scientific technique of DNA sequencing, polymerase chain reaction (PCR), was invented in 1977 by Frederick Sanger at Cambridge University in the United Kingdom. DNA sequencing begins with the preparation and replication of a segment of DNA to be sequenced. Next, many partial but shorter fragments of the 292

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segment are also replicated, each part with one less base than the prior part. Then the ending base (A,T, G, or C) is tagged with a fluorescent chemical. Then these fragments are all separated according to length and each recorded as to its length and ending base. Finally, the sequence of the bases in the original DNA segment can be reconstructed by ordering the different lengths of the DNA fragments with each identified base. The DNA segment sequence is the sequence of ending bases according to position in the DNA sequence by length of the replicated fragment.

In 1989, the sequencing of DNA was not automated and required much technician labor, with a cost of about $2 to $5 for identifying a base in a DNA sequence.

Automation was essential as the planned budget of $15 billion for the U.S. Human Genome project had been formulated at an estimate of $0.50 per base identification. Then Daniel Cohen, director of a scientific laboratory, Genethon, in Paris, France, had begun to made significant progress in automation. Cohen implemented eighteen machines, each of which could output identification of 8,000 bases per day. He had designed custom robots to place 450,000 samples of DNA per day on a membrane for reactions. Genethon published its physical map of the human genome in 1993 as an ordered set of large blocks of DNA (yet unsequenced within a block). This was used to break the human genome down into a set of blocks.

Progress in DNA sequencing machinery for automating the sequencing technique advanced rapidly, In the year 2000, a DNA sequencing machine could automatically produce 330,000 bases per day:

DNA sequencing (begins with) preparation and replication of relatively short segments of DNA, (then) the creation of partial copies of segments each one base longer than the next, (then) the identification of the last base in each copy, and (then) the ordering of the identified bases. . . .

Industrial robots shift genetic material from station to station with speed and accuracy. Sequencing machines use multiple capillaries filled with polymer gels (to add bases for DNA assembly). And DNA assembly computer systems . . . order a 120-million-base genome in less than a week. . .

(Hodgson 2000, p. 37).

We see here one of the circular interactions between science and technology, science research created a market for the technology of sequencing; and automating the sequencing technology made possible the science project of decoding the human genome.

Once a fragment of a DNA sequence was automatically analyzed as to the order of its bases, then a computer program could assemble these bases into the DNA sequence. The assembly software was developed by Phil Green and his colleagues at the University of Washington Genome Center in Seattle, Washington, USA.

One program, Pred (Phil’s read editor), looks at the fluorescent signal output from the DNA sequencers, identifies the base according to the fluorescent signal, and analyzes the quality of the signal to estimate the accuracy of the identification of the base. A second computer program, Phap (for Phil’s revised assembly program), assembles the DNA segment sequence by overlaying the fragment pieces to get PROGRESS IN BIOTECHNOLOGY 293

an end-to-end sequence of the segment. A third program, Consed (consensus visualization and editing program) allows a human finisher to see and edit the sequence of the DNA for storing in a DNA library of the genome.

The new technology in automatically sequencing DNA made possible the historic race between the human genome project and Celera: “Thanks to the efforts of hundreds of sequencing machines at work in laboratories around the globe, the rough drafts of the human genome were developed . . .” (Moore 2000, p. 35).

There were about 30,000 human genes.

Still the first draft of the “book of the human genome” was yet only a first step: “The completion of the human genome project does not represent an end for genomics. The human sequence still contains gaps. . . . Furthermore, there are lots of genomes of other important species to be sequenced. . . . (Also there are) large studies to track the effects of genetic variation within human populations” (Hodgson 2000, p. 37).

Sequencing DNA was a first step to understanding how DNA controls the construction and operation of a human. Next, the genes within each chromosome needed to be identified. It is a gene that has functional use for the organism. Once genes are identified, their functionality must be established. This is still a very complex work in progress.

PROGRESS IN BIOTECHNOLOGY

Progress in biotechnology is a prime example of a scientific technology—requiring interactive progress in both science and technology. Science provides the understanding of how and why biological systems operate. Technology provides the techniques for manipulating biological systems. Medicine is the application of scientific knowledge and technological instrumentation to diagnose and cure diseases and injuries.

CASE STUDY:

MIT Biotechnology Process Engineering Center

How, then, can research to simultaneously advance science and technology be done? Multidisciplinary research centers at universities provide a way to do this.

Biotechnology processing technology was established under an NSF/ERC grant at the Massachusetts Institute of Technology in 1985—The MIT Biotechnology Process Engineering Center (BPEC). BPEC was founded and directed by Professor Daniel Wang. In this center, university and industrial researchers had planned basic research for the technologies of producing biotechnology products grown in mammalian cell cultures. It has successfully operated over two decades (http://web.mit.BPECresearch.

html). We now look at its research plan of 1991 (Betz 1993, p. 445).

The technology system for biotechnology production divides into a bioreactor (which grows the cell cultures) and a recovery system (which recovers the desired proteins produced by the cells).

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Within the bioreactor, the technological performance variables for protein production are:

• Number of cells

• Functioning of each cell

• Quantity of the protein produced by the cells

• Quality of the protein produced by the cells

The products of biotechnology cell production are particular proteins that have commercial use in therapeutic medicine. The quantity of protein is a measure of the productivity. Proteins are long chains of organic molecules that fold back in particular configurations. The usefulness and quality of the protein depends not only on having the right molecules link together in the proper construction but also on the ability to fold up into the right pattern. One of the technical bottlenecks in protein production was to get the proper folding.

For the design of bioreactors (for the production of proteins from mammalian cells), the biotech engineer needed to design reactors that

• Grow a high density of cells

• Formulate the proper medium for their growth

• Provide proper surfaces for mammalian cells to attach to

• Control the operation of the reactor

• Provide for recovery of the proteins that are grown in the cells For the design of the recovery processes of the protein product from the bioreactors, the biotech engineer needed to control the physics and chemistry of protein structures, including

• Protein-protein interactions that avoided aggregation of proteins or mutations of proteins

•

Protein-surface interactions that affect aggregation of proteins or absorption of proteins to separation materials

• Protein stability that affected the durability of the protein through the separation processes

With this kind of system analysis of the bioreactor and recovery system and the important technological variables, the MIT Biotechnology Process Engineering Center had identified the research areas necessary to provide improved knowledge for the technology.

Daniel Wang is currently a professor in the Chemical Engineering Department at MIT. He obtained bachelor of science in biology from MIT in 1959 and a PROGRESS IN BIOTECHNOLOGY 295

master’s degree in chemical engineering in 1961. In 1963, he obtained Ph.D. in chemical engineering from the University of Pennsylvania. His research interests are in bioreactor engineering and the production of recombinant proteins. He has been a member of the U.S. National Academy of Engineering since 1986.

For the bioreactor portion of the system, the center had listed the following scientific phenomena as requiring better understanding: 1. Extracellular biological events

• Nutrition and growth factors of the cells

• Differentiation factors of the cells

• Redox (reduction-oxidation) oxygen conditions in the cellular processes

• Secreted products of the cells

2. Extracellular physical events

• Transport phenomena of materials in the medium

• Hydrodynamics of fluid-gas interactions of the medium

• Cell-surface interactions

• Cell–cell interactions

3. Intracellular events and states

• Genetic expression of the proteins in the cells

• Folding of the proteins and secretion from the cells

• Glycosylation of the proteins

• Cellular regulation of secretion

• Metabolic flows in the cells and their energetics We see this is a list of the biological and physical phenomena underlying the cellular activities in the bioreactor.

For the recovery process of the system, the center identified the scientific phenomena that required better understanding:

1.

Protein–protein

interactions

• Aggregation of proteins into clumps

• Mutations of protein structure

2.

Protein–surface

interactions

• Aggregation of proteins through denaturation

• Adsorption of the proteins to surfaces

3.

Protein

stability

• Surface interaction

• Chemical reaction

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• Aggregation in the solvent

• Stabilization

Accordingly, in 1992, the Center had organized its research into two areas: 1. Engineering and scientific principles in therapeutic protein production 2. Process engineering and science in therapeutic protein purification In the area of therapeutic protein production, the research projects were:

• Protein trafficking and posttranslational modifications: glycosylation and folding

• Redox potential

• Pathway analysis

• Intercellular energetics

• Regulation of secretion

• Hydrodynamics: gas sparging

• High-density bioreactor designs

• Substrata morphology for cell attachment

• Expression: transcription factors

The research projects in the second research area of protein separation were:

• Protein adsorption: chromatography and membrane

• Protein aggregation

• In vivo protein folding

•

Protein stability in processing, storage, and delivery Research for technology cannot be planned until after a basic technological invention has occurred. After the basic technology invention, research then can be planned by focusing on the generic technology system, production processes, or underlying physical phenomena. Technology-focused-and-targeted basic research can be planned for:

1. Generic technology systems and subsystems for product systems 2. Generic technology systems and subsystems for production systems 3. Physical phenomena underlying the technology systems and subsystems for product systems and for production systems.

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The physical phenomena underlying a biological system can be focused on any of the system aspects:

• Phenomena involved in the system boundary

• Phenomena underlying components

• Phenomena underlying connections

• Phenomena underlying materials

• Phenomena underlying power and energy

• Phenomena underlying system control

We can see in this illustration that in planning basic research for progress in science and in engineering, the research planning in the Center had: 1. Described the current technology system being used in industry 2. Identified technology performance variables for a new technology system 3. Identified the underlying scientific phenomena of a new technology that needs better scientific understanding

4. Formulated sets of research projects to investigate the scientific phenomena 5. Used the scientific results as the basis for new inventions or design aides or control procedures for a new technology

UNIVERSITY RESEARCH CENTERS FOR SCIENCE

AND TECHNOLOGY

Development of new scientific technologies to improve an existing technology requires multidisciplinary research in centers that span both science and engineering disciplines in universities. University research centers should be multidisciplinary in science and engineering disciplines and guided by a research vision for simultaneous progress in both science and technology—the next generation of scientific technology.

The proper leadership of a center director is critical to the performance of a university research center. The two basic qualifications for a professor to become a university research center director are: (1) a scientifically exciting multidisciplinary research vision and (2) the ability to successfully win a research grant for a center.

The critical element of a center director’s research vision is the ability to envision and assemble pieces of research as center projects that together will coherently yield integrated scientific and technological progress. A strategic research plan for a multidisciplinary university research plan should be expressed as sets of projects grouped on three planes of research goals: (1) a science base, (2) a set of new technology subsystems, and (3) experimental test-beds for prototyping a next-generation of the technology system.

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KEY IDEAS

In the case studies of:

• Origin of molecular biology

• Origin of the biotechnology industry

• Progress in gene therapy

• MIT Biotechnology Process Engineering Center

We have examined the key ideas of:

• Science bases for technology

• Scientific progress and industrial profitability

• University research centers

15

ETHICS AND TECHNOLOGY

INTRODUCTION

Finally, let us turn to ethical issues. Primarily, the ethical issues about technology are safety and security. Is the technology safe and secure to use? Safe to whom?

To the immediate user? To neighbors? To the environment? To future generations?

The ethical issues of safety and security become more complex when they include the environment and future generations.

Are there cases of a technology that is never safe to use—for anyone and for the environment and the future? Are there technologies from which no good can result in their application? We will denote such technologies-never-to-be-used as inherently bad technologies—intrinsically evil technologies.

The questions we will address include the following:

• What are the performance measures that impact the safety of a technology’s use?

• Do the ethical implications of a technology lie only in its use, or can they ever be implicit in the technology itself?

•

For a technology that should never be used, how is it to be controlled?

Technology and Ethics

Research

Invent

Commercialize

Market

Nature

Science

Technology

Economy

Utility

Exist

Discover

Manipulate

Embed

Use

naturally

nature

nature

nature

nature

The world as material

The world as money

Figure 15.1 Innovation process

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300 ETHICS

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As shown in Figure 15.1, we turn to understanding the ethical implications of technology applications.

CASE STUDY:

Monsanto’s Genetically Modified Seeds

In 1997, Robert B. Shapiro was chairman and CEO of Monsanto Company, based in St. Louis, Missouri. In an interview by Joan Magretta, Robert Shapiro explained how Monsanto had taken the initiative to reorient itself toward a future business in environmentally sustainable economies (Magretta 1997). It was a major strategic reorientation for Monsanto, since much of its traditional chemical business was in agricultural chemicals.

As an example of economy and environment, Shapiro used current potato production practices in the United States. For farmers to grow an annual crop of potatoes from seed potatoes, they needed to use pesticides against insects and viruses that would damage the crop. To produce the quantity of annually used pesticides required:

1. Starting with 4,000,000 pounds of raw materials and 1,500 barrels of oil, 2. To produce 3,800,000 of inert ingredients and 1,200,000 pounds of insecticide (plus 2,500,000 pounds of waste),

3. Which provided 5,000,000 pounds of formulated insecticide product (in 180,000 containers of packages),

4. Along with 150,000 gallons of fuel to distribute and apply the insecticide.

Of this 5 million pounds of insecticide, only 5 percent actually reached the target pests of the potato crop. The other 95 percent (4.75 million pounds) of insecticide uselessly impacted the environment. One of the major potato-growing areas in the United States where these insecticides were being applied was in Colorado, used to control the Colorado potato beetle.

