View and Purpose

ANATOMY OF DISRUPTIVE TECHNOLOGIES: ANALYSES AND COMPARISON

A dissertation submitted to the Kent State University Graduate School of Business in partial fulfillment of the requirements for the degree of Doctor of Philosophy

Eileen D. Weisenbach Keller

December, 2005

Dissertation written by

Eileen D. Weisenbach Keller

B.S., Indiana University, 1983

M.B.A., The University of Chicago, 1992

Ph.D., Kent State University, 2005

Approved by

William Shanklin, Ph.D. Co-Chair Doctoral Dissertation Committee

Marvin Troutt, Ph.D. Co-Chair Doctoral Dissertation Committee

O. Felix Offodile, Ph.D.

Robert Krampf, Ph.D.

Accepted by

Donald Williams, Ph.D. Doctoral Director, Graduate School of Management

Donald Williams, Ph.D. Dean, Graduate School of Management

ACKNOWLEDGEMENTS

The willingness of so many people to support and assist me through this long journey is a blessing I will cherish forever. Your inputs were varied and many and this research would never have developed as it did without the input from others.

Appreciation goes to my co-workers, doctoral student colleagues and friends. Through fruitless explorations, taxing analyses and rewrites, you all believed in me, offered your expertise and opinions and bolstered my determination.

I want to thank my dissertation committee: Dr. Felix Offodile, Dr. Marvin Troutt and Dr. William Shanklin. Traveling through this process with you has been enlightening.

Felix Offodile has been there for all of the questions regarding the details of the undertaking. Regardless of the hour of the day, no matter how high the stacks of administrative memos on his desk, Dr. Offodile thoughtfully answered all of my questions, sharing with me his considerable knowledge and expertise. For this I am grateful.

Marvin Troutt, my co-chairman, was the person who assured me throughout the process that the research I was conducting had value, and that the creativity I brought to the project was worthwhile and perhaps even indispensable. At times when I wasn’t sure anyone knew I was conducting research, Dr. Troutt would send an email with a new source of information or stop by with a book to assist me in pushing to a new level of knowledge and analysis. I will always remember the example he set for how to be a good teacher, mentor, researcher and professional.

William Shanklin, my co-chairman, through his consummate professionalism, has contributed to my development as an academic in more ways than I can list. He was the source of the idea for this research, igniting my interest during the doctoral coursework. He was always available for every question and provided guidance through every challenge. His persistence has influenced and taught me to do high quality work that has become a new standard from which I will build and grow throughout my career. His contribution to my academic life is second to none and I will never forget what he has taught me.

Finally, I express my gratitude to my family. To my parents and siblings for faith, faith in God and faith in me. To my children, Kevin, Marilyn and Andrew, you are light and love; you bring me so much joy. Thank you for being wonderful and for reminding me daily what is most important in life.

Above all, my deepest gratitude goes to Mark Keller, my husband and the love of my life. You have been my unending source of hope, mirth, strength, and love throughout this long journey. You have been father and mother to our children at times when I was working.

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You have worked hard and provided for us when there seemed to be no time to work. You have been my coach, my confidant, my cheerleader, and my spiritual rock. Thank you, I dedicate this dissertation to you with gratitude, love and respect.

TABLE OF CONTENTS

TABLE OF CONTENTS...... V LIST OF FIGURES...... VII LIST OF TABLES...... VIII CHAPTER 1 OVERVIEW AND PURPOSE ...... 1 RESEARCH QUESTIONS AND SIGNIFICANCE THEREOF ...... 2 Eye of the Beholder ...... 5 ORGANIZATION OF THE DISSERTATION...... 7 CHAPTER 2 LITERATURE REVIEW, CONCEPTUAL FRAMEWORK, AND PROPOSITIONS...... 9 Technology Cycle ...... 11 THE CONTEXTUAL LITERATURE: FIRM LEVEL ANALYSIS ...... 16 Firm Level Analysis: Resource Dependence and Resource Allocation...... 16 Firm Level Analysis: Dynamic Capabilities and Core Rigidities...... 21 DISRUPTIVE TECHNOLOGY AND DISRUPTION RANGE...... 23 The elements and necessary conditions of the model...... 24 Interactions among the elements ...... 30 Disruption Range ...... 33 CHAPTER 3 RESEARCH METHODOLOGY...... 36 CHOICE OF CASE STUDY METHOD ...... 36 DEFINITIONS ...... 37 THE RESEARCH DESIGN ...... 41 Case Inferences to Theory Development ...... 42 Application and Validation of Case Inferences...... 44 SELECTION OF INDUSTRIES...... 44 UNDERSTANDING THE DISRUPTION RANGE ...... 47 Sources of Evidence and Data Collection Methods...... 47 UNDERSTANDING STRATEGIC RESPONSE AND FIRM PERFORMANCE ...... 53 Sources of Data and Data Collection Method ...... 53 RELIABILITY AND VALIDITY OF THE FINDINGS ...... 57 CHAPTER 4 ANALYSIS AND FINDINGS...... 61 TELECOM INDUSTRY ANALYSIS (PROPOSITIONS 1-6)...... 61 Density (Propositions 1 and 2) ...... 61 Intensity (Proposition 3)...... 63 Revenues (Propositions 4 and 5) ...... 64

Summary (Proposition 6 and Definition) ...... 68 MOTION PICTURES VS. TV INDUSTRY ANALYSIS (PROPOSITIONS 1-6)...... 69 Count and Intensity (Propositions 1, 2 and 3)...... 70 Revenues (Propositions 4 and 5) ...... 73 Summary (Proposition 6 and Definition) ...... 76 MOVIEGOING VS. VIDEO RENTAL BUSINESS (PROPOSITIONS 1-6) ...... 78 Count (Proposition 1 and 2) ...... 78 Intensity (Proposition 3)...... 80 Revenues (Propositions 4 and 5) ...... 81 Summary (Proposition 6 and Definition) ...... 85 DISRUPTION RANGE DESCRIPTION - ACROSS INDUSTRY ANALYSIS...... 86 Comparing the Findings to the Extant Literature ...... 89 ANATOMY OF DISRUPTIVE TECHNOLOGIES ...... 92 MAINFRAME COMPUTERS VS. PERSONAL COMPUTERS (PCS): DISRUPTION RANGE ...... 95 Analysis and Categorization of this Disruption ...... 95 MAINFRAME COMPUTERS VS. PCS: STRATEGIC RESPONSE AND FIRM PERFORMANCE ...100 Qualitative Analysis ...... 101 Statistical Analysis...... 102 CHAPTER 5 CONCLUSIONS AND STRATEGIC IMPLICATIONS ...... 107 DISRUPTION RANGE ...... 107 Interpreting the Analysis ...... 107 Applying the Interpretation to the Selected Industries...... 113 STRATEGIC RESPONSE IN THE COMPUTER INDUSTRY ...... 115 STRATEGIC AND MANAGERIAL IMPLICATIONS ...... 117 Eye of the Beholder, Redux ...... 117 Expansion of the Implications...... 118 FUTURE DEVELOPMENT OF THIS RESEARCH ...... 121 APPENDIX A: DISRUPTION RANGE DATA SOURCES...... 124 APPENDIX B: SOURCE DATA – FOUNDATION INDUSTRIES ...... 126 APPENDIX C: SAMPLE INTERVIEW GUIDE ...... 134 APPENDIX D: STYLIZED SUMMARIES...... 135 APPENDIX E: STRATEGIC RESPONSE DATA SOURCES...... 137 APPENDIX F: SOURCE DATA – COMPUTER INDUSTRY ...... 138 APPENDIX G: STRATEGIC RESPONSE DATA – COMPUTER INDUSTRY....140 APPENDIX H: REGRESSION SUMMARY TABLE ...... 141 APPENDIX I: INDUSTRY EXPERTS ...... 143 WORKS CITED ...... 144

LIST OF FIGURES

FIGURE 1 TECHNOLOGY CYCLE...... 12 FIGURE 2 ELEMENTS OF TECHNOLOGICAL DISRUPTION ...... 26 FIGURE 3 RESEARCH DESIGN...... 42 FIGURE 4 TOTAL NUMBER OF TELECOM FIRMS (WIRELINE AND CELLULAR) ...... 62 FIGURE 5 PER CAPITA TELECOM REVENUES (ADJUSTED FOR INFLATION) ...... 65 FIGURE 6 COMPARISON OF CHANGE IN CPI AND CPI TELEPHONE SERVICES...... 66 FIGURE 7 INTERSTATE SWITCHED ACCESS MINUTES...... 67 FIGURE 8 STYLIZED SUMMARY: TELECOM...... 68 FIGURE 9 NUMBER OF LICENSED TV BROADCASTERS...... 72 FIGURE 10 MOTION PICTURE SCREENS ...... 73 FIGURE 11 BOX OFFICE GROSS ADJUSTED ...... 74 FIGURE 12 TV BROADCASTING REVENUES ...... 76 FIGURE 13 STYLIZED SUMMARY: MOTION PICTURES VS. TV ...... 77 FIGURE 14 NUMBER OF FIRMS: MOTION PICTURE PRODUCERS AND VIDEO RENTAL ...... 79 FIGURE 15 MOTION PICTURE PRODUCTION: REVENUES...... 81 FIGURE 16 BOX OFFICE GROSS REVENUES...... 83 FIGURE 17 PER CAPITA MOVIE ADMISSIONS...... 84 FIGURE 18 VIDEO RENTAL REVENUES (ADJUSTED FOR INFLATION) ...... 85 FIGURE 19 STYLIZED SUMMARY: MOVIEGOING VS. HOME VIDEO ...... 86 FIGURE 20 CATEGORIZATION OF TECHNOLOGICAL INNOVATION ...... 93 FIGURE 21 NUMBER OF FIRMS IN THE COMPUTER INDUSTRY...... 96 FIGURE 22 COMPUTER INDUSTRY: INTENSITY...... 96 FIGURE 23 COMPUTER INDUSTRY REVENUES (ADJUSTED FOR INFLATION) ...... 97 FIGURE 24 COMPUTER INDUSTRY REVENUES: INCUMBENTS VS. ENTRANTS ...... 98

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LIST OF TABLES

TABLE 1 INDUSTRIES STUDIED/SICS...... 49 TABLE 2 TELECOM INDUSTRY: INTENSITY ...... 63 TABLE 3 PER CAPITA TELECOM REVENUES ...... 65 TABLE 4 NUMBER OF LICENSED TV BROADCASTERS...... 72 TABLE 5 REVENUES ...... 75 TABLE 6 MOVIEGOING VS. HOME MOVIES: COUNT AND INTENSITY ...... 79 TABLE 7 BOX OFFICE GROSS REVENUES ...... 83 TABLE 8 VIDEO RENTAL REVENUES ...... 84 TABLE 9 SUMMARY – CROSS-INDUSTRY ANALYSIS ...... 86 TABLE 10 SUMMARY TABLE...... 99

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CHAPTER 1

OVERVIEW AND PURPOSE

In the past several decades, numerous industries and companies have been created or

obliterated, as the case may be, by exciting new technologies. Seemingly invulnerable icons like

IBM, Kodak, and Xerox were shaken and transformed forever by what Christensen (1997)

describes as disruptive technologies.

This phenomenon is neither new nor limited to high-technology products and services.