How could this kind of useless environmental impact be changed? This was Shapiro’s charge to his colleagues in Monsanto—find alternative technologies that could be environmentally friendly. Monsanto’s early investment in the new biotechnology in the 1980s provided new a technological approach. Monsanto’s scientists used biotechnology to develop what Monsanto called its NewLeaf potato. This potato had been bioengineered to defend itself against the destructive Colorado potato beetle, and in 1995 it was used on potato farms in Colorado. Adding to the NewLeaf potato’s resistance, Monsanto engineered it also to include resistance to leaf virus.

Shapiro’s positive attitude toward environmental challenges was practical.

It was based on new business opportunities that new technology (biotechnology) was making possible: “We can genetically code a plant, for example, to repel or destroy harmful insects. That means we don’t have to spray the plant with pesticides . . . If we put the right information in the plant, we waste less INTRODUCTION

301

stuff and increase productivity. With biotechnology, we can accomplish that” (Magretta,1997, p. 82).

Monsanto’s strategy was to move from being primarily a supplier of pesticides to a supplier of biotechnology-engineered seeds and seed plants. The ability of Monsanto to see new business opportunities in a future of environmentally sustainable economy was due a succession of visionary business leaders who thought strategically about the future. As Shapiro commented: My predecessor, Dick Mahoney, understood that the way we [Monsanto]

were doing things had to change. . . Dick got way out ahead of the traditional culture in Monsanto and in the rest of the chemical industry.

He set incredibly aggressive quantitative targets and deadlines . . . In six years, we reduced our toxic air emissions by 90 percent . . . Dick put us on the right path (Magretta,1997, p. 84).

This new biotech strategy for Monsanto had been strategically initiated earlier in September 1983 by a previous CEO, Richard (Dick) Mahoney. The traditional businesses of Monsanto had been in basic chemicals whose technologies of production had been relatively stable for years with no new radical invention. Basic chemical in the 1980s had become commodity-type products. Mahoney sold some of these traditional businesses that were no longer profitable (including some fiber operations, a petrochemical plant in Texas, nylon operations in Europe, and a subsidiary chemical and plastic firm in Spain). He then formulated a new technology strategy for Monsanto toward biotechnology.

In an interview with the Chemical & Engineering News in 1983, Mahoney had explained: “Probably the one (area) that gets most press at Monsanto is the biological component. Here I’m talking about our existing business in agriculture and a new business in nutrition chemicals, which we started a few years ago to move into animal health and nutrition, and what we hope will be a substantial effort in human health care area . . . ” ( Chemical & Engineering News 1983, p. 10).

Mahoney described Monsanto’s in biotechnology:

We have perhaps $100 million of the $300 million per year in discovery—

basic research. And $200 million in one form or another or applied research. Of the biotechnology area, virtually all of it is a component of the $100 million (basic research)—say $30 million or thereabouts.

So about one third of our basic science is going into biotechnology . . .

(Chemical & Engineering News 1983, p. 13).

It was the earlier CEO’s (Mahoney’s) technology vision and strategy, that provided the knowledge base for the next CEO, Shapiro, to build on—for Monsanto’s strategic future. In the later interview, Shapiro said: Ultimately, we’d love to figure out how to replace chemical-processing plants with fields of growing plants—literally, green plants capable of 302 ETHICS

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producing chemicals. We have some leads: we can already produce polymers in soybeans, for example. But I think a big commercial breakthrough is a long way off . . . I am not one of those techno-utopians who just assume that technology is going to take care of everyone. But I don’t see an alternative to giving it out best shot . . . The market is going to want sustainable systems, and if Monsanto provides them, we will do quite well for ourselves and our shareholders (Magretta 1997, p. 84).

The visions of Monsanto’s CEOs illustrate the important contribution of that technology-savvy leadership can make toward a firm’s long-term future. Shapiro suggested: “Sustainable development is going to be one of the organizing principles around which Monsanto and a lot of other institutions will probably define themselves in the years to come” (Magretta 1997, p. 84).

TECHNOLOGY IN ETHICS AS “MEANS AND ENDS”

In the secular world of science and technology, nature is viewed as without ethical features. In science and technology, it is only how humans use or abuse nature that brings ethics into the picture. Nature is the phenomenal universe in which humans exist. Ethics is a judgment on the values of human actions in this larger phenomenal universe.

Ethics is a complex topic and beyond the scope of this book, but we can look specifically at the relationship of technology to ethics by viewing technology as a kind of means-to-ends of an action. In general, ethics is about choices of end-of-action, and technology is a means-of-action. Many of the ethical concerns about technology are about the ends to which technological systems are the means.

As a means, technologies are judged, not as good or evil, but as efficient or inefficient toward an end.

It is the ends, the applications of technology, which are judged as good or evil.

CASE STUDY (CONTINUED):

Monsanto’s Genetically Modified Seeds

As we have learned by now, technology stories are never simple. So, too, the story of Monsanto’s biotechnology strategy became complicated—due to ethics. In the Monsanto’s NewLeaf product, there was technical agreement that the product was an efficient means to control the Colorado potato beetle potato. But there would be ethical disagreements about its ends, its applications. Did the genetically altered seed also harm other insects or foster evolutionary mutations in the beetle?

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Monsanto had not anticipated the political storm that arose when it tried to import genetically modified agricultural seeds to Europe. In particular, a modification of corn seed, called Bt corn, nearly set off a trade war between Europe and America.

In November 2007, the European Union (EU) government banned Monsanto’s products in Europe. Elizabeth Rosenthal reported:

BRUSSELS—A proposal that Europe’s top environment official made last month, to ban the planting of a genetically modified corn strain, sets up a bitter war within the European Union, where politicians have done their best to dance around the issue. The environmental commissioner, Stavros Dimas, said he had based his decision squarely on scientific studies suggesting that long-term uncertainties and risks remain in planting the so-called Bt corn. But when the full European Commission takes up the matter in the next couple of months, commissioners will have to decide what mix of science, politics and trade to apply (Rosenthal 2007).

The EU had resisted introducing the new technology into European agriculture: For a decade, the European Union has maintained itself as the last big swath of land that is mostly free of genetically modified organisms, largely by sidestepping tough questions. It kept a moratorium on the planting of crops made from genetically altered seeds while making promises of further scientific studies. But Europe has been under increasing pressure from the World Trade Organization and the United States, which contend that there is plenty of research to show such products do not harm the environment. Therefore, they insist, normal trade rules must apply” (Rosenthal 2007).

While the genetic engineering science progressed in industry, the science of the environmental impacts had not kept pace with the technical knowledge. The environmental science was not certain, as Rosenthal reported: Within the European scientific community, there are passionate divisions about how to apply the growing body of research concerning genetically modified crops, and in particular Bt corn. That strain is based on the naturally occurring soil bacterium Bacillus thuringiensis and mimics its production of a toxin to kill pests. The vast majority of research into such crops is conducted by, or financed by, the companies that make seeds for genetically modified organisms . . .

Rosenthal interviewed an ecologist at the Swiss Federal Institute of Technology in Zurich, Angelika Hilbeck:

“Ms. Hilbeck says that company-financed studies do not devote adequate attention to broad ripple effects that modified plants might cause, like 304 ETHICS

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changes to bird species or the effect of all farmers planting a single biotechnology crop. She said producers of modified organisms, like Syngenta and Monsanto, have rejected repeated requests to release seeds to researchers like herself to conduct independent studies on their effect on the environment (Rosenthal 2007).

Also, the problem of evolution—adaptation of species—can be a problem:

“For ecologists and entomologists, a major concern is that insects could quickly become resistant to the toxin built into the corn if all farmers in a region used that corn, just as microbes affecting humans become resistant to antibiotics that are prescribed often” (Rosenthal 2007).

Lessons about ethics were being learned by Monsanto. This could be seen in December 2009, when Monsanto’s first seed patent on soybeans ended. Andrew Pollack reported:

Facing antitrust scrutiny over its practices in the biotechnology seed business, Monsanto said it will not stand in the way of farmers eventually using lower cost alternatives to its genetically modified soybeans. . . .

Monsanto would allow farmers to continue to grow its hugely popular Roundup Ready 1 soybeans even after the patent protecting the technology expires in 2014 (Pollack 2009, p. B3).

Monsanto’s seed contains a gene that protects the plant from a herbicide, glyphosate, which is widely used. The gene allows farmers to control weeds without harming the soybean plant. In the United States, most of the soybeans (90 percent) grown there are protected by the gene. Two-thirds of the U.S. crops of corn and cotton are also protected by seeds with patented gene protection against herbicides.

Monsanto’s policy may establish important commercial precedents: “Because farmers and seed companies would no longer have to pay royalties to Monsanto on the gene after 2014, Roundup Ready soybeans would become agricultural biotechnology’s equivalent of a generic drug. . . . Roundup Ready seed can cost as much as $75 an acre compared with $30 to $35 for soybeans that are not genetically modified . . . ” (Pollack 2009, p. B3).

Also, farmers can no longer save seeds for future crops: “Many soybean farmers used to save seeds, but with Roundup Ready seeds they have been contractually obliged to buy new seeds each year. Monsanto has taken legal action against hundreds of farmers it has accused of saving seed” (Pollack 2009, p. B3).

Meanwhile Monsanto’s continuing research in biotechnology was enabling it to introduce next-generation-technology products: “Monsanto said it was confident that most farmers and seed companies would move to Roundup Ready 2, which uses the same bacterial gene but places it in a different location in the soybean DNA. Monsanto said Roundup Ready 2 crops would have higher yields, and other desirable traits would be added to those crops over time” (Pollack 2009, p. B3).

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But Monsanto’s ethics problems in genetically modified agriculture were turning out to be a continuing challenge. For example, use of one of its herbicides, Roundup, in U.S. agriculture sped up the evolutionary race with weeds. William Neuman and Andrew Pollack reported:

“For 15 years, Eddie Anderson, a farmer, has been a strict adherent of no-till agriculture, an environmentally friendly technique that all but eliminates plowing to curb erosion and the harmful runoff of fertilizers and pesticides. But not this year (2010). On a recent afternoon here, Mr. Anderson watched as tractors crisscrossed a rolling field— plowing and mixing herbicides to kill weeds where soybeans will soon be planted. Just as the heavy use of antibiotics contributed to the rise of drug-resistant supergerms, American farmers’ near-ubiquitous use of the weed-killer Roundup has led to the rapid growth of tenacious new super-weeds.” (Neuman and Pollack 2010).

Monsanto’s inventive idea was a commercial success:

“Sales took off in the late 1990s, after Monsanto created its brand of Roundup Ready crops that were genetically modified to tolerated the chemical, allowing farmers to spray their fields to kill the weeks while leaving the crop unharmed. Today, Roundup Ready crops account for about 90 percent of the soybeans and 70 percent of the corn and cotton grown in the United States.” (Neuman and Pollack 2010).

Weed species responded to their new environmental challenge in Roundup:

“But farmers sprayed so much Roundup that weeds quickly evolved to survive it. “What we’re talking about here is Darwinian evolution in fast-forward,” Mike Owen a weed scientist at Iowa State University, said. . . .

Now Roundup-resistant weeds, like horseweed and giant ragweed, are for

cing farmers to go back to more expensive techniques that they had long ago abandoned (Neuman and Pollack 2010).”

Nature is not simple. Progress in the science to understand the complexity of nature is essential for the ethical use and control of scientific technologies.

ETHICS AND REGULATION

In business, the ethical implications of the application of a technology are expressed in government regulation about safety and in legal suits over injury due to negligent use of technology. When introducing radical new technologies, a large company should look ahead and know the ethical implications of a new technology. Otherwise, protest movements may impede innovation and prove more costly in the long run than a foresighted and ethical technology policy.

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CASE STUDY:

Cyber-Attack in 2009–07–09

Security as well as safety is an important ethical concern in the application of innovative technologies. For example, as soon as the Internet was innovated, malicious attacks were made by software, called viruses, to destroy Internet sites or render them unusable or unavailable to users. Thomas Chen and Jean-Marc Robert described these:

“Computer viruses and worms are characterized by their ability to self replicate. . . . Computer viruses and network worms have evolved through a continuous series of innovations, leading to the recent wave of fast-spreading and dangerous worms. . . . First, while viruses were more common than worms initially, worms have become the predominant threat in recent years, coinciding with the growth of computer networking. Second, despite widespread use of firewalls and other network security equipment, worm outbreaks still occur. . . . Third, recent worms are appearing as a series of quick successive variants. . . . Fourth, recent worms have shown capabilities to spread faster and exploit more infection vectors. . . . Finally, more dangerous payloads are becoming com-monplace. This suggests that worm creators are using worms for other objectives than simply infection, such as data theft and setting up denial of service networks” (Chen and Robert 2004).

As an example of these “other objectives,” Joseph Menn and Song Jung-a reported a cyber-attack on government websites:

“Officials in the U.S. and South Korea were scrambling on Wednesday (8 July 2009) to combat an unusually powerful cyber-attack that overwhelmed both government and private sector websites. Malicious code recovered by researchers showed a batch of computers were instructed to attack websites in both nations. South Korean authorities said they suspected North Korea of being behind the electronic campaign. . . . The sources of most cyber-attacks are never uncovered. US officials said they did not know who was behind the assault. “We can’t accurately identify who an attacker is in cyberspace,” a White House official said.” (Menn and Song 2009).

As the technology of the Internet developed, a need for security while using the Internet developed along with it. Ethical concerns surrounding technology are not only about how to use a technology safely but also how to use it securely.

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ETHICAL IMPERATIVE AND TECHNOLOGY IMPERATIVE

When we began, we considered an imperative, a universal command, in technology: The imperative in technological innovation is that superior technology of a competitor cannot be ignored by other competitors, except at their peril. Is there a parallel to this in ethics? Can there be a universal ethical imperative about technology?

Ethical imperatives are a tradition in all religions. Such an imperative would be a law commanded by a deity through a prophet to a religious people. But science lies in the secular domain of society and not the religious domain. So an ethical imperative in technology must arise from a nonreligious source. In traditional philosophy, a famous ethical imperative was that of Immanuel Kant in his Critique of Practical Reason: “So act that the point of your action could be universally willed” (Kant 1788). This idea of universalization of action is a key tenet in any secular ethics.

Could one apply this principle to technology—but in a reverse way? Is there any technology that cannot be applied usefully by anyone in any context? This means that such a technology could have no universal use whatsoever.

There are historical instances. For example, in World War I in Europe, poisonous gas was used in battlefield applications. It was lethal and crippling. But it was also uncontrollable. If the wind shifted during an application, then the poison could be blown back over the user. Moreover, poisonous gas gave no military advantage, since an attack could not begin in force until after the gas dispersed. Therefore, poison gas attacks were militarily useless; after the war the international European community banned its use in future warfare. It was not used in World War II by the Europeans, even though arsenals of poisonous gas did exist for use if an opponent broke the convention.

When there is a technology to which there can never be an appropriate or good end for anyone, we will call this an intrinsically evil technology. This is in the spirit of the Kantian ethic—willing its use to no one at any time in all contexts.

Thus, ethically, technology should be used as a means to acceptable ends—good ends. As a means to good ends, technology is ethically judged merely as efficient or inefficient and as safe or unsafe and secure or insecure. But if there are never any acceptable ends for a technology as a means—only bad ends universally—so that the technology should never be used by anyone at anytime—we can call that technology intrinsically evil.

CASE STUDY:

Cuban Missile Crisis

Nuclear bombs provide an example of an intrinsically evil technology, since they not only destroy a present population but also radioactively contaminate an area for future generations. Moreover, if a sufficient quantity of nuclear weapons (about 1000 1–5 megaton warheads) were all exploded, a nuclear winter would likely ensue, destroying all human civilizations on earth. The challenge of controlling 308 ETHICS

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this intrinsically evil technology depended on the political policies and processes to avoid a nuclear war.

In the second half of the twentieth century both the United States and the Soviet Union had armed themselves with many, many hydrogen bombs—deliverable by intercontinental ballistic missiles, airplanes, and submarines. The thousands of hydrogen bombs that could be launched would annihilate all the population of each country and also exterminate all the other populations of the world. A nuclear war would be the annihilation of the human race—a global extinction of species.

The strategic policy for controlling this vast and extensive arsenal of annihilation weapons was called by U.S. policy makers “mutually assured destruction” (MAD). It assumes that each government has the nuclear capability to destroy the other; if one is attacked, the other would retaliate with equal or greater force, thus ensuring their mutual destruction.

Even through a technology may be recognized by all as intrinsically evil—

harming everyone in its application at all times—it still may be applied in the real world.

The challenge of an application of an intrinsically evil technology is for control that the application is never used.

The control of nuclear weapons faced a major test in the Cuban Missile Crisis of May 1963. Many books have been written on the subject (see, for example, Allison and Zeidow 1999). Because of its extensive documentation, it provides a clear example of the dangers of trying to control the application of an intrinsically evil technology. Such control turns out not to be easy.

The background to that crisis lay in the Cold War between America and Russian that began in 1947 and lasted to 1989. At the end of World War II, Europe was divided into two territories: Western Europe, occupied by the American and British armies, and Eastern Europe, occupied by the Soviet army. The countries under Soviet control had communist regimes installed, whereas the American and British fostered the building of democratic governments. The line between the two territories of communist and democratic governments was called by Winston Churchill the Iron Curtain.

The Korean War began in 1950, when the communist regime in North Korea invaded South Korea and United Nations forces, led by the Americans, repulsed the North Korean army and advanced to the border of China. Then China entered the war and pushed the United Nations armies back to the line between North and South Korea. General MacArthur, leading the UN army, advocated the use of nuclear bombs on China; the then-president of the United States, Harry Truman, fired him.

In Europe, the Soviet army was the largest army in the world, and as most of the American army had withdrawn from Europe, Western Europe was militarily vulnerable to any Soviet army attack into Western Europe. After the “hot war” in ETHICAL IMPERATIVE AND TECHNOLOGY IMPERATIVE 309

Korea, the new American president, Dwight D. Eisenhower, announced a defensive policy for Europe by which any Soviet invasion of West Europe would be met by America launching atomic bombs against the Soviet Union. This was the first military strategy to explicitly use the threat of nuclear war. The further development in nuclear weaponry escalated this nuclear strike doctrine.

The first American hydrogen bomb was successfully tested on November 1, 1952, yielding 10.4 megatons of energy—450 times stronger than the atomic bombs detonated over Hiroshima and Nagasaki. One hydrogen fusion bomb equaled 450 atomic bombs! The Soviets exploded their first fully successful hydrogen bomb three years later on November 22, 1955.

Nuclear weapons were not the only threat. The ballistic missile was innovated during World War II by Warner Von Braun in Nazi Germany and used to bomb London in 1944. Its warhead was made of TNT dynamite and could destroy a city block. After that war, new ballistic missiles were developed to fly over the North Pole—intercontinental ballistic missiles (and Von Braun would help lead the U.S. missile development effort). By 1960, intercontinental ballistic missiles would be armed with hydrogen bombs, which would destroy not just a city block but a whole city.

As the Cold War evolved, the threat of a nuclear-armed ballistic missile attack was called a nuclear strike. The launching of only one hundred nuclear missiles could destroy all the major urban areas of the United States or of the Soviet Union or Europe. A nuclear first strike by the United States or the Soviet Union would destroy everyone in the North Hemisphere of the world—the nuclear war would be over but everyone would be dead or dying.

In the 1960s, the immediate events that led to the Cuban Missile Crisis had, in part, been triggered by an American government decision to try to remove the government of Fidel Castro in Cuba. The American CIA trained and armed Cuban émigrés in Florida to invade Cuba—landing at the Bay of Pigs in April 1961. But Cuban military forces quickly stopped the invasion and captured the émigré fighters.

Castro feared further invasions and asked the Soviet Union for assistance. On May 1, Castro declared Cuba to be a socialist republic (communist) and began to receive USSR arms. Also in 1961, the U.S. had installed medium-range ballistic missiles with hydrogen bomb warheads in Turkey. These missiles had a 1,500-mile range and could attack the Soviet Union. So in 1961, Soviet Premier Nikita Khrushchev agreed with Castro to secretly place strategic nuclear missiles in Cuba—with a range to attack the United States.

The next year, in February 1962, the United States imposed an economic embargo against Cuba. Then on October 15, 1962, an American U-2 spy plan flying intelligence missions over Cuba photographed the construction in Cuba of launch sites built for Soviet intermediate range ballistic missiles.

These missiles were armed with nuclear warheads with a range of 4,000 kilometers (2,400 miles). They would have been capable of reaching major cities in the southern states of the United States, including Miami. These Soviet SS-4

missiles had arrived in Cuba at night on September 8. And construction had begun 310 ETHICS

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immediately for nine Cuban sites from which to launch the missiles. But even on September 11, the Soviet government said it had no need to install nuclear weapons outside the USSR. Khrushchev personally communicated to U.S. President John F. Kennedy that the Soviet Union would not install offensive weapons in Cuba.

On October 16 (the day after the U-2 flight), President Kennedy was shown U-2 photos of the construction of a launch site for a Soviet SS-4 missile at San Cristobal in western Cuba. He assembled the executive committee of the U.S.

National Security Council (ExComm), with fourteen key officials and his brother Robert, the U.S. attorney general. The committee discussed five courses of action: (1) doing nothing, (2) using diplomatic pressure to get the missiles removed, (3) an air attack on the missiles, (4) a military invasion of Cuba, (5) a naval blockade of Cuba. Kennedy decided first on a naval blockade. If that did not work to remove the missiles, then an invasion would be next.

On October 22, 1962, President Kennedy addressed the U.S. nation on the establishment of Soviet missiles in Cuba and announced a naval blockade on all ships approaching Cuba: “To halt this offensive buildup, a strict quarantine on all offensive military equipment under shipment to Cuba is being initiated.” The next evening, October 23, at 10:52 P.M., Premier Khrushchev sent a tele-gram to President Kennedy: “If you coolly weigh the situation that has developed, not giving way to passions, you will understand that the Soviet Union cannot fail to reject the arbitrary demands of the United States, and that the Soviet Union views the blockade as ‘an act of aggression’ and their ships will be instructed to ignore it.” (http://www.gwu.edu/nsarchiv/nsa/cuba_mil_cri) That same night, President Kennedy instructed the U.S. Strategic Air Command to go on alert status. This was called DEFCON 2, one decision level down from launching a military attack at DEFCON 1. In the morning of October 24, the president informed his executive committee that he believed only an invasion would remove the missiles from Cuba but would for the time continue the blockade.

Meanwhile, on October 24, a Soviet diplomat and agent, Aleksandr Fomin, asked an American news reporter, John A. Scali of ABC News, to an urgent lunch.

Fomin worried that “war seems about to break out.” He asked Scali to talk to his contacts at the U.S. State Department about a diplomatic solution in which the Soviets would remove the missiles in exchange for a public commitment that the United States would never invade Cuba. At 6:00 P.M. that same day, the State Department received a telegraph from Premier Khrushchev with a similar proposal to Fomin’s.

The crisis continued. On the morning of October 27, the CIA reported to the U.S. president that three of the four sites at San Cristobal were completed and operational. Then Moscow’s Voice of Russian broadcast a message from Khrushchev with a new condition for trade—the Russian missiles from Cuba would be removed if the American missiles in Turkey were removed: You are disturbed over Cuba. You say that this disturbs you because it is ninety miles by sea from the coast of the United States of America.

But . . . you have placed destructive missile weapons, which you call ETHICAL IMPERATIVE AND TECHNOLOGY IMPERATIVE 311

offensive, in Turkey, literally next to us. . . . I therefore make this proposal: We are willing to remove from Cuba the means which you regard as offensive. . . . Your representatives will make a declaration to the effect that the United States . . . will remove its analogous means from Turkey . . . and after that, persons entrusted by the United Nations Security Council could inspect on the spot the fulfillment of the pledges made. (www.gwu.edu/nsarchiv/nsa/cuba_mil_cri)

That same morning of the 27th, a Russian surface-to-air missile, S-75, was launched by a Soviet commander in Cuba and shot down a U.S. surveillance aircraft Lockheed U-2. In an interview forty years later, McNamara said: We had to send a U-2 over to gain reconnaissance information on whether the Soviet missiles were becoming operational. We believed that if the U-2

was shot down that—the Cubans didn’t have capabilities to shoot it down, the Soviets did—we believed if it was shot down, it would be shot down by a Soviet surface-to-air-missile unit, and that it would represent a decision by the Soviets to escalate the conflict. And therefore, before we sent the U-2 out, we agreed that if it was shot down we wouldn’t meet, we’d simply attack. It was shot down on Friday. Fortunately, we changed our mind, we thought “Well, it might have been an accident, we won’t attack.” Later we learned that Khrushchev had reasoned just as we did: we send over the U-2, if it was shot down, he reasoned we would believe it was an intentional escalation. And therefore, he issued orders to Pliyev, the Soviet commander in Cuba, to instruct all of his batteries not to shoot down the U-2. (www.lbjlib.

utexas.edu/archives.hom/oralhistory.hom/McNamaraR/McNamara.asp) A Soviet ship carrying more missiles was approaching the Cuban waters. U.S.

warships had orders to intercept and board the ship. But that traditionally would be considered an act of war between the United States and the USSR. The time remaining before that confrontation was eight hours. Time to prevent war was running out.

Meanwhile, Robert Kennedy had met with a Soviet agent to determine if a trade of Turkish missiles removal for Cuban missiles removal was real.

But the consensus in the President’s EXCOM meetings was still against the proposal, as it would undermine NATO and the Turkish government was against a trade. Finally, the president decided to ignore Khrushchev’s second offer about the missile trade—as least publicly (agreeing to do it only privately) and sent a message accepting Khrushchev’s first offer to remove the missiles for an American guarantee never to invade Cuba.