Cooper and Schendel (1976), for example, documented 22 firms across a variety of industries and times where this took place. Christensen (1997) popularized the term “disruptive technology” to describe the phenomenon. Although the word “technology” is used by Cooper and Schendel and others (Foster, 1986; Christensen, 1997) to describe this concept, nontechnical product markets have also experienced disruptions. Some industries such as locomotives and propellers, examined in Cooper and Schendel’s work would be considered technological in nature, while others, fountain pens and safety razors, certainly do not belong in that category. In the disruptive technology context, the term technology is used in a broad sense to refer not only to new products, but also to business practices employed to transform raw materials and subcomponents into the firm’s product or service.

Disruptive technology is a term used to describe the introduction of a new method, process,

or product category (hereafter referred to as technology) to a segment of a market that was

previously not served or to a segment that was over-served by an existing technology. Eventually,

the new offering serves to disrupt or unseat the existing not only in the segments described but with

majority or mainstream customers as well. The work in this area describes a unique convergence of

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circumstances. Not only is the new technology emerging, but it serves these two distinct groups: an existing but over-served segment, i.e. the technology was too sophisticated for the customers’ needs and/or an un-served segment. The product offered to these groups is inferior and often more

expensive than the product purchased by the mainstream customer base. The benefits of the original version of the new technology are not valued by the majority or mainstream group of customers. They do, however, provide enough benefits to the secondary segments to provide profitability to the opportunistic, entrant firm. The nascent technology is developed over time to a point where it eventually becomes attractive to a significant part of the incumbent firm’s customer

base, the mainstream customer. The new technology is disruptive when the incumbent technology

is rendered partly or wholly obsolete, causing the incumbent firm’s competencies to also be

diminished in usefulness and value.

Research Questions and Significance Thereof

An example of disruptive technology is the onset of digital imaging in the late 1990s.

Polaroid Corporation was enjoying a dominant market share of the instant imaging industry when digital imaging first arrived on the scene. Polaroid prided itself on making products that others could not. The company relied heavily on its research and development, which was centered in the field of chemical technology. While growth estimates for the digital imaging business ranged from

36 percent to 100 percent over a five-year time frame, Polaroid continued to see digital as an “outer ring” relative to the company’s core business of instant photography. Management believed that instant imaging would continue to be their main competitive weapon. Resource allocation, effort and energy were still centered on chemical film imaging. Digital was supported as a secondary product offering (Pruyne and Rosenbloom, 1997). Subsequently, digital products and technology exploded on the market and eclipsed film and instant imaging. Polaroid was ill equipped to

compete in this market, as its attention to digital had been an afterthought, overshadowed by the company’s dedication to chemical film. The core competencies of the firm, chemical R & D, were ineffective in the new market structure, and the firm’s capabilities relative to digital were weak, relative to many rivals.

Polaroid’s reaction was not atypical of firms facing potential disruption. Often incumbent firms become myopic and defensive in their treatment of a potentially disruptive technology.

Polaroid’s sales and profits plummeted in the late 1990s and the firm filed for Chapter 11 bankruptcy protection in 2001. The driving forces in this catastrophe were a new technological development and its effect on consumer demand. This resulted in a precipitous drop in revenues for the firm’s core product offering, instant imaging, as sales of digital photography equipment skyrocketed.

By contrast, an example of technological innovation that did not result in a disruption is helpful to more clearly define and understand disruptive technology. Take, for instance, the example of Dell Inc. The company entered the personal computer (PC) market utilizing several new processes to create a competitive advantage. Dell used mail order, and eventually the Internet, instead of the wholesale and retail channel customarily used by PC manufacturers, to reach consumers. The firm also used just-in-time delivery and low inventory methods to reduce cost.

Although neither of these business practices was invented by Michael Dell, the company’s founder, he was the first to apply them to the PC market. Thus, as described previously, a new technology in the form of new methods or processes was introduced. This, however, did not disrupt the market.

Extant companies like Compaq, Apple, and Sun had a new competitor, with innovative technologies that put pressure on their market share and profitability, but the new technology did not render their competencies and capabilities obsolete. The new practices that Dell brought to the industry were sustaining and additive, rather than disruptive. As proof, Compaq and the other rivals

eventually utilized many of Dell’s ideas in addition to their traditional methods to build and sustain their businesses.

These two examples illustrate an important distinction and call attention to the need for precise understanding of disruptive technology. Managers need to be able to interpret technological innovations and their potential impact on the business of the firm. Managers should approach technology that has the potential to disrupt differently from technologies that can be adapted and added to the firm’s current capabilities. Academic research that qualifies the characteristics and trends of disruptive technological change and differentiates those from the characteristics and trends of incremental technological change can help to improve management decision-making during times of heightened uncertainty. This underscores the importance of the first goal of this research:

• To determine if changes in industry conditions (number of competitors, intensity of competition, and revenues) can be used to diagnose whether a technological innovation is causing radical or incremental change.

If industry conditions enable the categorization of the disruption, can the category be used to indicate the firm’s best possible strategic response? This important question leads to the second goal of this research:

• To determine if there are tailored strategic responses (joint venture, acquisition, internal research and development, strategic business unit or no response) that increase incumbent firms’ likelihood of positive performance in the face of disruptive technological innovation. And to determine if the size of the firm and the timing of the chosen response have a bearing on that performance.

These research questions were addressed in this work through a careful combination of

primary research and analysis of historical market data.

Eye of the Beholder

Disruption is a term that in common parlance generally has a negative connotation. This negative undertone is strong when disruptive technology is the subject of academic research.

Disruptive technology is considered largely from the perspective of the disrupted, incumbent firms with little attention paid to the innovating firm that is entering the market. As will be seen in

Chapter 2, the emphasis in the literature rests more on the destruction levied by the innovation and less on the creation of new technologies and companies. In reality the meaning of the word disruption is not to destroy, but to break apart or to split up. Applying this definition encourages researchers to consider both perspectives on the subject, that of the existing firms and that of the new ones. The market will be split between them in some fashion so the significance of the disruption is in the eye of the beholder. How the market will be split, and which technologies and companies will emerge from the disruption is the source of much examination. Managers and entrepreneurs alike can benefit from possessing a keen understanding of how to identify a disruptive technology and what strategic responses to use.

From the perspective of the strategists at an established firm, it is customary to conduct environmental scanning and ongoing, sophisticated data collection and analysis to identify threats in the external environment. Fundamental strategy literature instructs the business manager to look for threats from potential new entrants, substitute products, and rivals (Porter, 1980). But the manager must decide the degree of threat posed by an innovation introduced by any of the above-mentioned competitors. Is the core business threatened by the innovation or is the threat less serious? The new technology might prove to be disruptive, splitting the customer base and gradually taking it away from the incumbent technology. Alternatively, the new technology might coexist with existing technology competing for the same customers without rendering the incumbent obsolete. Finally,

the new technology might simply be a “flash in the pan,” one that is a short-term fad or has performance flaws that quickly kill its popularity.

It is possible that, as in the case of Dell, the innovation could coexist with traditional technology and compete for market share without destroying the value of the incumbent firms’ offerings or processes. It is also possible that the innovation will not succeed. Kodak’s “disk” film of the 1980s was a new technology that had very short-term popularity and very little impact on the firm’s rivals. In the case of Polaroid and digital imaging, however, the technological innovation would not coexist, nor would it go away. It would remain and capture the majority of Polaroid’s market. Polaroid’s strength in chemicals and chemical photography innovation would no longer provide the firm with a meaningful advantage in the marketplace and could not blunt the disruption created by digital imaging.

As Christensen and Raynor (2004) stated, there is a time at the beginning of an enterprise or the very early stages of the development of an innovation when a deliberate strategy is impossible or difficult to know. Perhaps this is part of the reason so little has been written about disruptive technologies from the perspective of the disruptor. In reality, strategy for the innovating firm in the early stages of the invention may be unknowable. Despite this, understanding the characteristics of disruptions that persist and how they are different from those that do not endure would be meaningful to the innovating firm. This knowledge could alter the confidence, energy, and resource allocation with which entrepreneurs and managers of innovation pursue a new offering.

The strategy that managers should formulate hinges on management’s ability to discern the difference between various types of threats and opportunities. Incumbent firms frequently miscalculate the seriousness of a new technology and the probability of true disruption. Innovators frequently operate with little information, little more to go on than their own entrepreneurial gut

instincts. If managers are to become more successful, to raise the likelihood that they will properly understand the difference between a disruptive technology and one that is not, they must have more specific understanding of the makeup of a disruptive technology. Although this information is essential to managers and strategists, the literature on the subject is still underdeveloped and often anecdotal. Therefore, the goal of this research is to provide a more precise understanding of the phenomenon of disruptive technology.

The time during which an industry is disrupted has been referred to as the inflection point

(Grove, 1996) and the window of learning (Christensen, Suarez, and Utterback, 1998) and appears

to be a critical period, but is still only vaguely understood. In order to gain a better understanding

of this time frame, an in-depth analysis of four disrupted industries was conducted. Others

(Schnaars, 1994; Christensen, et al., 1998; Gilbert and Bower, 2002) have tested elements of the

phenomenon such as the timing of incumbent firm response, the size of the incumbent firm, and the

strategic response and found them to be significant variables to firm success or failure in other

industries. These variables will be further tested in this work based on data from the computer

industry.

This dissertation topic was researched so that interested academics, entrepreneurs, and executives will be better able to identify and understand the parameters of a particular type of technological change. Consequently, practitioners will be more capable of crafting responses that fit the circumstances.

Organization of the Dissertation

Subsequent chapters focus on the literature review, research methodology, analyses and

findings, and the conclusion. Chapter 2 includes the review of previous germane literature and

provides the contextual basis for the work conducted. The concepts of creative destruction and the

technology cycle are explained to build an understanding of the industry conditions that exist when a technological discontinuity occurs. However, disruptive technology cannot be explained solely from this macro perspective. For clarity, a micro perspective considering the firm as the unit of analysis has also been conducted. Past research in the areas of resource dependence, resource allocation, dynamic capabilities and core rigidities provide further context from which the current research was structured. Included in Chapter 2 are the research propositions that were developed and used to structure the inquiry.

Chapter 3 delineates the methods used to test the propositions. Chapter 4 explains the

analysis that was conducted and the findings that resulted. Chapter 5 discusses the conclusions

drawn from the study, the limitations of the work, the ideas for future research generated in the

process, the implications for executives in companies imperiled by new technologies and for

entrepreneurs who want to challenge incumbents.

CHAPTER 2

LITERATURE REVIEW, CONCEPTUAL FRAMEWORK, AND PROPOSITIONS

Many avenues of research pertain to the study of disruptive technology. In order to better understand the context in which this topic has been investigated and ground the current research in those findings, many streams of literature have been reviewed. Some researchers have approached the subject from the level of the industry and others from the level of the firm. Each of these categories is discussed below. A more specific review of the prior research on disruptive technologies follows the contextual literature, and the final section of this chapter lists and discusses the propositions to be studied.

The Contextual Literature: Industry Level Analysis

Understanding the impact and influence of technological change as it affects industries is one critical area of the literature. The primary subject areas that use the industry as the unit of analysis are: creative destruction and the technology cycle. Each is reviewed below.

Creative Destruction

Creative Destruction, as first identified by Schumpeter (1939), was an economic concept

describing the role of entrepreneurship in the progression of an economy. Specifically, Schumpeter

identified entrepreneurs as those primarily responsible for inventions that replace existing

technology and processes. The quest for wealth development in a free-market system drives the

entrepreneur to be creative and invent. These inventions cause destruction in the sense that they

often destroy the value of those products and services that came before. The newly created products

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offer improvements and advancements that older products cannot match. Although destruction (of the old) is taking place, the newly created product or service drives an economy to a higher standard

of living. Schumpeter observed that despite economic systems’ tendency toward equilibrium,

economic evolution is discontinuous and therefore not purely evolutionary, but at times

revolutionary.

According to Schumpeter, the discontinuity begins when an entrepreneur introduces a substantial innovation. The characteristic of being substantial is critical to his definition. An innovation causes the discontinuity not because it is new, but because it is substantial enough to require capital expenditures, the building of new or renovating of existing plants, and the creation of new firms. These firms often initially enjoy a monopoly, but serve to smooth the path for other entrepreneurs to introduce innovations in associated or related fields.

These entrepreneurial innovations are destructive to the older non-innovating firms because the new companies begin to occupy the minds and resources of lenders, investors, customers, suppliers, and workers, destroying their interest in the old market. They are creative in the sense that they give rise to new manufacturing or product markets, new credit and investment markets, and new consumer or customer markets. These new markets usually produce a rise in prices and general economic expansion.

Thus, the innovation is the impetus for discontinuous economic change only if the above

consequences are realized. Innovations that do not produce these substantial consequences are

interesting and have market effects, but are not the force that Schumpeter references that causes true

change and economic development of a society.

Tripsas (1997) distinguishes between Schumpeter’s early work and his 1950s publication on creative destruction. In the early work creative destruction was described in the context of industries that were fluid, allowing new technological innovations to leap frog and displace the

existing, only to have the cycle repeat itself and the nascent products be displaced or destroyed.

Tripsas states that in the later work Schumpeter makes a distinction. The concept is refined to include the advantage held by incumbents over entrants when high capital investment is required for the new technology, when specialized and costly assets are needed to gain advantage. The incumbent, having a revenue stream and investment base considerably larger than the firms offering the nascent technology, often has the advantage in this context, even when the new technology has destructive potential. For example, automobile manufacturers would hold an enormous scale advantage over upstart companies offering alternatives to internal combustion engines.

Schumpeter’s distinction between innovation that causes discontinuous change and that which does not is the foundation of the current literature on disruptive technology. It will also become apparent that his work influenced the development of the literature on the technology cycle and dominant design.

Technology Cycle

The technology cycle was introduced nearly 30 years ago and has been researched and considered by scholars in a variety of fields (Abernathy, 1978; Abernathy and Utterback, 1978;

Tushman and Murmann, 1998). While Schumpeter’s work emphasized the entrepreneur’s role in creative destruction, the technology cycle focuses entirely on innovations, not the innovators.

Schumpeter’s entrepreneurial innovations and the technologies referred to in the technology cycle literature are closely related. Both refer to new inventions or existing products applied in a new way to create new business. Michael Dell’s (an entrepreneur’s) application of the Internet (an innovation or new technology) to the personal computer market is an example that helps to illustrate this distinction between the innovator and the invention. This new system, the Internet, was an innovation. Michael Dell used it to alter the business of supplying personal computers.

Both Schumpeter and technology cycle researchers distinguish between inventions

(technologies or entrepreneurial innovations) that create a discontinuity versus those that merely

build on existing technologies to improve current products or businesses. The Michael Dell

example is also relevant to this distinction. His entry into the PC market did not create a

discontinuity or disruption in the manner of creative destruction. Dell’s application of the Internet

to the business of supplying PCs was sustaining to the industry. Although it created more

competition and made life difficult for rivals like Compaq, these existing firms were not disrupted.

If there had been a disruption, PC makers would have had the core competencies of their firms

rendered obsolete. This did not happen. These firms were forced to learn new skills in addition to

the competencies they already possessed, but their existing capabilities retained value.

Figure 1 Technology Cycle

C Nascent B Technology C A B

Performance A: Era of Ferment A B: Coalescence of Dominant Design C: Era of Incremental Change Incumbent Technology

Investment, Time and Effort History reveals that technology, which is used in a very broad sense to refer to business processes and practices, progresses through stages in a fairly predictable pattern. This pattern has been named the technology cycle. The stages in the cycle, depicted in Figure 1, are the era of ferment, the dominant design, and the era of incremental change (Abernathy, 1978; Abernathy and

Utterback, 1978). The jump from one technology regime to another, pictured in Figure 1 as the move from the first curve (incumbent technology) to the second one (nascent technology), is what

Schumpeter described as creative destruction. Although incumbent firms often continue to develop

their technology and get increased performance out of it (Cooper and Schendel, 1976), the commercial viability and profit potential of those products or processes suffer destruction due to the

creation of the nascent technology.

The initial phase that a new technology must survive, the era of ferment, is a time of great vulnerability. A new technology in its infancy is surrounded by uncertainty. The uncertainty in this era centers on whether the technology will grow and, if it does, which variation of it will take hold and succeed. An idea that has very low certainty of successful commercialization, but very high certainty of a costly development cycle, is often looked upon unfavorably. VHS versus Beta in the early days of videocassette recording is a good example of this competition and uncertainty. Some companies expected Beta to take over and therefore invested in it, only to be surprised by the success and dominance of VHS. If companies believe that an incipient technology will succeed, they pursue it with the goal of using proprietary know-how to create the variant most sought after— the dominant design. The dominant design evolves only after an era of ferment (Anderson and

Tushman, 1998).

Prior to a dominant design there is a period of instability caused by rapidly developing technology. During this era the seed of an idea exists and rivals vie to develop the concept. The advancement of the idea requires firms to determine the set of features and benefits that they believe customers will find most appealing. The attributes and benefits are derived from a winning arrangement of component parts and the mechanisms that join the components together. These pieces, and how they fit together to form a new technology, are what Murmann and Tushman

(1998) referred to as a bundle of subsystems. None of the competitors knows with certainty during this phase what the most favored bundle will be.

Throughout the era of ferment, new competitors with diverse capabilities enter the market

intending to utilize their unique set of resources to invent the dominant design and capture profits.

Understanding the era of ferment is important because it is during this period that the threat of the substitute or potential new entrant should become apparent to strategists at incumbent firms. One of the goals of this research was to determine if industry level changes during the era of ferment can be used to diagnose the disruptive potential of a technological innovation. Do changes, measured at the industry level, indicate that an incipient technology may serve to disrupt?

Once a bundle of subsystems is identified and takes root, the variation and flexibility in processes employed by firms attempting to gain an advantage via the new entity are replaced by more specialized and standardized processing clustered around the dominant design (Abernathy,

1978; Abernathy and Utterback, 1978). Anderson and Tushman (1990) argue that the dominant design does not result from the conscious selection by one firm of the single best bundle of subsystems but from an evolution through variations that leads to the dominant design. Thus the dominant design is not selected because of its technological or economic promise, but because of social, political, and institutional forces that are governed by economic and technical constraints

(Murmann and Tushman, 1998). Precisely what institutional forces they reference is not clear.

There continues to be debate as to how the market converges on a particular dominant design.

Abernathy (1978) argues that the development of a product type that has broad appeal attracts a large share of users and subsequently rivals. Competitors see the winning design and quickly mimic it to attract the user base.

While dominant design refers to a particular technology, the era of incremental change refers to a period of time in the technology cycle (Anderson and Tushman, 1990). Another period of time in the technology cycle, the era of incremental change, follows the coalescence of the dominant design. During this time the evolution of the winning design is driven by technical and economic forces. The emphasis for firms participating in a market through incremental change is on process innovations and product improvements, clustered around the now established dominant

design of the product. Throughout this phase of the technology cycle, companies begin to build on the model that has been established as dominant to improve and adjust their offerings and increase efficiencies. They cannot, however, ignore nascent technologies that are on the horizon. The study of technology cycles has shown that another predictable occurrence, a new era of ferment, may occur. The cycle begins again when discontinuous change threatens the position of the dominant design. When there is a shift from ferment to a new dominant design, there is a change from one technology regime to another (Jenkins, 1975). Often, multiple regimes are strung together creating long, evolutionary cycles punctuated by the occasional shift or revolution. If this shift to the new technology regime renders the old obsolete, it is referred to as a disruptive technology.

Although research on the technology cycle has provided insights into radical, discontinuous change in markets, examples of firms being devastated by the discontinuities continue. Research to extend the knowledge base could help incumbents and entrants to better understand and manage times of revolutionary change. The time span from one dominant design to the next can be thought of as a disruption range. It is a period during which there is both incremental change to existing technologies and fermentation of nascent ones. It is a time of technological regime change and includes what others have called simply discontinuity. The technology cycle literature describes what is happening to technologies during the disruption range, but what is happening to the composition of the industry has not been investigated. Therefore, this research proposed to investigate the changes in the structure of industries during the disruption range.

P1: The onset of the disruption range can be identified initially by a marked increase in population density (the number of rivals competing for the customer base).

P2: The ending of the disruption range can be identified by a sharp decrease in the population density of the market.

P3: Changes in the population intensity (the number of firms needed to constitute 75 percent of the market share) occur. The intensity in the first phase of the disruption range is greater than the intensity in a later phase. The mean or

average number of firms constituting 75 percent market share is smaller during Phase 1, indicating a more concentrated, more intense population in Phase 1 than in Phase 2.

The Contextual Literature: Firm Level Analysis

The industry technology cycle becomes more meaningful when one considers how it informs business processes and management decision-making on a firm level. Managers of firms need to understand the cycle and the disruption range if they are to anticipate and prepare for future probable developments. Perhaps most importantly, more thorough understanding allows managers to plan to take advantage of the shifts from one technology regime to another rather than being disrupted or displaced by this occurrence. Several different streams of literature using firm level analysis are related to the technology cycle. Specifically, the following sections look at the allocation of and dependence on resources, firm capabilities, and rigidities.

Firm Level Analysis: Resource Dependence and Resource Allocation

On a firm level, the era of incremental change provides focus. Because resources are concentrated on one path, the firm is more able to progress along that path; with this comes higher levels of achievement and firm performance. Capabilities and assets are specialized and used to create depth of knowledge so significant that the firm overshadows rivals relative to the dominant design, thus affording the company strength and muscle in the marketplace.

This strength often allows the firm to gain economies of scale that contribute to increased firm performance. Microsoft offers an illustration of how this concentration and single-mindedness are beneficial in some situations. Microsoft Office has for years found ways to continuously improve the features and benefits provided to the large customer base it serves. Focus on this clientele is partially responsible for Microsoft’s ability to offer attractive advancements to customers that allow the firm to stay in front of its rivals. The way components are pieced together

and the cooperation among the firm’s employees to satisfy this customer base is an underpinning of the corporation’s dominance. This concentration creates insights into the customers’ wants and needs, and enables the company to continue to outperform the competition.

The muscle, however, can become problematic for companies if not managed carefully. If the singular focus creates a sluggishness or inability to change, inertia can result (Hannan and

Freeman, 1984). If this inertia becomes the driving force for the business, the firm becomes incapable of any meaningful strategic or structural change and the relative importance and potential impact of the technology cycle is dismissed or disregarded. Polaroid once again provides an example. Polaroid’s focus on chemical photography enabled the firm to provide customers who demanded instant photography with ever-increasing features and benefits. Polaroid knew this market well and was able to outpace rivals or would-be competitors to the point where the company had a near monopoly. This evolved into inertia, which caused the firm to dismiss the reality of the threat posed by digital photography.