A letter was delivered to Premier Khrushchev at 8:05 P.M. that same evening of October 27 from President Kennedy:

As I read your letter, the key elements of your proposals—which seem generally acceptable as I understand them—are as follows: 1) You would 312 ETHICS

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agree to remove these weapons systems from Cuba under appropriate United Nations observation and supervision; and undertake, with suitable safe-guards, to halt the further introduction of such weapon systems into Cuba. 2) We, on our part, would agree—upon the establishment of adequate arrangements through the United Nations, to ensure the carrying out and continuation of these commitments (a) to remove promptly the quarantine measures now in effect and (b) to give assurances against the invasion of Cuba. (www.gwu.edu/nsarchiv/nsa/cuba_mil_cri) Meanwhile, still on October 27, while at sea and awaiting the arrival of the Soviet ship delivering additional missiles, a U.S. destroyer ship detected a Russian submarine in the ocean near Cuba. It dropped depth charges as a warning. But unknown to the American officer, the Soviet submarine was carrying a nuclear-tipped torpedo with authorization to use it if the submarine was damaged.

The submarine was not able to surface, and, running out of air, the commander decided to sink the U.S. destroyer with its nuclear-tipped torpedo—which would have been the first U.S.–USSR use of a nuclear weapon against each other.

Fortunately, the Russian commander changed his mind and surfaced without firing his torpedo.

At the same time, the U.S. Air Force was preparing to launch air attacks against Cuba on presidential command the next day, October 28. In Cuba, Soviet ballistic missiles were armed and ready to launch against the United States if American planes began an attack on Cuba.

Thus, within eight hours of a U.S. warship interdiction of a Russian military ship, two forms of nuclear weapons had been ready to launch under local command of military officers—a nuclear-tipped torpedo from a submarine and intermediate range ballistic missiles from Cuban missile bases.

Finally, the crisis broke. On October 28 at 9 A.M., a message from Premier Khrushchev was broadcast by Radio Moscow: “The Soviet government, in addition to previously issued instructions on the cessation of further work at the building sites for the weapons, has issued a new order on the dismantling of the weapons which you describe as ‘offensive’ and their crating and return to the Soviet Union.”

President Kennedy immediately responded:

I consider my letter to you of October twenty-seventh and your reply of today as firm undertakings on the part of both our governments that should be promptly carried out. . . . The U.S. will make a statement in the framework of the Security Council in reference to Cuba as follows: it will declare that the United States of America will respect the inviolability of Cuban borders, its sovereignty, that it take the pledge not to interfere in internal affairs, not to intrude themselves and not to permit our territory to be used as a bridgehead for the invasion of Cuba, and will restrain those who would plan to carry an aggression against Cuba, either from CONTROL SYSTEMS FOR INTRINSICALLY EVIL TECHNOLOGIES 313

U.S. territory or from the territory of other countries neighboring to Cuba. (www.gwu.edu/nsarchiv/nsa/cuba_mil_cri)

The first missile crisis was over. The United States and the Soviet Union avoided a Third World War in the twentieth century. The Soviets removed the missiles from Cuba, and six months later, the United States quietly removed its missiles from Turkey.

CONTROL SYSTEMS FOR INTRINSICALLY EVIL TECHNOLOGIES

From this example, we can see how important and critical is the operation of a control system to stop the application of an intrinsically evil technology. The ethics in the application of intrinsically evil technologies lie in establishing an effective and efficient societal control system that bans or prevents the use of the technologies.

Yet in the example, one can see how marginally effective the missile control system between the United States and the Soviet Union had been in the second half of the twentieth century. Communication between the two major leaders had been limited to telegraphs, radio broadcasts, and clandestine discussions between agents. After the crisis, a direct telephone link, called a hot line was established.

Then in a crisis, leaders in the two antagonistic countries could directly talk to each other.

In 1991, the United States and the Soviet Union still had missile defense systems but then began to reduce the number of warheads. A first treaty to mutually reduce missiles was signed between the United States under George H. W. Bush and the Soviet Union under Mikhail Gorbachev on July 31, 1991—the Strategic Arms Reduction Treaty. It allowed each country to have 2,200 operational nuclear bomb delivery vehicles. On December 31, 1991, the Soviet Union dissolved into independent nations. In December 2009, the treaty expired. The latest in the series of START treaties, the New START treaty, was signed in April 2010 between U.S.

President Barack Obama and Russian President Dmitri A. Medvedev, but then still needed ratification by the U.S. Congress for the United States. (http://nytimes

.com/2010/11/19/world/europe/start.html)

But the START treaties were only a start toward nuclear weapons elimination.

The 3,100 nuclear bombs still operational could obliterate all civilization on Earth if launched—nuclear Armageddon—the end of the world.

The ethical issues of a technology in an application are: (1) functionality, performance, efficiency, and safety.

The ethical issue of an intrinsically evil technology lies in effective political control for prevention of its use—by anyone, anywhere, any time.

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KEY IDEAS

Through the case studies of:

• Monsanto’s Genetically Modified Seeds

• Cuban Missile Crisis

We have examined the following key ideas:

• Technology as ethically neutral or intrinsically evil

• Functionality, performance, efficiency, and safety of an ethically neutral technology

• Control for prevention of use of an intrinsically evil technology III.

INNOVATION

HANDBOOK

16

INNOVATION PRACTICE

INTRODUCTION

We have reviewed the theory and implementation of innovation—and found all this to be complicated. What should we remember and use? We now construct a practical guide on how to manage this complexity. We call this a kind of handbook for formulating and implementing innovation strategy. We will summarize the theory for managing six practical innovation procedures:

1. How to manage innovation processes

2. How to manage research and development (R&D) 3. How to manage product development

4. How to manage high-tech marketing

5. How to manage service innovation

6. How to manage biotechnology innovation

HOW T

O MANAGE INNOVATION PROCESSES

Technology is the knowledge of the manipulation of nature for human purposes, consisting of a transformational logic mapped to a sequential of natural states. As shown in Figure 16.1, there are four generic categories of technologies—material, power, biological, informational—depending on the kinds of underlying nature being manipulated: inanimate and substance/matter, inanimate and form/energy, animate and substance/matter, and animate and form/idea.

Since technology manipulates nature, technological progress is limited by the phenomena of nature. As shown in Figure 16.2, all technologies are based on natural phenomena, and any natural phenomenon is cognitively finite (although nature is infinite in extent and numbers).

New basic technological innovation creates new industries that have an economic impact as sources of Kondratiev long-waves of economic cycles. Incremental 317

318 INNOVATION

PRACTICE

Inanimate

Animate

Material

Biological

Substance

technologies

technologies

Power

Information

Form

technologies

technologies

Figure 16.1 Types of technologies

innovation in industries impacts the industrial life cycle of an industry, taking it from a new high-tech industry into, eventually, a mature commodity-type industry.

There are two levels of innovation: (1) a macro-level of science and technology policies in a nation and (2) a micro-level of product/service innovation in a business.

The macro-level of a national innovation system consists of three institutional R&D

sectors—universities, industries, and governments—each of which provide activities toward radical inventions. Below that, a micro-level consists of the set of high-tech businesses that produce new products/services that embody newly invented technologies. The national innovation process consists of the procedures by which the macro- and micro-level organizations interact to create radically new technological innovation. It is in the design of high-tech products/services that new knowledge is transformed into new economic value.

Physical technologies

Natural limits

Linear portion in

physical

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of physical technologies

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to go to smaller

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period

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Figure 16.2 General form of the technology progress—comparing physical with information technologies

HOW TO MANAGE INNOVATION PROCESSES 319

Managing a National Innovation System

As shown in Figure 16.3, a national innovation system is a research and development (R&D) infrastructure in a nation which translates knowledge of nature into utility. Thus an institutional infrastructure for innovation must include at least two kinds of organizations: (1) universities for science creation and technology invention, and (2) industry for technology invention and commercialization. In addition, since science is a public good and is also costly, government agencies are necessary to fund scientific research in universities.

Although the flow of knowledge in the process of radical technological innovation is linear—in that knowledge ultimately flows from nature to utility—the infrastructure processes of generating and using knowledge are interactive and circular. As shown in Figure 16.4, the interactions within a national innovation system are circular; and there are two cycles of knowledge-creation activities: (1) a science-technology circular interaction and (2) a market-business circular interaction.

Science discovers nature in experiments and science explains nature in theory.

Experiments and theory provide technology with a scientific knowledge base about nature in which to invent new technology and improvements to existing technologies.

Technology, in turn, provides the improvement of instruments to science.

The circularity in business and market knowledge interactions happens as technology provides engineering with technical knowledge that engineers use to design products and services. Problems in engineering design stimulate further technological research to improve technologies. Business sells high-tech products/services to a market, and market applications create a demand for technical improvements in high-tech products/services.

National knowledge infrastructure

University

Nature

Government

research sector

research sector

Macro-level

Industrial

research sector

Transforming

knowledge to utility

Micro-Level

Market

High-tech

Business

product/service

Customer

Utility

Application

system

Figure 16.3 Transforming knowledge to utility 320 INNOVATION

PRACTICE

Nature

Nature

Science

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/service

Tec

Figure 16.4 Circular interactions of knowledge for radical innovation In these circularities, there has emerged the specialization of research roles in a modern national innovation system.

Figure 16.5 indicates the various roles of the different sectors in a national innovation system. The institutional sponsors of R&D are principally industry and the federal government (with some support by state governments and private philanthropic foundations). The performers of R&D are industrial research laboratories, governmental research laboratories, and universities. Industry is the principal producer of

technological progress. Universities are the principal producers of scientific progress. (Government laboratories participate in varying degrees by country in performing some technological and scientific progress.) Governments have become the major sponsor of scientific research and a major sponsor of technological development in selected areas (such as military technology).

Managing the whole of a national innovation system falls under the topic of science and technology policy of a national government. An internationally competitive national innovation system should have six properties: 1. A national innovation system should have in its industrial sectors strong research capabilities.

2. A national innovation system should have a high-quality research-oriented university sector.

3. A national innovation system should have at least one strong internationally competitive industrial or service sector.

HOW TO MANAGE INNOVATION PROCESSES 321

Government

support of

Scientific

Nature

science

progress

Science

knowledge

base

Technology

invention

Society

sees science

through technology

Engineering

use of

Industrial

technology

support

of

technical

progress

Business production of

high-tech

products/services

Economic value

in market

Utility

Figure 16.5 Transforming knowledge of nature into utility 4. A national innovation system should have a culture of valuing high-quality performance.

5. A national innovation system should have government policies that strongly fund appropriate R&D activities in universities and selected mission areas—

using peer-review to evaluate quality of research.

6. Science and technology policy must balance research for technology improvement in current industries and research to establish new internationally competitive industries in new technologies.

Managing Innovation in a Business

Technological progress impacts the future competitiveness of a business. New technology provides a competitive advantage for a technology leader and creates the necessity of innovation for a technology follower as a defensive position—the technology imperative. A manager of innovation needs to make investments in innovation and lead innovation teams to commercialize innovations. Accordingly, an innovative manager does not need to know technical details, but hires and leads technical experts to research, develop, and design new high-tech products/services.

To lead effectively, a technically savvy manager needs to think generically about technologies and also about the innovation process.

To be technically savvy, a manager needs to view businesses as operating within an industrial value chain, as shown in Figure 16.6. A manager should anticipate technological change in an industrial value chain, particularly for the industrial

sector in which a business operates. Industrial sectors identify which technologies are relevant to a business.

322 INNOVATION

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Nature

Resources

extraction sector

Materials refining synthe-

sis and shaping sector

Tools and

Information

equipment

Part and

technologies

sector

components sector

sector

Major device

manufacturer sector

Service

Retail

sector

sector

Customer

Figure 16.6 Industrial value chain

Within a business there are different levels of organizational activity that need to be properly coordinated for successful innovation. In a large corporation, innovation strategy happens in a distributed management process. As shown in Figure 16.7, all levels of an organization need to be involved in different aspects of technology strategy.

1.

At

the corporate-level of innovation strategy, strategic thinking for innovation is focused on diversification and merger strategy—based on innovation.

2.

At

the business-level innovation strategy, strategic thinking for innovation is focused on launching new high-tech business ventures—built on innovation.

3.

At

the

divisional-level innovation strategy, strategic thinking for innovation is focused on the management of technical programs for business competition—implementing innovation in new products/services/processes.

4.

At

the

operations-level innovation strategy, strategic thinking for innovation is focused on the management of technical projects for business competition—developing new products/services/processes.