A firm enjoying success in a given market cannot afford to succumb to inertia; the company and its managers must heed the patterns of the technology cycle demonstrated time and again by history. The firm must anticipate that one technology regime may give way to another and understand that the skills needed to create or even benefit from the next dominant design may well be very different from those needed to exploit the dominant design of the current regime.

Incumbent firms need to embrace this because the nature of the disruptive technology is that it obsoletes or at least significantly diminishes the existing technology. If the incumbent technology is rendered obsolete and the incumbent firm’s singular focus created inertia, the firm risks being one of the casualties of the disruption.

The causes of inertia are further explained by Pfeffer and Salancik (1978) in their work on resource dependence. Companies are dependent upon customers for profits and goal achievement

and on investors for financial support of current and future growth. The resources that firms need to prosper are provided by these external and very influential sources, and the organization becomes dependent upon them. The dependence created can serve to limit the scope of creativity and change that the company practices. Specifically, firms actively pursue change if it enables the further development of the current dominant design, because that is the design valued by the mainstream customers. Investors are in favor of this type of change as well, because a higher level of customer satisfaction leads investors to feel more certain about the stability of returns. Change or creativity directed at innovation or emerging products that are not demanded by the mainstream customer are unacceptable. There is too much uncertainty that surrounds these innovations, as they do not serve a known customer base. The existing customers are not interested and investors perceive the inventions as risky. For example, Digital Equipment Corporation was reluctant to introduce a line of PCs. The firm’s customers, large corporations, needed the computing power and memory capacity of the mainframe, therefore the company considered the PC inferior. The over-served smaller business and un-served home office, small office customers were the segments to whom the

entrant firms appealed.

An example of customer power and its impact on firm success appears in Christensen and

Bower’s (1996) study of Control Data Corporation (CDC). When CDC initiated a new line of

products, the 5.25-inch disk to replace the 14- and 8-inch disks, the company located the production

plant in Oklahoma City, many miles from CDC’s existing Minneapolis plant. Management

forecasted that because the new product was emerging, initial orders would be smaller than orders

customarily received for the established lines. Although the company was confident about the new

product offering, the reaction of plant workers to the smaller orders was of concern. Managers at

the company feared that familiarity and comfort with large orders would desensitize workers to the

smaller orders. Employees might interpret smaller orders as an indication that the new product was

inferior to the existing, creating low enthusiasm and consideration. The concern was that the lack of attention to orders for the new product could undermine the 5.25-inch disk in its infancy. In this scenario, employees’ focus on large orders for existing products represents dependence on the current customer base. The inattention to smaller orders for new products and from new customers exhibits inflexibility that could prevent the company from making needed adjustments to changes in technology. By locating the new product facility separate from the existing products’ plant, CDC mangers attempted to manage resource dependence, suspend inertia, and benefit from innovation.

In addition, firms’ resource allocation processes often feed dependence by favoring projects

and initiatives that support customers’ needs and the investors’ expectations. The dedication of the

firm’s resources to a particular project or product is a tangible, measurable sign of the firm’s

commitment. Bower’s (1970) resource allocation model identifies three common steps that move a

firm toward the commitment to a project or product: definition, impetus, and commitment.

Definition is the time when a new initiative is identified and the financial and technical

specifications of an idea are fleshed out. Once defined, these ideas go through an evaluation and

selection process and, if selected, enter the impetus stage. This stage gives the concept energy to

propel it further into the organization, increasing the importance of the idea and the likelihood that it

could move from a mere concept to an actual development project. With impetus, a decision about

the initiative must be made. Will the firm place resources behind this idea and commit to it? If the

answer is yes, the third stage of the resource allocation model has been met: commitment. If the

answer is no, the product or project is abandoned.

Bower noted that firm members at different levels play key roles at each stage. Definition is the domain of people at the operational level of the organization. Often engineers identify and define new ways for the company and its products to progress. The force behind this step in the process is largely technical—what can be done? “What is our firm capable of adding to this

product group?” Once the operational units identify and define the concept, middle managers become the key players. They are responsible for evaluating the newly defined ideas—what should be done? Mid-level managers determine whether to propel the initiative through the organization in search of resources.

Middle managers face some risk in backing a particular, newly defined idea. They reduce that risk by assessing the likelihood that the idea will be met with favor by higher-level managers who allocate or withhold resources. Favor is often granted based on old technology regimes and target achievement levels of established products (as in the CDC example). Burgelman (1983) calls this “induced strategic behavior” (p. 64). He notes that strategy enacted by senior management is the guide for action throughout the firm. The nature of strategy is to prescribe for firm members what products and behaviors to pursue and what decisions to make. Therefore the behaviors exhibited by personnel that align with the strategy are considered “induced”. If the strategy set by top-level management is built upon or focused around a firm’s past successes, then the middle managers are conditioned to place impetus behind incremental changes to existing products; they are induced by the strategy to pursue the company’s current trajectory.

Another element of risk is that middle managers have their personal capital at stake. If they routinely back projects that do not reach the commitment stage, it reflects negatively on them and their personal capital is diminished. If the projects that they champion meet with favor and are bestowed resources, their personal capital is enhanced. If a middle manager feels the new initiative does not fit the company’s current mission or program, he recognizes it as being higher risk and is less likely to support or champion the idea. Therefore, those ideas that are more in keeping with the company’s current trajectory (which is based upon past offerings and performance) proceed through the impetus stage more often, here again behaviors that uphold the current strategy are induced. So the ideas presented to top management for resource allocation tend to be incremental in nature,

offering improvements and adjustments to existing items. The initiatives are “new and improved” rather than “new.”

Bower (1970) acknowledged that this “bottoms-up” approach is not the sole means of a project gaining commitment. He cited examples wherein senior managers rather than operational employees initiated new projects. There are also examples of projects gaining impetus without the integrating role of middle management. These, however, tend to be the exception, and the bottoms- up procession through the three discreet phases of definition, impetus, and commitment is the rule.

The examination of resource dependence and resource allocation revealed that there is significant pressure for incumbents to stay committed to the current product offerings even at the cost of adjusting to new developments in the marketplace.

Firm Level Analysis: Dynamic Capabilities and Core Rigidities

Two additional streams of literature lend important insight into the context that exists for firms faced with technological discontinuities, regime change, and potential disruption: dynamic capabilities and core rigidities. The first, dynamic capabilities, was initiated by Teece and Pisano

(1994) and extended by others (Teece, Pisano, and Shuen, 1997; Eisenhardt and Martin, 2000;

Winter, 2003). This field of research challenges academics and practitioners to broaden the scope and function of strategy. Strategy, as described earlier, is a prescription for action based upon the firm’s competencies, past behaviors, and successes. This definition is supported by Burgelman’s

(1983) concept of induced strategic behavior and the idea of path or resource dependencies (Pfeffer and Salancik, 1978). Dynamic capabilities introduced the idea that competitive advantage in the face of technological regime change is derived from the ability of the firm to understand the new and different demands presented by a high-velocity market, and capitalize on them through the

reconfiguration of internal competencies, such as product design or manufacture, and external competencies, such as company reputation or customer relations.

“We define dynamic capabilities as the firm’s ability to integrate, build, and reconfigure internal and external competences to address rapidly changing environments. Dynamic capabilities thus reflect an organization’s ability to achieve new and innovative forms of competitive advantage given path dependencies and market positions” (Teece, Pisano and Shuen, 1994, p. 516).

Surely this conceptualization of strategy was not entirely new. Burgelman (1983) strongly alluded to this when he analyzed the effects of “autonomous strategic” (p. 62) behavior on firm structure. As compared to induced strategic behavior, autonomous strategic behavior is when entrepreneurial mid-level managers identify new and different opportunities that are outside the established or institutionalized strategy. The savvy manager determines how to package the new initiative so that it seems to fit with the existing strategy and can therefore gain impetus and move on to commitment. The difference here is that these initiatives, if not properly packaged and sold, will be terminated because they are not supported by current resource and path dependencies. If the manager is able to package and sell the unusual idea, then strategy is adjusted so that the autonomous strategic behavior is embraced and can be viewed as in keeping with the strategy.

The concept of dynamic capabilities suggests that some firms nurture this type of entrepreneurial spirit and adaptability to the point where it becomes a competence. These firms have firm-specific assets that enable them to capture competitive advantage in the marketplace.

These firms also possess integrated groups of people and processes that enable them to build the company’s wealth through the creation of a unique advantage. Like other firms, these companies acquire and build these resources and capabilities to pursue an existing product offering. Firms with dynamic capabilities have an additional type of strength, one that is not built around the firm’s existing offering. This is the power to change and adjust, create, recognize, and embrace new

opportunities in an evolving or high-velocity market. Combining this capability with the traditional paradigm on strategy enables firms to avoid the creation of strategic blind spots (Teece, Pisano, and

Shuen, 1997) and provides a potential antidote to inertia. It creates a firm that not only encourages and benefits from the autonomous strategic behavior described by Burgelman (1983), but actually

causes this dynamism to become an ongoing or stable element of the firm’s strategy.

The second important body of literature in this category, core rigidities, was initiated by

Leonard-Barton (1992). She called attention to the fact that capabilities within a firm are rooted not just in skills, knowledge, and systems, but also in values and norms. Capabilities embedded in an organization’s (and its members’) values and norms can become rigid and very difficult to change.

It becomes difficult or impossible for employees to act contrary to those values, and therefore there is resistance to new products and processes that require new capabilities. For instance, long-time journalists may not be able to make the transition to Internet distribution of newspapers. Some would describe this simply as trying to “teach an old dog new tricks.” Therefore, if a firm does not have dynamic capabilities as one of the core values of the firm—a key element of the firm’s strategy—core rigidities may result in it becoming increasingly difficult for the firm to adjust to a changing technological regime.

Disruptive Technology and Disruption Range

Disruptive technology is at the nexus of all of these other theories. To date it has been loosely defined and therefore widely interpreted. Many researchers (Cooper and Schendel, 1976;

Foster, 1986; Dewar and Dutton, 1986; Anderson and Tushman, 1990; Henderson and Clark, 1990;

Henderson, 1993; Christensen and Rosenbloom, 1995; Christensen and Bower, 1996; Tripsas,

1997; Dahlin and Behrens, 2005; Husig, Hipp, and Dowling, 2005) have considered this topic.

Some have investigated under the heading of discontinuities (Foster, 1986) others using the

terminology of radical technological change (Cooper and Schendel; Dewar and Dutton; Anderson and Tushman; Henderson and Clark; Henderson; Dahlin and Behrens) and others using the nomenclature coined by Christensen, i.e. disruptive technologies (Christensen and Rosenbloom;

Christensen and Bower; Husig, Hipp, and Dowling). Dahlin and Behrens found that although the authors mentioned above all studied the same phenomenon (new, previously untested technologies causing significant change to an existing industry), there was a lack of continuity and overlap, not only in what the phenomenon was called, but in the definition as well.

Cooper and Schendel studied industries in which new technologies had “devastating impact.” The devastation was felt by the incumbent as almost full substitution occurred when the innovation was introduced. Foster’s graphic explanation of the gap between the s-curves (see

Figure 1) is another example of the explanation of radical, discontinuous change. In the 1990s

Anderson and Tushman, Henderson and Clark, and Christensen and a variety of co-authors attempted to draw a definitive line between radical and incremental change. As recently as 2005,

Dahlin and Behrens wrestled with the question, “When is an invention really radical?” and answered that such revolutionary technological change is when an invention has been successful in converting an industry. Husig, Hipp, and Dowling further defined radical technologies as those that substantially alter the basis of competition in an industry to the disadvantage of the incumbent firms in the market.

The elements and necessary conditions of the model

The term “disruptive technology” is something of a misnomer. Based upon the literature to date, it is not the technology itself that is inherently disruptive, but a convergence of market factors and conditions that give rise to a disruption in the marketplace. A nascent technology that is inferior to and more expensive than an incumbent and can be incrementally developed will not

necessarily or automatically be destructive to an existing technology. It must be introduced into a market that has other necessary conditions relative to the incumbent technology and firms and the customer base. The five elements of a disruptive technology are depicted in Figure 2. They are:

The nascent technology

The entrant firm

The incumbent technology

The incumbent firm

The customer base

Disruptive technology cannot be described just by the five elements of the phenomenon.

Certain conditions must exist to give rise to the disruption. These conditions are denoted as bulleted items in Figure 2. The following narrative explains each element and the related conditions.

The customer base that the incumbent firms serve must be segmentable. That is, there must be distinct groups of buyers with different needs or demands within the customer base. The incumbent firm has used the dominant technology to serve and satisfy a majority of the customer base. As mentioned previously, this majority is referred to as the mainstream customer. The majority of these buyers are satisfied by the incumbent technology and are referred to as mainstream customers. Mainstream customers can be further segmented with some subgroups described as high-end customers. These are the buyers who are interested in the incremental improvements made to the incumbent technology or dominant design. They are willing to pay more for “new and improved.” These are the customers upon whom the incumbent firm is heavily dependent for its resources and profits, now and in the future. Accordingly, the incumbent firm strives to continuously improve its offerings to the mainstream customers while concurrently finding process improvements to lower costs. Because of so much attention to the mainstream customers, the incumbent firm usually ignores another segment of the market, the low-end segment.

Figure 2 Elements of Technological Disruption

Nascent Technology Incumbent Technology • Inferior features relative to • Demanded by incumbent technology mainstream customer • More expensive than • Can be incrementally incumbent improved • Can be incrementally developed

Incumbent Firms Entrant Firms • Continuously improving • New capabilities product • Serves mainstream customer

Customer Base • Must be segmentable Mainstream Low-end customers High-end customers Un-served customers

Low-end customers are over-served by the incremental improvements to the incumbent technology. They are satisfied with earlier generations of the product and are not pleased to be forced to pay more for unneeded improvements. To use the example mentioned previously, this is the small business owner in the early 1980s who did not need the increased power or memory of the mainframe computer. The needs of this segment are ignored by the incumbents and become the target for the entrant firms.

Another subgroup is made up of the un-served customers, those who are not benefiting from the technology. For example, the average homeowner in the United States during the early

1980s did not have a computer at home and might never have considered one. Yet they became the

target audience for the entrant firms—Apple, Dell, Compaq, and others.

According to much of the research on this subject, in the formative years, a disruptive

technology is adopted by a small segment of an existing market but is not valued by the other, larger segments of the incumbents’ customer base. Based on the description above, it is not surprising that

the target segment for the disruptive technology consists of two kinds of customers. The first are

patrons interested in the low-end or standard incumbent technology and therefore are over-served

by the constant improvement to the existing technology. The second are customers not served at all

by the previous technology. Minicomputers provide an example of a low-end incumbent

technology. Initially, they were considered far inferior to mainframes. They had considerably less

computing power and memory capacity. They were appealing to small companies that did not need

the massive computing power and memory of the continuously improved IBM mainframe. The

other segment that found the minicomputer attractive was the small office, home office, or

individual consumer who had never purchased a computer.

Although it is not necessary, historically the firm offering the nascent technology is

generally either entrepreneurial or from an industry unrelated to the incumbent market, thus it is designated an entrant firm. It is necessary that the novice firm has competencies that allow it a unique value proposition with the un-served or over-served segment of the customer base. The nascent technology that results from these competencies is one with inferior performance relative to the incumbent technology. The profit margin potential is correspondingly small. The nascent technology is appealing to a relatively small segment of the customer base so it has low volume potential as well. The final necessary condition for the nascent technology is that it has potential for incremental improvement.

At first blush, the new or nascent technology appears to have little customer appeal or value because it does not satisfy the mainstream customer within the industry as well as the incumbent technology does. The nascent technology is inferior, or as stated by Cooper and Schendel “crude

and expensive” (1976, p. 61). Andrew Grove (1996) of Intel described it as trivial technology.

Therefore, the incipient technology receives little attention from incumbent firms. It is offered to a

niche segment by an entrant firm. For the nascent technology to become disruptive the entrant firm

must develop the technology to the point where:

it is eventually “good enough” for the incumbent firm’s mainstream customers,

it is eventually “cheap enough” or affordable for the incumbent firm’s mainstream customers, and it eventually overtakes and displaces nearly all of the incumbent technology’s market.

At the start of the disruption range, the incumbent technology is the dominant design. The improvements made to the product for mainstream customers are incremental, offering ever- increasing performance characteristics and augmented prices and profits. It is superior, initially, relative to the nascent products but is actually overperforming relative to the needs of some of the customer base, which have great revenue and profit potential in the future.

The incumbent firm serves the mainstream customer with the dominant design, which is

based upon the industry-standard technology. The incumbent firm has core competencies that align it with the needs of mainstream customers and allow the firm to offer a unique proposition that their clientele finds valuable. Henderson and Clark’s (1990) detailed explanation of competencies allows more complete understanding of the conditions that give rise to disruption. They distinguished between components and architecture of a firm’s offering. Components are the parts needed to construct a product. Changing the components of a product is often referred to as modularity.

Henderson and Clark confirmed that modular change, where the architecture of the product remains the same and the components are changed, is easy for rivals to mimic and seldom causes disruption.

Architecture refers to the way the components are connected to form a system so that a

product functions. Competencies are built around both components and architecture. But the

architectural innovation or change in the architecture, as described by Henderson and Clark (1990),

is the one that is more difficult for rivals to copy. Again an example illustrates the differences. In

computers there are various types of chips, a motherboard, bios, video cards, and other components.

How the component parts are connected is the architecture. Computer manufacturers all have

different methods of connecting and combining the parts to achieve stylized levels and types of

performance. This system of connectivity is an important proprietary firm resource.

It is more difficult for competitors to change, adjust, or mimic the elements of architecture

because the know-how that creates the architecture is often tacit and weakly defined. This know- how enables a firm to create a unique value proposition. Competitors may use the same components (these are easier to copy because they are tangible), but each firm combines and connects the components differently based upon their tacit knowledge. For instance, Southwest

Airlines (SWA) enjoys a very positive reputation and competitive strength for, among other things, the timeliness of flights. In fact, countless news stories, case analyses, and business reports have been written about the firm. Any interested competitor can gain a great deal of information about

SWAs union contracts, flight schedules, airplane fleet, recruiting and training programs to try to regain parity with SWA. Yet few, if any, have been able to mimic the performance of the firm.

There is something special about how all of these components are put together at SWA, how they interact and connect, that gives SWA a competitive advantage that is not easily copied.

Firms use competencies to stay aligned with the mainstream customer base in unique ways.

The architecture is like connective tissue and is often less well defined than the components. It

becomes difficult for members of these firms to know what or how to change the architecture

because, although they understand its value and how it works, it is not explicitly identified and

defined. The challenge of changing this architecture is what creates, in part, the resource

dependence identified by Pfeffer and Salacnik (1978). Continuing with the example of SWA, if the

company decided to alter the chemistry or culture of the firm and to utilize personnel differently, it

would be very difficult. If SWA went from the team-based system it uses now to a more traditional,

individual-based system, the employees would have a very hard time adjusting. It is not just the

skill set of each individual that allows SWA to accomplish consistent on-time arrivals and

departures; it is the way that each individual works with the others. SWA’s competency is based on

architecture.

Interactions among the elements

Knowledge of the interaction among the five elements of the disruption discussed above is

critical to the understanding of disruptions. The incumbent firm uses its know-how (architecture) to

continuously improve the incumbent technology so that the mainstream customer remains loyal and

the firm profitable. All is fine, for a while. But as Shanklin (2000) asserted, creative destruction is

often a zero-sum game. The entrant firm uses different components and/or architecture to structure

a nascent technology and begins the pursuit of the low-end or un-served segment of the customer

base. When the development of the nascent technology proves the new product “good enough” or

affordable for some of the incumbent’s over-served customers, the collision of the two states takes

place and a potential disruption is in the making (Christensen and Rosenbloom, 1995).

Both incumbent and entrant firms would benefit from understanding the pattern of changes

in industry revenues leading up to a clash such as this. Changes in revenues of incumbent and

entrant firms were examined to determine if a pattern could be identified. For incumbents,

knowledge of this pattern could facilitate change within the organization, despite the considerable pressures of resource dependence and inertia. For entrants, this type of data would not aid the firms in determining which configuration of the new technology will become the dominant design, but it could indicate that the product category has substantial promise and encourage appropriate investment of time and resources. To determine if a pattern in industry revenues can be identified, the following propositions were developed and tested.

P4: An early and late phase of the disruption range is marked by significant decline in sales for incumbent technologies.

P5: Revenues for the nascent technology increase gradually in the early phase and the rate of growth increases in the later phase.

P6: Based upon the findings in propositions 1-5, two phases of the disruption range can be identified: Phase 1 (the early phase) and Phase 2 (the late phase).

Initially, Henderson and Clark (1990) hypothesized that when the nascent technology presented the incumbent firm with a radical change to the architecture, the incumbent firm would be disrupted. Christensen, Suarez, and Utterback (1998) concurred. They found that entrant firms utilizing architectural innovation tend to be more successful than those attempting to capture the market with component innovations. Others have suggested that architectural innovation is not always disruptive. If the architectural innovation is one valued by the mainstream customer, the incumbent firm is sufficiently motivated, due to resource alignment with the mainstream customer, to learn the new architecture. It is only when the innovation is not of likely perceived value to majority of the mainstream customers that the incumbent firm dismisses the nascent technology and stands a greater potential of being disrupted and possibly displaced.

Firms offering the nascent technology pursue it mostly unfettered by competition. This reality is what Schumpeter (1939) referred to as the monopolistic nature of entrepreneurs’ early endeavors. They enjoy this enviable situation because the niches they serve are too small to be meaningful to the established firms. Even if an incumbent firm recognizes the new technology as

potentially disruptive, it does not pursue the segments targeted by the disrupters. Instead, existing firms attempt to make the new technology good enough to satisfy their mainstream customers. But to reiterate, the nascent technology is clearly inferior to that demanded by the mainstream. Even when the incumbent firm perceives a threat and allocates resources, the mission is usually misguided (Gilbert and Bower, 2002). The resources are allocated so that the firm will be ready when the new technology gets good enough to satisfy the mainstream customer. But, as

Schumpeter stated, the initial monopolistic entrepreneur has paved the way for other entrepreneurs.

By the time the incumbent firm and mainstream customer find the nascent product worthy, the field of rivals is already crowded, making it difficult for the incumbent to find a unique value proposition to offer.