Successful innovation occurs when the strategies in these levels are properly integrated. Innovation strategy should be a key part of an annual long-range business strategy. The top level must authorize and fund innovation activities, including research and new ventures. The middle level must perform corporate research and engage in launching new businesses. The bottom-level must formulate and perform HOW TO MANAGE RESEARCH AND DEVELOPMENT (R&D) 323

Corporate-level innovation strategy:

Authority

Funding corporate research

Board of directors

structure

Chief executive officer

President

Top

Business-level innovation strategy:

Down

Launching new business ventures

business 1

Top

Down

Functional

and

specialization

Bottom

up

Bottom

up

Bottom

up

Research and

Information

Divisional-level innovation strategy:

engineering

technology

managers

manager

Programs of product/service planning

Project-level innovation strategy:

Operations

Operations

Research project and product-development projects

level

level

Figure 16.7 Levels of innovation strategy in a corporation research projects for new technology and also design projects for new high-tech products/services. New technology is (1) acquired by inventing the technology or purchasing a license to use the technology from an inventor and (2) developed by research and development and designed into products/services. New technology is commercialized by engineering through product/service design projects to develop new high-tech products or services. A new venture should be planned around: (a) a new high-tech product/service, (b) the market for this, (c) the organization needed to produce it, and (d) the capital required to reach profitability.

To check on the proper integration of top-down and bottom-up perspectives in innovation strategy, one can use a checklist of strategic thinking: 1.

Check

that

top-level strategy is focused on innovation through financing research and rewarding innovation.

2.

Check

that

middle-level strategy is focused on entrepreneurial launching of new high-tech business ventures.

3.

Check

that

bottom-level strategy is properly focused on research and development projects for innovation.

HOW TO MANAGE RESEARCH AND DEVELOPMENT (R&D) As shown in Figure 16.8, research for innovation occurs in different stages of research from nature to utility. Research has both a logical process and an organizational context. In managing innovation in a business, the logical process consists of velopment

ocess

ormation

vement

vement

ation prv

ic feasibility of manipulation

very and understanding nature

acturing product design

eting and sales of product

vention—functional manipulation

Disco

echnical feasibility prototype

Scientif

In

T

Functional application prototype

Engineering product design

Engineered product prototype

Manuf

Production process de

Full-scale production system

olume production of productV Mark

Production impro

Product impro

wledge-utility transf

as a linear inno

Kno

Science research

echnology research

research

research

T

Engineering

Commercialization

Utility

Research

e to utility

Knowledge-utility transformation as a linear innovation process alue

Customer

system

et

Application

e 16.8

Mark

Figur

wledge to v

vernment

ransforming knowledg

Go

T

research sector

Product service

wledge infrastructure

Industrial

orming kno

research sector

versity

ransf

National kno

Uni

T

research sector

Business

324

HOW TO MANAGE RESEARCH AND DEVELOPMENT (R&D) 325

the different stages of research for innovation, and the organizational context for this is the corporate research laboratory.

Logic of Research Stages

Research within a corporation is performed in logical stages—from nature to utility.

Stages 1 and 2—Scientific Understanding and Feasibility of Manipulation.

The first kind of research that begins a radical innovation lies in science.

Scientific research goals are to discover and explain nature. Scientific research can make inventions in the creation of new research instrumentation or instrumental techniques. Science provides the scientific feasibility that an invention can actually be made.

Stage 3—Invention. When a basic invention is created, then knowledge moves from science toward technology. A basic invention is a clever way to manipulate the nature.

Stage 4—Technically Feasible Prototype. Whoever creates a basic invention—a scientist or an engineer or a technologist—the next logic stage of research is to demonstrate the invention. This step is accomplished by building a technically feasible prototype. This refines the invention to demonstrate a technical performance in a range of usability. A technically feasible prototype shows that the invention will do a particular function—but is not necessarily good enough for an application.

Stage 5—Functional-Application Prototype. The next research step in innovation is to build a functional-application prototype—having sufficient technical performance to effectively serve a customer’s application.

A functional-application prototype demonstrates that the technical performance of the invention is then sufficient to be successful for a particular commercial application.

Stage 6—Engineering-Product Design. The next procedural step in innovation is to create an engineering-product design that can perform a function sufficiently to expand the market of customers. The engineered-product prototype is the design of a product that can be sold in large volume into the marketplace.

Stage 7—Engineering Prototype. The engineered-product prototype is the design of a product of sufficient performance that can be sold in large volume into the marketplace.

Stage 8—Manufacturing-Product Design. Before large-volume production of a new engineered-product design can begin, the engineering design may need to be modified to manufacture it with existing or improved production processes. A manufacturing-product design modifies the engineering-product design to be volume-produced with production processes.

Stage 9—Production Process Development and Full-Scale Production System. Often in a new high-technology product or service, an existing 326 INNOVATION

PRACTICE

production system technology is not sufficient to produce the new product in volume. New production processes may need to be invented or improved.

Stage 10—Volume Production of Product Even after sales of a new high-tech product (service) begin, research can be performed to improve production and to improve product designs. The research in this stage of commercialization is essential and important to competition.

Stage 11—Production Improvement Also a new product needs to be improved to beat competitors’ products—by lowering the cost of production of the product and improving product quality. Continuing research in commercialization for improving product quality and lowering product cost is necessary to continue to beat competition.

In summary (as shown in Figure 16.9), the logic of innovation establishes categories of different kinds of research issues: science, technology, engineering, commercialization. Each stage has a different research focus.

In each stage of research, the cost increases, as shown in Figure 16.10.

Research project costs accelerate in the latter two stages of engineering and commercialization.

The critical point at which commercial risk (can the business make money from the research) begins to dominate (as opposed to the technical risk of will the technology work) is in the development of a functional prototype for the new technology (or industrial design standard). Here the engineering research and engineering development work begins. Following the successful engineering design of a new product or service, the commercialization stage begins with the research now requiring participation not only of scientists and engineers but also marketing personnel and managers to target the research for a commercial product (or service or process).

And in the engineering and commercialization stages, the costs of the research and development and design and production begin to greatly escalate from previous research costs (science and technology research).

In the commercialization stage of innovation, the costs continue to rise rapidly as design of a new product (or service or process) is developed and technical problems to make the new technology work in the design must be solved. Here, engineering and marketing personnel dominate in the design phase, with scientists playing a Science

Technology

Discovery

Invention

Theory

Problem-solving

Engineering

Commercialization

Design

Standards

System

Production

Figure 16.9 Types of research issues

HOW TO MANAGE RESEARCH AND DEVELOPMENT (R&D) 327

Research

Utility

Fundamental

Applied

Technology

Commercialization

research

research

development

New knowledge as discovery

New knowledge as

New knowledge as improvement

New knowledge as proprietary

and understanding and manipulation

functional manipulation of nature

of critical parameters and optimization

skill in the design and production

of nature

in response to identified need

of performance in functional

of goods and services, utilizing

manipulation of nature

functional manipulations

of nature

Functional

prototype

Technical risk

and design

Commercial risk

standards

Scientists

Scientists

Scientists

Scientists

and managers

and engineers

and engineers

and engineers

and managers

and marketing

and marketing,

Cost

personnel

production,

and managers

finance

personnel

and managers

University laboratory and industrial

University

laboratory

laboratory

Industrial

Industrial

laboratory

division

Time

Figure 16.10 Radical new product realization process minor role and then only on solving technical problems in the application of the new technology into the new product (or service or process).

Most of the money a business may lose on the commercial failure of a new product or new service occurs here in the commercialization stage, when the new product fails to deliver a performance or price advantage to a customer against competitive products. This is the commercial risk in innovation.

Managing a Corporate Research Laboratory

Within a business, a corporate research laboratory is necessary for the performance of research for innovating with scientific technology. The general objectives of corporate research are (1) to have a window on science for long-term threats and opportunities, (2) to maintain technical competitiveness in existing businesses, and (3) to create the technology innovation on which to begin and build new businesses. Corporate research provides a firm with a way to perform relevant science and to transform it into new technologies—a corporate institutional capability of scientific technology. For a corporation, incremental innovations protect current businesses, and radical innovation can create a new business.

328 INNOVATION

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A failure of a past CEO to invest in research and commercialize new high-tech products/services—to prepare for the corporate future—will create problems of corporate survival for a future CEO.

A useful way for management to track the attention required for product development for a business is profit-gap analysis, which shows from a financial perspective the new for new products in a business, as shown in Figure 16.11.

Some product lines, such as Product A, can be projected to continue to contribute steadily to profits; others, such as Product C, already have sales dropping and will be obsolete due to its aging technology (and the company plans to terminate the product line C). In between these extremes, some product lines, such as Product B, can be revitalized by redesign. When all the profits from these three types are summed to the total profits of the company, the chart shows the projected profits of the company. If then the management plots on this chart a dotted line of where the desired profits should go, then the area beneath the dotted line and above the summed profits line shows the gap between desired profits and actual profits.

The profit gap displays the financial magnitude-over-time of a business’s need for new products.

However, it is a challenge to successfully manage corporate research because of the two cultures of technology and business. For this reason in evaluating projects in research, a review process is often used as a dual track of evaluation in a staged Desired

profit growth

utions

its

Projected

Total prof

its from

profit gap

products C+B+A

usiness prof

to b

Product-line contrib

Profits from

Profits from

Profits from

product C

product B

product A

Time

Figure 16.11 Product line contributions to business profits HOW TO MANAGE PRODUCT DEVELOPMENT 329

TABLE 16.1 Comparing Organizational units

Perspectives

R&d Org

Business-unit Org

Time Horizon

Long Term

Short Term

Financial

Expense Center

Profit Center

Product

Information

Goods/Services

Method

Technology Push

Market Pull

planning of R&D project portfolios. Stages in evaluation of research projects and/

or joint research and business personnel evaluation of R&D projects works well for incremental innovation—because current businesses can provide a clear market focus for innovation.

But the cultural challenge remains in corporate research strategy. As we earlier saw, Xerox failed to properly commercialize the brilliant research from its PARC

laboratory and the PARC lab failed to share its technology strategy. And there are many other historical cases of innovation failures—precisely due to visionary conflicts between business units and research units (see Table 16.1).

Research and development (R&D) is a long-term investment. Even the shorter product developments from applied research to development can take two to three years. The longer developments from basic research usually take ten years. Thus, R&D

must have a long-term planning horizon and be strategic. In contrast, business units are always under the quarterly profit accounting system, focused principally on this year’s business. Business units are short-term operational in their planning horizon and are tactical. But if an operational component is not included in R&D strategy, it will not integrate properly into the business unit’s planning horizons. Conversely, if business units do not have strategic ten-year planning horizons, they will have strategic difficulty in integrating the R&D unit’s plans. This is the basic MOT challenge—properly matching strategic R&D planning with tactical business unit planning.

HOW T

O MANAGE PRODUCT DEVELOPMENT

Technological innovations have been motivated either (1) by conceiving a technological opportunity or (2) by perceiving a market need—called technology-push or market-pull innovation strategy. Market-pull innovation strategy most often creates the incremental innovations; technology-push innovation strategy most often creates the radical innovations.

In the product-development cycle (as shown in Figure 16.12), the technology implementation stage begins with research (benchmarking), which then leads to product planning (necessary and desired features) and then into product design before product production. Incremental technology improvements need to be anticipated and then acquired. Implementation of new technology requires research in how to embed the technology into a new model of a product, plan that new model, and design and produce the new product model. The product development process operates in a cycle, innovating principally through incremental innovations.

330 INNOVATION

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Technology

Technology

anticipation

acquisition

Product

development

cycle

Technology

Technology

motivation

implementation

Research

Product

planning

Product

design

Product

production

Figure 16.12 Cyclic incremental innovation process Also in a product development process, there is only a timely window of opportunity during which innovating new technology in a product can gain a competitive advantage. A speedy product development process is necessary to use technological innovation as a competitive advantage. Moreover, once in that window, the product must have been correctly designed for the customer in terms of proper application focus, adequate performance and features, and reasonable pricing, safety, and dependability. Speed and correctness are the criteria for a good product development process and are essential when innovating new technology.

But speed and correctness are not always easy to accomplish in the product development cycle; and different problems cause delays in the process, as shown in Figure 16.13. There are different kinds of uncertainties, which, taken together, are the sources of delays and errors in the overall product design and production.

There are uncertainties about timing in the anticipation of new technology—when will it be ready for embodiment in new products? There are uncertainties about the performance of new technologies—how much actual improvement will a technology achieve in a new product? There are uncertainties in customer requirements in the new product—how will the new technology alter the customer’s use of the product? There are uncertainties in the trade-off between performance and cost that the product designer can provide. There are uncertainties in production of the product from the inherent variability in the physical production processes.

The idea of “quality” is an important term in business, but it is used with four meanings:

HOW TO MANAGE PRODUCT DEVELOPMENT 331

Technology

implementation

Need for production improvement

Need for

new products

Product cost/quality problems

Research

Design

problems

Production

Product

problems

planning

Technology

risks

Product

design

Uncertainty

about

customer

Product

requirements

production

Tradeoffs

between

performance

Variation

and cost

in

production

Figure 16.13 Sources of delay in the product development process 1.

Performance quality— the quality of how well a product technically performs in its central function, a high-tech product of superior performance 2.

Design quality—the quality of a product design as focused on a particular application, a high-tech product focus on application 3.

Production quality—the quality of a production process that reproduces quantities of a product rapidly, without defects, and at low cost, a high-tech production process

4.

Service quality—the quality of a product in service as to durability and maintenance, a high-tech serviceable product

A technological innovation may affect one or more of the different kinds of quality: performance, design, production, and service. The success of a new product introduction requires not only a good product design but also good management of both the product-development process and marketing process. The product development cycle should be managed to provide innovation: (1) in product performance, (2) in productivity, (3) in responsiveness to market changes, (4) in competitive advantages.

332 INNOVATION

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Software development presents different management challenges from hardware development. In software, logic is particularly complex and production simple. In hardware, design is simpler than production. Technical difficulty in large software projects arises both from the complexity of the application and the process of managing a large team of programmers.

HOW TO MANAGE HIGH-TECH MARKETING

The key marketing idea to persuade customers to purchase lies in the perspective of seeing the value of a product/service from the eyes of the customer. This customer-focused perspective is often called a business model of the business.

A correct business model focuses on how the business makes money, depicting:

• Who are the customers for the business

• What value a product/service provides for a customer

• What price the customer will pay for the product/service

• What profit a business can make in selling the product/service To get a correct business model, a business must correctly identify and reach the proper customer and must place the correct price-to-value judgments of products/

services. There are two ideas about value— value to the customer of the product (called utility in economic jargon) and value to the business (called price). The challenge of a business model is to match up these two kinds of value—utility to price, as shown in Figure 16.14.

Ultimately, all the technology used by a business is directly (or indirectly) seen by a customer through the business’s product/service. Yet the engineering and marketing personnel of a business may not see fully the customer’s application. This is a common reason why product/service designs fail to satisfy a customer, when a high-tech business’s engineering and marketing personnel may not have fully recognized the customer’s needs—the value of a product/service in an application.

The perceptual divide between price and value in a new high-tech product is the basic reason why marketing forecasts about the potential of a basic new high-tech product often fail—such forecasts are derived based only on a market analysis of existing markets.

The innovation gamble is won by the high-tech firm that first best guesses how to match superior product performance to customer value in a new market.

The technical risks in designing a product/service arise from knowing exactly and precisely the engineering specifications to satisfy the customer needs for an application—utility. The technical risks arise from uncertainties about what the HOW TO MANAGE HIGH-TECH MARKETING 333

National innovation system

Price

Nature

University

Government

Value

research

research

Industrial

Industrial

research and

value-adding

development

structures

Research in

transforming

nature to utility

Business

Market

Production Administration

Customer

marketing

finance

Product

engineering

information

research

Task

Application

system

Figure 16.14 National innovation system for transforming nature to utility product or service can really do for a customer in applications, how well it does it, how much resources it will consume, how dependable it is, how easy it is to maintain and repair, and how safe it is.

In contrast to technical risks in trying to design products, there also are commercial risks arising from uncertainties about just who the customers are, how they use the product, what specifications are necessary for use, how they gain information and access

to the product, and what price they are willing to pay. For successful technological innovation, both engineering personnel and marketing and financial personnel need to communicate and cooperate—to match technical specs with market needs and price.

Also, for a new high-tech market to emerge, a high-tech product must be completed for an application. A technology, a product, and an application are different systems. A technology is a system of knowledge about how to manipulate nature in a logical scheme to achieve a functional transformation. A product is a system of a device or process that embodies a technology to perform the functional transformation. An application is system of tasks requiring a transformative device. It always takes more technological systems than the core technology to complete a product system. Also, it usually takes more products than the major device project to complete an application system. The first marketing problem for a new high-tech product is to determine what, how, where, and when it can be completed by additional products for a customer’s application.

334 INNOVATION

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Marketing of new high-tech products is not only an advertising challenge but also an application-system-completion challenge.

Marketing of new high-tech products is difficult, as both the radically new technology of the product and its applications must be developed together—

simultaneously and interactively.

Technology is implemented into products, production, service, and operations.

Some of the technology implemented can be generic (publicly known) and some proprietary (known only by the company). Both generic and proprietary technology are important to competitiveness, but in a different way. Generic technology is necessary but not sufficient to competitiveness, while proprietary technology is not necessary but sufficient. Any competitor’s technology must be up to the same standards as the best competitor’s generic technology. But high-tech products without proprietary technology simply invite imitators into its market. And proprietary technology and quality are important to protect the brand time over time against competition. A brand name cannot maintain a premium price when customers learn that the brand does not provide higher quality than competing products.

HOW TO MANAGE SERVICE INNOVATION

Hardware innovation depends on the scientific and engineering understanding of physical nature. Software innovation depends on the scientific and computer science understanding of logic and social nature. Biotechnology innovation depends on scientific understanding of biological nature. Services innovation depends on integrating hardware as platforms for software that improves the delivery of a service.

The logical steps in the delivery of a service centers on identification and provision of a service: (1) service referral, (2) service scheduling, (3) diagnosis and selection of service application, (4) service application, (5) payment for service.

Changes in information and other technologies for service operations and service transactions are essential to strategic improvement of service systems.

Using the Internet, a business model for services requires: (1) identifying customers, (2) customer needs, (3) products/services to satisfy their needs, and (3) channels of reaching customers.

As shown in Figure 16.15, an managed system can be modeled on three planes: (1) activities of support of the transforming activities, (2) activities of the functional transformation, and (3) activities of control of the transforming activities. This modeling technique can be used to model services, but the model must extend beyond the firm offering a service to the service system as a whole.

A model of a service system is useful to identify points in a system’s performance—where innovations might improve service competitiveness. Innovations to increase knowledge assets of a service business can be classified into seven knowledge competencies:

HOW TO MANAGE BIOTECHNOLOGY INNOVATION 335

Support

plane 1

Overhead activities

supporting production

Transformation

plane 2

Production and sales

Inputs

Outputs

activities

Control

plane 3

Overhead activities

controlling operations

Figure 16.15 Model of a managed system

1. Diagnosing the problems and needs of a client, 2. Using proper devices essential to service delivery, 3. Supplying and maintaining devices used in service delivery, 4. Timeliness and correctness of services in a delivery, 5. Development of service technologies,

6. Communicating and conducting transactions with customers and suppliers, 7. Knowledge competencies for controlling the activities of the service firm.

HOW TO MANAGE BIOTECHNOLOGY INNOVATION

Science has provided the knowledge bases for scientific technology in a pattern: 1.

Scientists pursue research that asks very basic and universal questions about what things exist and how things work. In the case of genetic engineering, the science base was guided by the questions: What is life? How does life reproduce itself?

2. To answer such questions, scientists require new instrumentation to discover and study things. In the case of genetic research, the microscope, chemical analysis techniques, cell culture techniques, x-ray diffraction techniques, and the electron microscope were some of the important instruments required to discover and observe the gene and its functions.

3. These studies are carried out by different disciplinary groups specializing in different instrumental and theoretical techniques: biologists, chemists, and physicists. (Even among biologists, specialists in gene heredity research differ from specialists in viral or bacterial research.) Accordingly, science is pursued in disciplinary specialties, each seeing only one aspect of the 336 INNOVATION

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existing thing. Nature is always broader than any one discipline or disciplinary specialty.

4. Major advances in science occur when sufficient parts of the puzzling object have been discovered and observed and someone imagines how to put it all together in a model (as Watson and Crick modeled the DNA molecule). A scientific model is conceptually powerful because it shows both the structure and the dynamics of a process implied by the structure.

5. Scientific progress takes much time, patience, continuity, and expense.

Instruments need to be invented and developed. Phenomena need to be discovered and studied. Phenomenal processes are complex, subtle, multileveled, and microscopic in mechanistic detail. In the case of gene research, the instruments of the microscope and electron diffraction were critical, along with other instruments and techniques. Phenomena such as the cell structure and processes required discovery. The replication process was complex and subtle, requiring determination of a helix structure and deciphering of nature’s coding.

6. From an economic perspective, science can be viewed as a form of societal investment in possibilities for future technologies. Since time for scientific discovery is lengthy and science is complicated, science must be sponsored by government as a social overhead cost for a technically advanced society.

Without the support for these overhead costs of basic knowledge creation, technological innovation eventually stagnates for lack of new phenomenal knowledge for its inventive ideas.

7. Once science has created a new phenomenal knowledge base, inventions for a new technology may be made by either scientists or by technologists (for example, scientists invented the recombinant DNA techniques). These radical technological inventions start a new technology S-curve. Then is the time to invest in a technological invention and begin a new industry.

8. When the new technology is pervasive across several industries (as genetic engineering is across medicine, agriculture, forestry, marine biology, materials, etc.), the technological revolution may fuel a new economic expansion.

The long waves of economy history are grounded in scientific advances that create basic new industrial technologies.

9. There are general implications for corporate management. Corporations should be supportive of university research that focuses on fundamental questions underlying core technologies of the corporation. Corporations need to perform some active basic research in the science bases of their core-technologies to maintain a window on science for technological forecasting.

Why had the early hoped-for big profits in biotechnology not occurred, even though biotechnology itself survived and continued to develop? The answer to this is in the complexity of biological systems. The more complex is the nature underlying a technology, the more scientific research is required to further understand the nature underlying a technology (which is engineered into a product).

SUMMARY—OVERVIEW OF INNOVATION THEORY 337

The innovation process for biotechnology industry in the United Sates includes (1) developing a product, (2) developing a production process, (3) testing the product for therapeutic purposes, (4) proving to the FDA that the product is useful and safe, and (5) marketing the product. In the case of the recombinant DNA techniques, developing the techniques themselves was only a small part of the technology needed by the biotechnology and the smallest part of its innovation expenditures.

The testing part of the innovation process to gain FDA approval took the longest time (typically, seven years) and represented the greatest cost. Because of this long and expensive FDA approval process in the United States, extensive partnering occurred between U.S. biotech firms and the larger, established pharmaceutical firms. Also, the U.S. government role in supporting science was essential to the U.S. biotechnology industry.

Progress in biotechnology is a prime example of a scientific technology requiring interactive progress in both science and technology. Science provides the understanding of how and why biological systems operate. Technology provides the techniques for manipulating biological systems. Medicine is the application of scientific knowledge and technological instrumentation to diagnose and cure diseases and injuries.

Development of new scientific technologies to improve an existing technology requires multidisciplinary research in university centers that span both science and engineering disciplines. University research centers should be guided by a research vision for simultaneously progress in both science and technology—next-generation of a scientific technology.

The proper leadership of a center director is critical to the performance of a university research center. The two basic characteristics necessary for a university research center director are: (1) a scientifically exciting multidisciplinary research vision and (2) the ability to successfully win a research grant for a center. The critical elements of a center director’s research vision is the ability to plan and assemble discrete pieces of research as center projects that taken together together will yield coherent integrated scientific and technological progress. A strategy for a multidisciplinary university research plan should be expressed as sets of projects grouped on three planes of research goals: (1) a science base, (2) a set of new technology subsystems, and (3) experimental “testbed” for testing a prototype of a new technology system.

SUMMARY—OVERVIEW OF INNOVATION THEORY

The practice of innovation is complex because innovation theory itself is complicated. There are four aspects to innovation:

1.

Process of innovation—Innovation must be implemented by a process that connects research in science to invention in technology and to the design and production of high-tech products and services for the economy.

2.

Organization of innovation—Innovation within a nation must be practiced at two different scales: (1) the macro-level of research in a nation and 338 INNOVATION

PRACTICE

(2) micro-level of commercialization in a business—both levels effectively coupled in knowledge creation and use—as a system for innovation in a nation.

3.

Generic types of technologies— A manager of innovation must think generically about all kinds of technologies—while leaving particular technical details to technical experts—and also while leading experts in an innovation team.

4.

Economic development— A manager of innovation needs to understand (and act strategically on) technological innovation for competitiveness by designing new high-tech products/services or improving performance and lowering costs in products/services.