If incumbent firms climb on board once the new technology gets to the point of “good enough,” there are two problems. First, the incumbent has not learned from the trial-and-error process allowed by the era of ferment. Therefore, the understanding of what works in this new technology and why is less complete than the competitors who have been involved in the experimentation and learning during the era of ferment. The second problem is that it is difficult to recognize that the dominant design has emerged. As a result, if the incumbent attempts to wait until the design is present, it will miss much of the beginning of the dominant design. Moreover, this is when much of the explosive growth takes place (Christensen and Raynor, 2004). So the existing firm that has delayed its entry to lessen its risk, finds itself with a less competitive product offering trying to gain momentum from a dead standstill. This disadvantage puts the company at risk of trying to reap sizable rewards from a market that is on the move and may have already entered a growth stage.

Disruption Range

Despite the valuable insights provided by all of the literature explored above, historical examples of firms disrupted by technological regime change abound. Schumpeter described the importance of innovation and entrepreneurs to the evolution of economies. The technology cycle literature depicted how technologies are altered during the disruption range, while resource dependence, resource allocation, dynamic capabilities, and core rigidities explained incumbent firms’ motivations, abilities, limitations, and behaviors. The literature on disruptive technology facilitates the understanding of the conditions that are necessary for a disruption to occur. The synthesis of all of these streams helped to identify a period of time, herein referred to as the disruption range, about which further understanding is needed. Disruption range is the term used to describe the period of time from the establishment of one dominant design to the development of a new one. It is a period during which there is both incremental change to existing technologies and fermentation of nascent ones. It is a time of technological regime change and includes what others have called simply discontinuity. This research seeks to improve the understanding of what is happening in industries during the disruption range.

Grove’s (1996) concept of the inflection point is similar to the disruption range construct.

Mathematically, the inflection point is when the rate of change of the slope of a curve changes sign.

For instance, the rate of change goes from negative to positive. Although there is certainly change in the industry, research has shown that there is not a single point in time that one can identify as the inflection point. Shanklin (2000) noted that incumbents are aware of a substantial change to their industry yet spend precious time “bewildered,” wondering why the business model is no longer working as well as it once did. Grove’s application of the inflection point is expanded in this work and investigation of the disruption range is pursued. This is the range during which the incumbent

must recognize and move to capture the new technology if it is to benefit from this new growth business. Conversely, it is the era during which entrants strive to dominate the new market.

In this study the propositions previously enumerated were used to guide the examination of a variety of disrupted industries. Common dimensions were measured in each industry so that

comparisons could be made between and among the markets. The goal was to determine if there

were patterns of change in the industry that could be used to better understand the disruption range.

If patterns occurred they could be used to indicate the potential of burgeoning technologies. Firms

employing this knowledge to recognize the onset of disruption would have the opportunity to

respond to the change in an advantageous timeframe. Timing of response is not the only factor

influencing a firm’s effectiveness in dealing with a disruption. The type of response was also

examined to determine what bearing it had on the degree of firm success within the new industry

structure (Christensen, 1997; Christensen, Suarez, and Utterback, 1998; Shanklin, 2000; Gilbert and

Bower, 2002). At the time of the proposal for this research, the major categories of strategic

response were expected to be:

• No reaction

• Intrapreneurship

• Merge/Acquire

• Establish independent strategic business unit (SBU)

Gilbert and Bower (2002) found that in the newspaper industry, firms that were considered

high performers were those t hat set up and implemented independent units to manage the new

Internet-based businesses. They drew a distinction between those independent units that were

autonomous and those that were connected, finding the latter to be subject to more confusion and

contradiction with regard to competitively pursuing the new venture. Some firms employed a semi-

autonomous unit with the hope of information sharing, strategic fit and synergy between the

existing enterprise and the new one. To the contrary, Stern and Henderson (2004) found that the nature of the external industry in the nascent market is often so different from the existing industry structure that transferring knowledge from an existing business to the semi-autonomous or sibling business is difficult or impossible. The questions raised by these studies gave rise to the following proposition:

P7: There is a relationship between the incumbent firm’s strategic response and that firm’s future performance. Firms that responded with an independent SBU fared better than any other category of response.

There are strong indications that the timing of firm response within the disruption range is

important to firm success. Christensen, Suarez, and Utterback (1998) identified a window of

learning. It is a phase immediately preceding the coalescence of the dominant design. They found

that firms entering the disk drive industry during this window fared better than those that entered at

other times. This element of the theory will be further tested through the analysis of proposition

eight.

P8: Firms that respond late in the early phase of the disruption had stronger future performance than those that respond in either the first or third phase of the disruption.

The final proposition also relates to the findings of Christensen, Suarez, and Utterback

(1998). They found that i n the disk drive industry there was a relationship between the size of the

firm and the likelihood that the firm would survive post-disruption. This finding will be tested by determining the impact of a firm’s size on future performance.

P9: The larger the firm at the time of introduction, the better the firm’s future performance.

In Chapter 3, the methods employed to test each of these propositions are thoroughly

explained.

CHAPTER 3

RESEARCH METHODOLOGY

This chapter presents the research methods used in this study. The chapter begins with an explanation of the use of the case study method. The second section explains the selection of the industries. The third discusses the sources of the evidence and the data collection methods. The analytical design is then explained. The chapter concludes with a discussion of the validity and reliability of the research design.

Choice of Case Study Method

“In brief, the case study allows an investigation to retain the holistic and meaningful characteristics of real-life events…especially when the boundaries between phenomenon and context are not clearly evident.”

Yin, 1994 p. 3 and 13

Yin’s description clearly fits the effort at hand. The circumstances surrounding companies making long-range, direction-setting decisions are very complex. Many events influence the decision-making process and the success or failure of plans. It is difficult to distinguish between disruptive technological events and the many other factors affecting an industry. Nevertheless, disruptive technologies should be studied and understood. The magnitude of the impact of new technologies that serve to disturb existing markets creates a need to understand them with greater precision.

36 37

When studying shifts in industries and reactions of firms to those changes, there are many

factors that combine to add complexity to the situation being examined. In order to have a more

precise, yet realistic, understanding of the phenomenon, the case study method was used. The case

method allows for the retention of the natural complexity while studying the concept for greater

understanding. The goal of this work is not to generalize to all industries or all disruptions. The

goal is to understand the phenomenon of disruptive technologies better so that the boundary between it and other phenomenon can be drawn. Once the more distinct understanding of the concept is postulated (based upon the research herein) a larger sample of markets that have been disrupted should be studied to determine if the portrayal of the phenomenon could be generalized to all discontinuities.

Definitions

As explained in Chapter 2, the term disruption has many interpretations due to its loose definition. In this study, an operational definition of disruptive technology was developed. The operational definition was used to measure the concept of technological disruption so that a more precise understanding of the phenomenon can be gained. Disruptive technology was defined for

this research as that technology which, when introduced in the marketplace, eventually surpasses the dollar sales volume of an existing technology or incumbent technology and serves to reduce the

revenues of the prevailing technology to less than 50 percent of its previous peak sales year. As

described by Christensen and others (Christensen, 1997; Cooper and Schendel, 1976), the new

technology provides the customer base with many of the same functions as the incumbent, but does

so using a different set of features and benefits. Although the operational definition applied in this

work eliminated the inclusion of many technological developments that others would regard as

disruptive, the rigidity and narrowness were intentional. A lexicon describing the concept of

disruptive technologies already exists; it was developed in the literature and discussed in Chapter 2.

It provides an insightful and provocative description of the phenomenon, but also leaves room for a great deal of interpretation that can cause confusion and disagreement about what qualifies as disruptive. The operational definition herein intentionally confines the definition of disruptive technology so that the boundaries that differentiate this type of industry change from others can be identified. The recognition of the boundaries complements the description developed in the lexical definition.

The reduction in the prevailing technology’s revenues to less than 50 percent of peak year sales was an important decision in the construction of this research. This decline represents a substantial change in an important industry condition. Schumpeter offered that a technology that required large capital investment, building of new or renovating of existing plants, and the creation of new firms had the characteristic of being substantial and the power to disrupt. These measures, if used alone, could represent many new technologies, not only those that disrupt. In the past

quantitative measures have not been used to define the parameters of disruptive technology. In

order to more specifically qualify an innovation as disruptive or destructive, this additional measure

of substantial has been included. Most would agree that a reduction of revenues to less than 50

percent of peak year sales would qualify as substantial. In a sense, the selection of a quantitative

threshold is a means of operationalizing Schumpeter’s (1939) claim that the entrepreneur’s

innovation, in order to creatively disrupt, must be substantial, not just new. If managers are to

better understand and more successfully respond to discontinuities in the market place, there must

be a distinction between these radical changes and other fluctuations. General economic trends,

shifts in costs of component parts or critical raw materials, and entry or exit of major competitors

can cause fluctuations in revenues. At times these fluctuations can be dramatic. These are not the

types of changes that constitute a disruption and the investigation of these would not enhance the

understanding of disruptive technologies. Therefore, to focus this research on innovations that cause discontinuities the 50 percent threshold was selected as a heuristic measure. In this case 50

percent was chosen as a starting point and could be modified pending the actual data. The use of

this measure was compared to the actual outcome in the industries studied; the results of this analysis are discussed in Chapter 4.

The quantification of the effect of the entrant technology on the sales of the incumbent is a

critical contribution of this research. The distinction and definition is important because many new products based upon new technologies are introduced every year. Understanding the difference between those that will disrupt and those that will simply challenge existing products is important.

Without this distinction, the parameters that actually separate disruptive events from those that are not are blurred. The tighter definition will enable the study and understanding of the threat of disruption to be clearer and therefore more helpful to practitioners and researchers. Using the definition of disruptive technology developed here, disruption can only be identified ex post, or after the fact. This is not helpful to strategists trying to anticipate and appropriately react to a potential disruption. However, until a solid understanding of what is and what is not disruptive technology is established, it does no good to try to anticipate it. Therefore, this definition was used and provided so that the boundaries of disruptive technology can be better understood. Subsequent studies need to be conducted to determine, ex ante, which new technologies are likely to displace or destroy existing ones.

When the propositions were originally developed, the dominant design was considered to be a stable, easily identifiable marker in the history of technologies. Abernathy (1978) and

Anderson and Tushman (1990) described a single product architecture that pervades the industry as the common means of building and producing the product class. It was anticipated that the dominant design could be used to mark the middle of the disruption range. As seen in the case of

mobile telephony, a single product architecture may not last throughout the period of disruption.

Initially, mobile telephone architecture was built around analog technology, but before the disruption ended a new dominant design, digital technology, took hold. Due to the evolving nature of dominant design, it was not used to calibrate the middle point in the upheaval of the market.

Fortunately, the disruption of industries like telecom was so well documented that the range

could be estimated and analyzed despite the lack of one significant, truly dominant design. Since

the goal of this research centered on defining and understanding the disruption of markets (not the

dominant designs thereof), this change from the proposed structure was not critical.

Companies that were in the marketplace offering the product or technology that was

threatened by the introduction of the new technology are referred to as incumbent firms. Such

companies, established for varying lengths of time, are those that built their businesses around the

technology that preceded the nascent technology. If a corporation offered the disrupted technology,

it is considered incumbent, even though it may have eventually introduced the new technology.

Firms that were not in the market until the introduction of the new technology or product are called entrant firms. These may have existed prior to introducing the new technology, but they did not manufacture, market, or sell the incumbent technology or existing goods to the existing customer base.

The term disruption range is introduced in this work. As explained previously, the disruption range is the time spanning the era of incremental change to the incumbent technology

and the era of ferment for the entrant. Grove (1996) and others have used the term inflection point

to describe a point in time at which the market shifts and changes directions in favor of the new

technology. Grove also described the difficulty of finding one point at which the change occurred.

Therefore, the term inflection point was rejected and the concept of a disruption range is used in its

place.

In the initial phase of the research, three industries were studied. Referred to as foundation industries, these were examined so that inferences could be drawn from actual examples of disruption and applied to the conceptual explanation of disruptive technology. The term foundation was chosen because it denotes a firm footing or basis for enduring existence. Therefore, by

studying three industries that suffered disruptions and about which broad and deep data were available, the research was established upon a secure base. The foundation industries enabled a full investigation of each variable in the propositions and each parameter of the operational definition developed herein. The inferences from the analysis of these parameters were then applied to the

concept of disruptive technology.

The Research Design

Figure 3 presents a model of the research conducted to test the propositions developed in

the preceding chapter. A review of this model reveals two primary stages in this research. The goal

of the first stage was to make inferences from examination of disrupted industries, using case

analyses, to the developing theory of disruptive technology. According to Eisenhardt (1989),

mistakes in information processing when analyzing case results can be avoided by the use of

categories, which are also called dimensions. Dimensions are utilized to organize the study of cases and look for understanding based upon them. The categories measured and analyzed in this part of

the research were industry density, industry intensity, and revenues.

The goal of the second stage of the research was twofold. First, the objective was to apply the inferences drawn from stage one to an additional case for validation of the findings. The second purpose was to determine if any of three variables—firm response, timing of response, or size— influenced performance in the course of a disruption.

Figure 3 Research Design

TELECOMMUNICATIONS MOTION PICTURES vs. T.V. MOTION PICTURES vs. HOME VIDEO Disruption-Range Data Disruption-Range Data Disruption-Range Data - population density - population density - population density 1 - population intensity - population intensity - population intensity - revenues - revenues - revenues

Within-Industry Analysis Within-Industry Analysis Within-Industry Analysis Theory se Inferences to

2 Across-Industry Comparative Analysis

DISRUPTION RANGE Interpretations Findings 3 Comparison to Extant Literature Conclusions

COMPUTER INDUSTRY Disruption-Range Data - population density - population intensity - revenues Within-Industry Analysis, Application/Validatio

Comparative Analysis for O f

Confirmation of Findings In Case

5 f erences

COMPUTER INDUSTRY

n n Strategic-Response Data - Firm Response - Firm Size - Timing of Response vs. Subsequent Firm Performance - ROA

Case Inferences to Theory Development

In order to accomplish the goal of the first stage of the research, density, intensity, and revenue data for each foundation industry were collected and studied (see step 1, Figure 3).

Dimensions with continuous measurement scales allow for graphing of the findings. Each of the three dimensions assessed in this research were continuous, and therefore a graphic representation

of each was created for all industries. This enabled a visual inspection of the trends in each industry, resulting in an estimate of what happened to every dimension with the advent of a new technology.

The trends estimated by the visual inspection of the graphs were then tested quantitatively.

To augment the visual representation of the time series data, the numerical analysis technique of finite differences was employed. Kellison (1975) recommended the use of this method when the factors studied are finite, as in number of rivals and revenue figures, rather than infinitesimally small. Therefore, the first and second differences were calculated to determine the change in the numbers from year to year and the acceleration or deceleration over time. Averages of the differences were calculated for periods where there appeared to be a general trend in the graphed line. These differences and averages were then compared to determine if they supported the visual impression made by the graphs of the dimensions studied. When these two methods of analysis converged, the finding was considered conclusive. In other words, when a trend could be verified both visually and quantitatively, it was considered to be an actual occurrence in the given industry.

The final step in the within-industry analysis was to create a summary graph of each industry. The graphs, referred to here forward as stylized summaries, are the qualitative equivalent of a regression line. They represent the general tendency of the data even though they may not show the data with strict accuracy (Koremenos, Lipson, and Snidal, 2001). The method of developing these stylized summaries is explained in detail later in this chapter.

The stylized summaries made possible the across-industry comparison of the findings (step

2, Figure 3). The goal of this part of the research was to determine if there were common patterns or essential differences among the cases. A forced pair comparison (Eisenhardt, 1989) was conducted. The results of each industry were compared to the outcome of the other two markets.

This comparison revealed the similarities and differences between the disrupted industries from which inferences about the disruption ranger were made.

The outcome of completing steps (1) and (2) of the research was a detailed description of the disruption range (step 3, Figure 3). This description was then tested against the extant literature

that was reviewed in Chapter 2. This iteration allowed for the validation of the findings against the

existing body of research on the subject, adding further clarity and precision to the understanding of

the phenomenon.

Application and Validation of Case Inferences

As stated, the second phase of the research involved comparing the conclusions from the

foundation industries against another disrupted market—the computer industry during the

introduction of personal computers (PCs) (step 4, Figure 3). This forced comparison was used as a

method of testing the portrayal of the disruption range produced by the first three steps of the

research. Comparing the developed description to a different market allowed the conclusions drawn from the first three steps of the research to be confirmed or denied. This follows Eisenhardt’s

(1989) initiative of utilizing at least four cases and grouping them for comparison.

The computer industry was also used to test Propositions 7, 8, and 9. The subject of these propositions was the strategic response of the firms within the industry and the subsequent performance of those companies (step 5, Figure 3). Correlation and regression analysis were used

to examine these relationships. The statistical methodology is explained in detail later in this chapter.

Selection of Industries

Foundation industries, as explained earlier, are those upon which the initial study of the disruption range was conducted. These were used to calibrate and gain a more precise

understanding of the time during which an existing market is disturbed by a new technology.

Industries were selected if a new technology had been introduced that caused substantial change

within the market, and multiple sources of information regarding the industry were available. The telecom industry in the United States during the years 1985 to 2000 and the motion picture industry from 1940 to 1970 and from 1980 to 2000 satisfied the criteria and thus were chosen as foundation industries.

Initially, it was determined that a clear disruption of the telecom industry took place based

upon the observed phenomena (CTIA, 2003; Largent, 2005) of wireless telephony redirecting the

demand for telephone communication from wireline to wireless. The subsequent radical change in

the competitive makeup and economics of the telecommunications industry reinforced the selection.

The need for traditional wireline phone companies to change their offerings and approach to the market was apparent as the demand for wireless phones increased. The study of this shift was intended to allow a clearer, more precise understanding of the changes transpiring as the displacement of the incumbent technology occurred. The information available on the telecom industry was abundant, owing to the broad and deep data set maintained by the U.S. Federal

Communications Commission. Additional sources of information in the form of industry experts

and published reports were also available as the disruption is a significant and current phenomenon.

The motion picture industry was chosen as a foundation market that is somewhat unique in

that it actually sustained two disruptions, both of which are analyzed here. At the dawn of the

television age, in the late 1940s, movie producers, distributors, and cinema theatre owners feared

that the demand for their offerings would be eviscerated. In fact, there was a major demand shift toward the new and remarkable technology known as the television set. Just a decade after the movie industry regained equilibrium, the introduction of the videocassette recorder (VCR) presented a new challenge to the industry.

The Motion Picture Association of America (MPAA) makes available a data set that provides a long history of the supply and demand for movies in the U.S. since the 1950s. Although industry experts from the 1950s and 1960s are not readily accessible today for consultation, much archival evidence in the form of news and industry reports was identified and employed here to enhance validity. The more recent disturbance of the film industry, by VCRs, was well documented in the MPAA and National Association of Theatre Owner (NATO) data. In addition, the recency of

this upheaval enabled contact with industry experts and access to many published reports that enhanced the quality of the case analysis.

The computer industry from 1978-1990 also fit the criteria of the presence of a well- documented disruption and the availability of multiple sources of information. It was used to test the findings in the first part of this dissertation regarding the description of disruption range and to

analyze the issues raised in Propositions 7, 8, and 9 pertaining to firms’ performance following

disruption.

The computer industry was attractive for two additional reasons. First, during the time

frame studied, it was nearly completely confined to the United States (Norris, 1983). The threat of

foreign entrants was real, but for the most part competitors in this market, both incumbents and

entrants, were U.S. based. This delimitation allowed for thorough study of the industry participants

without the obstacle of accessing historic data from foreign-based firms. Second, the majority of

the firms in the industry from 1980 to 1990 were not diversified, thus simplifying the collection of

business-level information (Banker, Chang, and Majumdar, 1996). Therefore, the study of each

firm’s actions and performance was relative to the computer industry. The absence of

diversification circumvented the need to uncouple consolidated financials and alleviated the

potential for inappropriate interpretation of the numbers. The financial data, the annual reports, and

published news stories about the companies were about their computer business. This single focus on computers and only computers made the industry desirable to study.

Understanding the Disruption Range

Sources of Evidence and Data Collection Methods

To feel confident about setting boundaries for a phenomenon, the case approach must be

pursued carefully and methodically. Yin and others (Snow and Thomas, 1994; Eisenhardt, 1989)

recommend using evidence from two or more sources and multiple types of sources, such as

interviews, archival references, and databases, to improve construct validity. (Construct validity is

achieved if there is a correspondence between the indicators employed in the analysis and the

concept they are intended to represent. This test is addressed in more detail in the final section of

this chapter.) The sources consulted for this research were categorized as interviews, databases, and

archival references. Some of these, such as interviews and annual reports, were internal to firms. In

interviews, employees provided subjective data rich in nuance and detail. Archival references like

annual reports were internal and objective, and were used to balance the subjectivity of the personal

employee accounts. Databases created and maintained by industry regulators and research firms

were external to the companies, involved primarily objective data, and controlled for the possible

bias of the subjective input from interviews and internal documents. The diversity of sources

provided the fullness of the highly realistic data acquired through internal sources and interviews,

while external, objective database information enabled a greater degree of control.

Appendix A includes spreadsheets that serve as a repository for the information gathered in

the study of the disruption range. Miles and Huberman (1984) recommended the use of a

spreadsheet as this format permits one to collapse copious data into a systematic format. Moreover,

there is an accounting of where the information was found. This is one piece in the chain of

evidence that allows others to analyze and understand the logic used to move from the research propositions through the collection and analysis to the conclusions drawn from the research. This ensures that other researchers can follow and reproduce the study efficiently (Yin, 1994). Notes taken during the investigation were categorized in the spreadsheets according to their industry, relative proposition, their source, and the specifics of the finding. This chain of evidence is one of the tactics recommended by Yin to ensure construct validity.

Databases. Databases in this study refer to tables of numbers and figures that have been recorded and relate to a particular industry or company. Dun and Bradstreet, the business information clearinghouse for over 160 years, conducts an annual survey of more than 8,000 businesses. Data are organized in the annual Dun’s Business Rankings (hereafter referred to as

Dun’s). These books rank public and private companies according to four-digit Standard Industrial

Classification (SIC), sales volume, and employee size. The data collected by Dun’s are deemed extremely reliable and accurate based upon the reputation Dun and Bradstreet has earned through time due to sound collection and recording methods.

For most of the foundation industries there is a unique four-digit SIC∗ that describes the incumbent category and a separate SIC for the entrant or nascent category of business. Dun’s ranking of firms within each SIC was the source of information used to examine the foundation industries and the computer market relative to the first five propositions. In the case of television

(TV) and motion pictures, Dun’s was not available, as the disruption preceded Dun’s initial publications. In this case, the Federal Communication Commission (FCC) data of the same quality and consistency were used.