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INDEX

A

Applications-systems-integrator

industries, competitive

Adcock, Willis, 216–217

factors, 191

Administration function, 67

ARPAnet, 8–9

Air Defense System Engineering

Atanasoff, John, 55, 61

Committee, creation, 64

Atom, plum pudding model, 203

Air transportation system, model, 275

Audio tube, 205

Akins, Robert, 165–166

Allen, Paul, 126

Altair computer, 123, 126–128

B

Alternative technology confi gurations,

systems analysis, 238–240

Backus, John, 248–249

Altos PC System (Xerox), case study,

Bardeen, John, 210–211

21–24

Beehive coking, 182

Amazon.com (case study),

Bessemer, Henry, 179, 184

116–118

Best practice, balance, 141

AOL/Time Warner

Bezos, Jeffrey, 116–118

information businesses, 160

Biological system, physical

merger (case study), 157–160

phenomenon, 297

strategies, 159

Biological technologies, 218

Apple computer, 126–127

Bioreactors, 295

Apple iPod

design, 294

iTunes (case study), 266

Biotechnology

music business (case study), 263–264

Industry, 286–288, 366

Applications

innovation process, 337

defi nition, 128

origin (case study),

high-tech products, completion,

innovation, management process,

127–129

335–337

systems, technology (relationship),

profi ts, 288–290

192–194

progress, 293

technology performance, 231

systems, 278

349

350

INDEX

Bolton, Elmer K., 85–89, 93

experiments (Thomson), 203–204

Boolean logic circuits, 58

invention, 202

Bottom-level innovation strategy, 164,

tube, 202–203

172–174

Cell structure (case study), 279

Boyer, Herbert, 279–288

Central processing unit (CPU),

Brand name, impact, 133, 334

usage, 121

Brattain, Walter, 210–211

Cetus, 286–288

Braun, Karl Ferdinand, 202–203

bankruptcy, 289

Build business case stage, 109

Charged particles, 204

Bureaucratic manager, style,

China, economic reforms, 6

167–168

Chiron, 288–289

Business

Chromatin, 280

customers, perceptual divide, 120

Circular change, 135

functions, 67

Circular knowledge interactions, 72–75

interactions circularity, 73–74

Clark, Douglas, 257

level, 156

Close-follower technology, 129

micro-level innovation, 156–157

Coal, mining technology, 181–182

mission statement, 195

Cohen, Stanley, 279–288

model, 332

Coking technologies, 182

defi nition, 119

Cold War, evolution, 309

price/value, 118–121

Commercial failure, monetary loss, 327

operations, modeling form, 274f

Commercialization, 13

product development cycle, 144f

business process, 25

product lines, projected

category establishment, innovation

contributions, 107f

logic (usage), 94

profi ts, product line contributions, 328f

innovation stage, 92

strategy, technology (integration), 177

radical innovation, logic, 91–93

success, top-down/bottom-up

stages, 108–109

perspectives (importance),

Commercial risks, 125, 326

163–164

Commercial success, 142

system, direct experience, 120

Commodity industry, 48–50

technologically based failures, 235

Commodity-like products/services,

Business integration team

fashion differentiation, 135

(BIT), 108

Commodore 64 personal computer

Business-level innovation strategy,

(case study), 138–142, 144–145,

156–157, 322

148–151

Communications, public standards

C

(examination), 45–46

Competition, 195

Capital, necessity, 168

Competitive benchmarking, 140

Carothers, Wallace Hume, 81–84,

Competitive factors, 187

85–89, 93

Competitors

Cash fl ow, decrease, 169

future products, beating, 141

Castro, Fidel, 309

future technical capability,

Cathode ray, 201

extrapolation, 140–141

INDEX

351

Computer-assisted networks, 252–254

Cyclic innovation process, 214

Computers

Cyclic process designs, implementation

invention (case study), 52–54, 55–57,

phase, 214

59–60, 63–66

Cymer, Inc. (case study), 165–167

product lines, 72

schematic logics, hierarchical

levels, 59

D

science, origination, 57

De Forest, Lee, 205–206

system progress, 150f

Deltamax, 64–65

technology, 58–59

Demographics, impact, 135

Computer-to-computer

Deoxyribonucleic acid (DNA)

communications, 11

bases, sequences, 290–293

Concurrency, promotion, 147

discovery/chemical analysis (case

Constraints, examination, 141

study), 279–280

Control specialized language, 251, 254

function (case study), 281

Control systems, 313

sequencing, 291–292

Coolidge, William, 100–103, 106

structure (case study), 281–283

Cooper, R.G., 108–109

Design

Core technologies, 41

bug-centered approach, 258

Corporate growth, occurrence, 164

bugs, 256–258

Corporate level, 156

production, 257–258

Corporate-level innovation strategy,

criteria, design team formulation, 141

156, 322

function/form challenge, 246

Corporate prosperity, maintenance, 161

process, details/decisions, 142

Corporate R&D

quality, 152, 331

budgeting, 113

Development project,

evaluation, 112–114

classifi cation, 173

innovation, relationship, 97

Development stage, 109

Corporate research

Discontinuous innovation, 215f

laboratory, management, 327–329

Discontinuous progress, 235–236

objectives, 106–107

Discovery

Corporation

occurrence, 81

innovation strategy, levels, 163f, 323f

stages, 108–109

organizational activity, levels, 156

Divisional-level innovation strategy,

power, problems, 164

156–157, 322

strategic thinking, levels, 156f

Division level, 156

Corpuscles, 204

DuPont, nylon (case study), 78–79,

Creative destruction, 34, 72

81–84, 85–91, 93

Crick, Francis, 282–285

Cuban missile crisis (case study), 307–313

Cultural evolution, 104

E

Customer application system, 192–193

Cyber attacks (case study), 306

Early programming languages (case

Cyclic incremental innovation process,

study), 248–249

143f, 330f

Eckert, Presper, 55–60

352

INDEX

E-commerce, services (relationship),

Ethics

269–271

regulation, 305

Economic activities, expansions, 36

technology, relationship, 299, 302

Economic cycles, Kondratieff

Evaluation, dual track, 109–110

long-waves, 317–318

Excessive fi nancial enthusiasm, 29

Economic development, 338

Excess production capacity, 36

Economic expansion, 37

Expansion, periods, 35–36

Economic innovation, 33

Expense centers, 110–111

Economic input-output model

(Leontief), 37–38

Economic long waves, 27, 31–34

F

Economic value, 193

Feedback loops, 146

Economy

Finance function, 67

system model, 38

First movers, 46

term, usage, 14

Fisher, Emil, 280

Edelheit, Lewis, 105

Fleming, John Ambrose, 204–205

Edison, Thomas, 98–99

Flight transformations

Electron fl ow, physics path, 200f

control activities, 276

Electronic Discrete Variable Automatic

support activities, 275

Computer (EDVAC) project, 66

Float-glass process, innovation,

Electronic Numerical Integrator and

169–171

Computer (ENIAC)

Forrester, Jay, 63–66, 273–274

case study, 55–57, 59–60

Full-scale production system, 91, 325

knowledge, 64

Functional-application prototype, 325

Electronics, 200

Functional capability, 193

Electrons, naming, 204

Functional prototype, 214

Electron vacuum tube

project step, 170

construction, 206

Functional transformation, 226

invention (case study), 200–206

Furnace technology, 183–185

Engineering

Fusfeld, Herbert, 104–105

category establishment, innovation

logic (usage), 94

function, 67

G

projects, 173

prototype/testing, project step,

Gates, Bill, 126

170, 325

Geissler, Heinrich (Geissler tube),

research, 89–90

201–202

term, usage, 14

Genecor R&D (case study), 107–108

Engineering-product design, 89, 325

Genentech, 286–288

Engineering product-prototype,

General Electric (GE)

89–90

corporate research center, 98–103

England, economic cycles, 36

Research and Development

Entrepreneur style, 167–168

Center, 103

Equipment sector, technologies, 189

General Motors, bankruptcy, 47–48

Ethical imperative, 307

Genetic coding (case study), 283–284

INDEX

353

Geographic locale, fl ight

Human Genome Project (case study),

transformations (support

290–293

activities), 275

Human purpose, defi ning, 225

Global industrialization, initiation, 27

Global trade, increase, 7

I

Goedel, Kurt, 60

Goedel notation, usage, 60–61

IBM knowledge strategy, 71

Goods/services, performance/

IBM mainframe computer market, 129

effi ciency, 124

IBM System 360 Computers (case

Google, Inc.

study), 68–71

business model, 119

Illumination (case study), 227–228

case study, 15–16

Incremental innovation, 30

phone (case study), 262

impact, 317–318

Griffi th, Frederick, 281

improvements, motivation, 143

Groupware, 173

management, 137

planning, 108–109

Incremental progress, 235–236

H

Incremental-research strategy,

procedure (steps), 109

Haggerty, Patrick, 213

Incremental technological progress, 236

Hardware business, decision, 266–267

Industrialization

Hardware-product design

early economic expansion, 34–35

systems analysis, 246–247

timeline, 4–12, 5f

technical innovation, system

Industrial life cycle, 42, 42f, 44

questions, 247

Industrial research and development,

Hardware product system, 245–246

186

intelligence, design, 245

Industrial researchers, impact, 104

Heredity, principles (case study), 280

Industrial Research Institute, 104

Hewlett-Packard, federated model, 105

Industrial standards, 44–46

High-tech business ventures, 167–169

Industrial structures, industrial value

requirement, 168

chain description, 181

High-tech industries, 49

Industrial value chain, 179–181, 322f

High-tech marketing, management

technical progress, occurrence, 185

process, 332–334

Information

High-tech processes, term (usage), 14

function, 67

High-tech product

strategy, 270

IBM introduction, proprietary

technology, 218

technology (absence), 132

sector, technologies, 189–190

innovation, 115

Initial production/sales, project

marketing, 129, 131, 332–334

step, 171

High-tech products/services

Innovation

basis, 168

bottom-level strategy, 323

design/development, 63

bottom-up strategy, 161–164

term, usage, 14

commercial success, 142

High-tech projects, 173, 174

corporate R&D, relationship, 97

354

INDEX

Innovation ( Continued )

Internet

costs, 94–95

architect, 8f

defi nitions, 13–14

businesses, 270–272

economic scale, 29–31

Federal Network Council defi nition,

economy, relationship, 27

11

importance, list, 54t–55t

innovation, case study, 7–12

levels, 318

radical innovation, 30

logic, 141–142

technological innovation,

management, 321–323

commercialization, 8

markets, relationship, 115

technology, components, 11

micro-level, 62

usage, 334

middle-level strategy, 323

Intrinsically evil technologies, control

national innovation system, 60–63

systems, 313

national systems, relationship, 51

Invention

organization, 337

importance, 84–85

practice, 317

project step, 170, 325

process, 12–14, 337

Invested capital, recovery (exploitation),

management, 317–323

168

product development,

Iron Curtain, 308

relationship, 137

Iron refi ning technology, 183–185

research, 77

research logic, 77

stages, 79–92

J

science/technology, importance, 61

Jobs, Steve, 126–127, 134, 266–269

strategic vision, 267

success, occurrence, 157

theory, 4, 337

K

top-down strategy, 161–164

top-level strategy, 323

Kelly, Mervin, 209–211

types, 33

Kennedy, John F., 310–312

Innovation strategy

Khrushchev, Nikita, 309–312

business level, 156–157, 322

Kilby, Jack St. Clair, 216–217

concept, 161

Knowledge

corporate level, 156–157, 322

application, 3

distributed management process, 156

assets, 186, 189–190

divisional level, 156–157, 322

base, 13, 336

levels, 163f

circular interactions, 73f, 320f

operations level, 156–157, 322

strategy, 71

relationship, 153

transformation, 62f, 66f, 167f, 319f

Innovative product design, 141

Knowledge-utility transformations, 324f

Institutional R&D sectors,

Kondratiev, Nikolai, 31–33

macro-level, 61

Kondratiev economic long waves,

Interface Messaging Processor (IMP), 10

occurrence, 41

International economic

Kondratiev long-economic wave, 39

competitiveness, 6–7

Kondratiev long wave, 36

INDEX

355

historical inevitability, absence, 37

knowledge interactions, circularity,

stages, 36–37

73–74

Kondratiev wave, 32–33

niches, 134–135

opportunity, exploitation, 168

positioning, 130–132

L

steps, 131

product, designing, 124–125

Language, structuring reasoning (formal

share, 195

role), 255

volume, chart (proposal), 42

Lasseter, John, 268–269

Marketing

Launch stage, 109

business function, 118

Leontief, Wassily, 38

fi nance, relationship, 110

economic input-output model, 37–38

function, 67

Light-emitting diodes (LEDs), 228

Market-pull innovation, 139–140

Lighting technologies, technology

Markkula, A.C., 126–127

S-curves, 227f

Massachusetts Institute of Technology

Lilienthal, Otto, 221–222

(MIT), Biotechnology Process

Linde-Frankl process, 185

Engineering Center (BPEC)

Linear innovation process

(case study), 293–297

functional prototype, 214

Materials refi ning/synthesis/

knowledge-utility transformation, 324f

shaping sector, technologies,

volume production, 215

186–187

Lisa project (Apple), 134

Material technologies, 218

Long economic cycle, 32–33

Mauchly, John, 55–61

Lower-level innovation strategy, 171–172

McPherson, John, 65–66

Low-tech projects, 173–174

Medium-tech projects, 173, 174

Lucas, George, 268–269

Mensis mirabilis, 85

Middle-level innovation strategy, 164,

M

167–169

Miescher, Friedrich, 279–280

Mahoney, Richard, 301–302

Molecular biology, origin, 281

Mainframe computers, IBM

(case study), 279

commercialization, 30

Monsanto, genetically modifi ed seeds

Major-device industries/service

(case study), 300–305

industries, 180

Moore, Samuel, 291

Major-device sector, technologies, 188

Moore’s law (case study),

Managed systems, model, 274,

233–235

275f, 335f

Morgan, Thomas, 280

Management, term (usage), 14

Morphological analysis, 238

Manipulation, feasibility, 79, 81, 325

Morris, Charles, 249–250

Manufacturing-product design, 91, 325

Motorola

Marconi, Guglielo, 202

Technology Roadmap (case study),

Market

194–195

demand, 111

technology-roadmap documents,

emergence, 128

sections, 194–195

356

INDEX

MP3 Player (case study), 264

innovation, software-design

Mutually assured destruction

decision, 255

(MAD), 308

source, 168

Next-generation product lines,

N

radical innovation (relationship),

71–72

Nanotechnology, impact, 218

Next-generation technology, 212–213

Napster (case study), 265

innovation, 30

National competitiveness, 7

product-line platform, 197

National innovation system, 60–63

products, innovation process,

circular interactions, 72–75

214–215

management, 319–321

Nobel, Alfred, 181–182

performance, 75–76

Noyce, Robert, 217

product development cycle, 144f

NSFnet, cessation, 11

research, 80f

Nuclear strike, 309

National Science Foundation (NSF),

Nylon, technological innovation (case

supercomputer center program

study), 78–79, 81–84, 85–91, 93

establishment, 10

Natural phenomenon, 225

Natural resources, acquisition/

O

transportation, 183

Offi ce level, 156

Natural states, manipulation, 225

Ontology (case study), 252

Nature

Open architecture, 10

complexity/research, 290

Operational processes, 157

ghost, 207

Operations-level innovation strategy,

knowledge, transformation, 74f, 321f

156–157, 322

logic, addition, 207

Opportunity, product technology

physics, 206

criteria, 130

potentials, ghosts, 208

Ore benefi ciation technologies, 182

technology/potentials, 206–208

Ore transportation technology,

term, usage, 12, 13

182–183

transformation, national innovation

Organic light-emitting diodes

system (usage), 120f, 193f, 331f

(OLEDs), 228

Nernst, Walther, 98–99

Organization

Networked European Software and

future directions, 161

Services Initiative (NESSI)

operational processes, 157

Group, 261

Organization for Economic

New-application technologies, 186

Co-operation and Development

New industries, dynamics, 45f

(OECD), innovation review, 75

New Product Development Map, 196

New product introduction,

successes, 148

P

New technology

anticipation, timing (uncertainty), 330

Pacing technologies, 41

industries, 34–35

Packet switching, 9, 11

INDEX

357

Palo Alto Research Center (PARC)

development

(Xerox), 21–23, 106, 134

management process, 329–332

failure, 329

process, delay (sources), 330f

Parallel processing computers, 268

families, strategy, 197

Parts/components/subsystems sector,

fl exibility, emphasis, 131

technologies, 187–188

functionality, 124

Performance, 193

information/access, gain, 125

quality, 152, 331

innovation peaks, rate, 44

Personal computer industry (case

lifecycle, 195

study), 121–123, 125–127, 129,

lines, projected contributions, 107f

132, 134

new-generation, 72

Phage group, 281

plan, 195

Physical phenomenon, mechanical

planning, 151

structures/processes, 238

platforms, 197

Physical process, 208

production cost, lowering, 92

Physical technology

proprietary technology, absence, 133

incremental technological

quality, 152

progress, 236

strategy, integration, 151

performance parameters, 232

systems, 241

S-curve, 228–230

technology, market evaluation, 130

exponential portion, 229

volume production, 91, 326

format, 229f

Product-design standards, 44

Pilkington Float Glass R&D Project

Product development

(case study), 169–171

cycle

Pilot-plant production, project step,

importance, 149

170–171

management, 148

Pirsig, John, 207

incremental innovation, 142–143

Pixar (case study), 267–269

innovation, relationship, 137

Plucker, Julius, 201

projects, factors, 147–148

Porter, Michael, 179, 272–273

strategy, commercial success, 142

Post-launch review stage, 109

study, 147

Power technologies, 218

Product development process, 137

Pragmatic specialized language,

criteria, 145

251, 254

delays, 145–148

Price, product technology criteria,

operation, 144

130, 332

Product-family planning, 196

Process development research, 92

Production

Process functionality, 124

experience curve, 195

Product

function, 67

commercial success, design/

improvement, 91–93, 326

development time

processes, development, 90, 91, 325

(importance), 147

commercialization, 92–93

design, 141

prototype, project step, 170–171

innovation logic, 141–142

quality, 152, 331

performance/cost tradeoffs, 147

variation, feedback loops, 148

358

INDEX

Product-line planning, 196–197

logic, 87, 89–90, 91–93

Product-line platforms, 197–198

macro/micro levels, 66–67

Product-model lifetime, 72

next-generation product lines,

Products/services

relationship, 71–72

designing, technical risks, 124, 332

occurrence, 31

features/performance, 135

process, 62

innovation

knowledge transformation, 66f

business functions, 67

Radical new product realization

micro-level, 318

process, 95f, 327f

value, 119

Recombinant DNA technique (case

Profi tability, product technology

study), 284–285

criteria, 130

Reproduction, DNA function (case

Profi t-gap analysis, 107, 328

study), 281

Profi t margin, determination, 49

Research

Program Evaluation and Review

capability, 63

Technique (PERT), 173

categories, focus, 94

chart, probabilities, 174

focus, 94

Programming languages, 253

function, 67

(case study), 248–249

issues, types, 94f, 326f

linguistic syntax, specialization, 249

laboratory/business division, 109–112

Promethean inventions, 208

management, confl ict, 111–112

Proprietary technology, 132–133

stages, logic, 325–326

Protein product, recovery

Research and development (R&D)

processes, 294

center/business units, products

Protein production, technological

characteristics, 111

performance variables, 294

evaluation, 113

Protein recovery process, 295–296

expenditures to gross domestic

Protein separation, research

product ratio, 74–75

projects, 296

focus, 187–191

funding, direct impact

Q

(measurement), 38f

institutional sponsors, 74, 320

Quality

long-term investment, 110

idea, importance, 330

management, 323–329

meanings, usage, 152

product pipeline, 109–110

product technology criteria, 130

project, 173

term, usage, 92

support, 112

Quality market niche, 135

share, division, 113

Quarterly profi t accounting system, 110

support, necessity, 106

Research and engineering, 110

R

Research-project portfolio management,

108–109

Radical innovation, 30

Research sector system, aspects, 75–76

commercialization, 72

Resource-extraction sector,

information network, 253f

technologies, 186

INDEX

359

Resources, quality/location, 183

military/economic superiorities, 5

Retail service industry, competitive

origin, 5

factors, 190

product/service basis, 3–4

Retail/service sectors, technologies,

Scientifi c technology, term (usage), 14

190–191

Scientifi c understanding, 79, 81, 325

Return, calculation, 112–113

Scoping stage, 109

Return on investment (ROI),

Sculley, John, 267–269

evaluation, 112

S-curve, 227–228

Rice, Edwin, 99–100

format, 195

Roberts, W. Edward, 122–123, 126–128

incremental progress, 235–236

Rosenthal, Elizabeth, 303–304

step-function, 232–233

Rutherford, Ernest, 203

Semantic specialized language,

Ryanair

250–251, 254

(case study), 271–272

Semiconductor chip transistor density,

differentiation, 276

technology progress, 234f

Semi-conductor integrated circuit

chip invention (case study),

215–217

S

Service

Safety, requirements, 124

application, diagnosis/selection, 263,

SAGE computer project, 61, 66

334

case study, 63–66

business

Sales history/forecast, 195

decision, 266–267

Samsung Electronics (case study),

knowledge assets, increase

191–192

(innovation classifi cation),

Sandstrom, Richard, 165–166

334–335

Schumpeter, Joseph, 33–34, 72

delivery, 261

Science

factors, 276

base, 35–36

functionality, 124

category establishment, innovation

innovation, management process,

logic (usage), 94

334–335

circularity, 73

payment, 263, 334

European civilization initiation, 5–6

quality, 152, 331

internationalization, 6

referral/scheduling, 263, 334

knowledge base, case study, 52–54

systems, 260

macro-level, 318

model, 272–276, 334–335

specialized languages, 250f

transactions, logic, 263

term, usage, 13

Services

timeline, 4–12, 5f

e-commerce, relationship, 270–271

university research centers, 297

performance/innovation, 276–277

Scientifi c feasibility, 81

science, 260–261

Scientifi c technology

Service Science, Management, and

facilitation, steps, 60–61

Engineering (SSME), 261

institutionalization, 103–106

Shapiro, Robert B., 300–302

knowledge bases, 285–286

Shockley, William, 209–211

360

INDEX

Sikorsky

T

helicopter, 241–245

innovation, 244–245

Teal, Gordon, 213–214

prototype, 244

Technical change

Silicon transistor invention (case study),

anticipation, 150

213–214

planning/creation, 150

Software

Technically feasible prototype, 84–85,

development, management

214, 325

challenges, 148

Technical personnel, business personnel

innovation, hardware innovation

(relationship), 25–26

(comparison), 258–259

Technical process, 25

project, completion (failure), 256

Technical programs, lower-level

technologies, 232–233

innovation strategy, 171–172

Software design

Technical progress, occurrence, 237

decision, 255

Technical projects

logic/language, 255–256

bottom-level innovation strategy,

reasoning, structure, 256

172–174

Stanley, W.M., 281

types, 173

Steel industry innovation (case study),

Technical risks, 124, 332

178–179, 181–185

Technical savvy, fi nancial savvy

Steinmetz, Charles, 99–100, 106

(relationship), 20–21

Stine, Charles, 78–79, 81–84

Technical self-suffi ciency,

Strategic business vision, 266–267

practicality, 105

Strategic technologies, 41

Technical variables, solving, 125

Strategic thinking

Technical vision, impact, 267

checklist, 164

Technological innovation

logics, perspectives, 161–162

economic impact, measurement,

types, 161–162

37–38

Strategy, perspectives, 162f

focus, 4

Super high-tech projects, 173, 174

globalization, 6–7

impact, 3

Swanson, Robert A., 286–288

Switzerland, innovation strengths, 75

motivation, 140

Syntactic specialized language, 250, 254

new industries, dynamics, 45f

System 360 Computers (IBM) (case

process, 17

study), 68–71

scales, 30

Systems

stimulus, 182

analysis, 238–240, 246–247

Technological invention, impact, 208

concept, 246

Technological process, impact, 33

defi nition, 224, 225

Technological progress, 236–238

dynamics, 273

Technology

elements, 238–239

advance, occurrence, 236–237

projects, 173

application systems, relationship,

technical progress, 237

192–194, 226

technological innovation,

business strategy, integration, 177

identifi cation, 246–247

categories, 317

INDEX

361

category establishment, innovation

types, 217–218, 318f, 338

logic (usage), 94

university research centers, 297

commercialization, 17

viewing, 236

defi nition, 225

wealth, relationship, 17, 17f

development, 130

Technology-focused-and-targeted basic

discontinuity, 72, 211

research, 296

planning, 235–236

Technology-push innovation, 139–140

effi ciency, 231

Technology-push research

ethics, relationship, 299

opportunities, 239

functionality creation, 181

Technology Roadmap (Motorola) (case

impact, 133, 230

study), 194–195

imperative, 4, 307

Technology-system application

implementation, 132–133

control, 226

design targets, 247

Technology-system control, 226

importance, reason, 141

Technology-system process, 226

invention, 85, 199

Technology systems, 220, 224–227

facilitation, steps, 60–61

confi guration, 226f, 246

inventors/designers, 25

functional context analysis, 239

knowledge interaction, circularity, 73

innovation, absence, 237

limitations, 213

parts, improvement, 184

macro-level, 318

Testing/validation stage, 109

obsolescence, 185

Therapeutic protein production, 296

performance parameters, 230–232

Thomson, J.J., 203

power, attention, 208

cathode ray experiment, 203–204

progress

3M Corporation

dependence, 289

diversifi cation (case study), 154–155

format, 232f, 259f, 318f

divisions, creation, 155

push, 111

Top-level innovation strategy, 164–165

quality/safety, 231

Tramiel, Jack, 138–139, 144–145, 151

research, 87

Transformational logic, 207, 208

project step, 170

Transistor invention (case study),

resources, 231

208–212

roadmap, 195, 196–197

Transmission Control Protocol/Internet

science bases, 54, 285–286

Protocol (TCP/IP), 10

scientifi c technology, contrast, 3

Tungsten, technical promise, 101–102

S-curve, 228

exponential growth, mathematical

expression, 229

U

linear portion, 229–230

strategy, 131, 186–191

Universal Automatic Computer

commercial success, 142

(UNIVAC), 59–60

integration, example, 144–145, 151

U.S. auto fi rms, numbers (case study),

term, usage, 13–14

46–48

timeline, 4–12, 5f

U.S. auto industry, case study, 39–41

transformation, confi guration, 226

U.S. auto sales, 43f

362

INDEX

U.S. dot.com stock bubble, case study,

Welch, Jack, 161

28–29, 28f

Whitney, Willis Rodney,

Utility, term (usage), 119, 332

100–103

World industrialization, pattern, 6

V

Wozniak, Steve, 126

Wright Brothers, Flyer

Vacuum tube, electron fl ow (physics

(case study), 221–224

path), 200f

glider control, 222–223

Value, product technology criteria,

improvements, 224

130, 332

Wright Brothers, powered

Value/price, economic concepts

fl ights, 242

(contrast), 119–120

Venn diagrams, usage, 61–62

Venture capital funds, capital input, 29

X

Vertical integration/deintegration, 188

Von Braun, Werner, 309

Xerox

Von Neumann, John, 52–53, 61

Altos PC system, case study,

Von Neumann computer architecture,

21–24

58–59

commercialization failure, 110

innovation (case study),

W

18–20

Offi ce Products Division, 111

Watson, James, 281–285

XML (case study), 252

Wealth, creation, 17

Table of Contents

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