Appendix B contains the data collected from Dun’s for each SIC. The relevant SICs and the descriptions are listed in Table 1.

∗ Because of the historical nature of the cases, SIC, the predecessor to the current NAICS, was used.

Table 1 Industries Studied/SICs Industry #1 – Wireline Phones vs. Wireless Phones (1990-2000) SIC Description Incumbent 4813 Telephone Communication Except Radio Phones Entrant 4812 Radio Telephone Communication (Includes cell phones) Industry #2 – Motion Picture Industry vs. Television (1940-1970) SIC Description Incumbent NA Entrant NA NA, Not Applicable as FCC data used, not listed by SIC Industry #2 – Motion Picture Industry vs. Home Video Industry (1980-2000) SIC Description Incumbent 7813 (changed to 7812, 1990) Motion Picture Production (not TV) Entrant 7841 Videotape Rentals

The Dun’s data are organized by year and company. For each year, the companies listed in

Dun’s and their corresponding sales figures were recorded. Subsequently, the number of the firms participating in the industry each year was calculated and recorded as a measure of industry density.

Appendix B contains the source data used for the analysis of each of the foundation industries. These data were used to estimate the density or number of firms competing in each category over time. Where firm-level data were available (all industries except the early days of

TV), market share was calculated based upon the individual sales figures of each company divided by the total sales for the SIC for the year. This was used to estimate the intensity within the industry over time. Intensity refers to the number of firms needed to constitute 75 percent market share. The more firms needed to reach 75 percent market share, the lower the intensity. Source

data were also used to chart longitudinal changes in revenue and in the calculation of the finite differences and averages for the detailed study of the trends in revenues.

Revenues were adjusted for inflation using the Consumer Price Index (CPI) from the

Bureau of Labor Statistics. The base year CPI was divided by the data year CPI and multiplied by the data year revenues, thus producing a revenue figure adjusted for inflation.

Where possible, other quantitative sources of information were utilized to validate and add detail to the description and understanding of each industry. The Federal Communications

Commission (FCC) has, during the entire history of telecommunications in the U.S., required telecom providers to report a breadth and depth of information. These data have been recorded and are available for pub lic use. A variety of reports were accessed and utilized throughout this dissertation. The MPAA and NATO have large databases of information regarding American moviegoing habits, admission prices, and availability of film productions, which were acquired for this research. These data and sources are included in Appendix B.

As Eisenhardt (1989) noted, the importance of case study analysis is that the researcher become intimately familiar with each case. This enables the researcher to recognize the unique patterns within the case prior to conducting the across-case comparison in search of patterns that may be generalizable to the phenomenon being studied. With the goal of within-case pattern recognition in mind, the density, intensity, and sales tables were used to analyze the disruption range for each industry. The longitudinal study of these variables was used to construct a summary graph of each market. These charts were then used to conduct an across-industry analysis to determine if any common patterns exist.

Interviews. Industry experts were interviewed and their input was used to verify the understanding of the researcher, and to add accuracy, depth, and nuance not obtained from the analysis of numeric data. The value of interviews is diminished if they are used in the absence of any other data because of the potential for interviewer and interviewee bias. This problem was avoided in the present study by the inclusion of interviews, archival references, and databases. The

triangulation, or use of three types of information, facilitated depth of understanding and insight,

while reducing the potential for bias.

Interviewer bias was managed by carefully constructing interview guides (see Appendix

C), which helped to control the discussion and keep it focused. While this method helped to guide the dialogue, there was also enough flexibility built in to permit the interview candidates to augment the material being explored. Because the interviews revolved around events taking place in the industry, rather than decisionmaking by any individuals within firms, the threat of bias was mitigated. The accuracy of statements made in the interview could readily be validated against the data set and archival references. This was another means of reducing bias.

Experts on the telecom industry were more accessible than the other foundation industries.

Therefore three individuals who are involved in telecom were interviewed. Two experts had for many years worked on the wireless side of the business. One started his career with McCaw

Communications, which was eventually purchased by AT&T and used to launch AT&T Wireless.

At the time of the interview he was a regional manager for AT&T wireless. The other wireless expert also worked with two cellular providers as a result of an acquisition. She began her career in telecom with GTE Wireless Communications, which was subsequently acquired by Alltel,

Corporation. Her experience as a district market manager was the basis of her expertise. Finally, a veteran of the wireline industry, the Vice President – Public Affairs for AT&T was interviewed.

Each was asked to confirm the interviewer’s understanding of the emergence of cellular in the

United States and its impact on wireline phones. Each assisted the interviewer in interpreting the industry jargon and record keeping.

In the movie industry, the Vice President/Executive Director of the trade organization

NATO was interviewed. Her views on the effects of TV on moviegoing were strictly historical, as she was not affiliated with the industry at the time of the disruption. Her perspective on the movie

rental industry and its impact on moviegoing were used to augment the researchers understanding of this case.

Archival References. Archival references in this study refer to historical accounts, published stories and reports relative to a particular industry or company. They are especially valuable because of the consistency they offer. Specifically, archival references provide a source that is published at an earlier time and therefore is not altered by the passing of time. They do not suffer from potential inaccuracies in memory or retrospective bias. They also add value through their stability, enabling future researchers to review and reference them (Yin, 1994).

The limits of archival references in a longitudinal study are that they can be difficult to retrieve or access can be blocked. The more distant the time period studied, the more challenging it

became to retrieve the data. This was a challenge relative to the disruption of motion pictures by

TV in the 1950s and 1960s because the disruption took place nearly a half-century before the study

was conducted. However, the event was so pivotal in the evolution of entertainment in the U.S. that

significant reports and books, some by individuals involved in the industry at the time of the first

disruption, were available and used. The proprietary nature of some archival data presented another

challenge. Information such as production studio market shares, that would have proved valuable to

this work, also proved impossible to access. This omission was overcome by more extensive use of

other sources of data.

The numerical analysis technique applied in this study is the calculation of finite

differences. Because the intervals in the data are finite rather than infinitesimally small, the

mathematics of “finite differences” was used rather than calculus (Kellison, 1975). Throughout the analysis of each industry, an array called a difference table was used to organize the finite difference data. The array includes the calculation of first and second differences as well as averages over specified date ranges to aid in the interpretation of trends in the data.

Understanding Strategic Response and Firm Performance

Once the description of the disruption range was established and confirmed, the response of firms competing in the computer industry during the PC disruption of mainframes was studied. As

exhibited in Step 5 of Figure 3, this was done by comparing three independent variables—size,

response, and timing of response—to the dependent variable Return on Assets. Garg, Walters, and

Priem (2003) recommended the use of ROA as a legitimate measure of firm performance. In this

study ROA was calculated as the Average Return on Average Assets for the five-year period

following the disruption range. The goal of this comparison was to determine how much of the firm’s performance (ROA) following the disruption range could be explained by the three

independent variables. The table in Appendix E lists the sources of information for each of the last

three propositions. The following section elaborates on the sources of data and the methods of

collecting, recording, and analyzing the information on the computer industry.

Sources of Data and Data Collection Method

Databases. To determine how firms reacted to the disturbance of the marketplace, Dun’s

Business Rankings was employed to identify the firms involved in the computer industry. The changes in industry participants as well as the shifts in revenues and market shares were used to better understand the market disruption of mainframe computers by personal computers.

Dun’s was used to identify the top 30 rivals in 1982, which is near the beginning of the disruption and again toward the end of the disruption range in 1990. The method of using rivals competing during the peak of the disruption and at the end allows for inclusion of firms that might have started out to be significant competitors and those that came on late. These sets were combined; all publicly traded firms that were in the combined set and those for which information was available were included in the analysis. In addition, the largest public firms for which

information was available from each year between 1983 and 1989 were included even if they were not in the combined set.

The average return on average assets for each firm from 1990-1995 was collected from

Standard & Poor’s Compustat Services, Research Insights.

Archival References. Annual reports were used to gauge the size of the firms in the year

that they responded with the introduction of the PC. Size was based upon the sales for that year.

Firms not introducing a PC were qualified as No Response. In the disruption of the computer

industry, 1982 was considered a pivotal year. It was the first full year of sales for IBM PCs (IBM

annual report, 1982) and the first full year that the rest of the industry reacted to Big Blue’s lead.

Because of the importance of this year, 1982 sales were used as the measure of size for those firms

that did not introduce PCs. Subsequently, Compustat data were used as the source for the average

return on average assets from 1990 to 1995. These data are included in the Appendix G. The database was constructed so that the information can be sorted and analyzed by five different categories: firm, response, size, year of entry, and ROA.

In the proposal, several categories of strategic response were identified. They were

entitled:

• No reaction

• Intrapraneurship

• Merge/Acquire

• Establish independent strategic business unit (SBU)

After conducting the research, it was determined that a fifth category, joint venture, should be added. In addition, the research revealed that the more common terminology for internal development of the new products, or so-called “organic growth,” was Research and Development

(R&D) rather than Intrapreneurship. Therefore, the five categories of strategic response were:

• No reaction

• R&D

• Merge/Acquire (M/A)

• SBU

• Joint Venture (JV)

A company was categorized as No Response if, during the time of the disruption (1982-

1990), the firm did not develop, manufacture, market, sell, or introduce a PC. R&D indicates that the firm, through internal R&D, developed, produced, marketed, and sold a PC or line of PCs sometime during the disruption range. M/A designates that the firm acquired another firm or PC technology during the disruption range. To qualify as M/A, the acquisition target had to possess a

PC offering. This product line had to be stipulated as one of the reasons that the acquiring firm made the purchase. A firm’s response was qualified as SBU if there was evidence that the company had established a unit financially, geographically, and managerially separate from the primary operation with the explicit mission to offer PCs. When a firm joined with another company without taking controlling interest in the other company and with the intent of developing and/or selling a

PC, the firm was qualified as a JV. In one set of analyses JV and M/A were grouped as one external response to determine if any more significant relationship might exist with a reduced number of categories for the response variable. Analysis revealed no benefit from this alteration so it was not pursued.

Lexis Nexis, Internet search engines, and Business Source Premier were then searched for published materials involving the identified firms during the disruption range. Subsequently, articles and books were studied for evidence of the actions taken by each competitor.

Announcements of the introduction of a PC by a particular firm were found in published data. Once

the year of the introduction was established and recorded, the means by which the company came upon the new product were sought.

All of the response a nd year of introduction data were recorded in Appendix G. Once the

estimation of the year of introduction of the PC and the method of so doing was identified, annual

reports were used to verify th e accuracy of the findings. In a few instances, the archival data allowed for the identification of a two-year window during which a firm introduced a PC. In these cases the annual report was consulted and used as the final date. The annual report was also the source of the sales figure used to qualify the size of the firm. This number was entered in the database.

The plan to build a regression model to determine the relationship between the three

independent variables (size, response, and timing) and the dependent variable, ROA, necessitated a

sample size of at least 14. This sample size was based upon Troutt’s (2005) explanation of the 10k

rule. He noted that it is reasonable to limit the degrees of freedom to ten and utilize the rule n ≥ 10

+ k + 1, where k represents the number of independent variables tested. The application of this directive in this case resulted in a minimum sample size (n) of 14. Sufficient information on the one dependent and three independent variables was available regarding 20 firms, and so a sample of 20 was utilized.

The statistical analysis package, SPSS Version 10.1, was used to check for correlation

between variables and to build a linear regression. A Pearson correlation test was conducted to test

for an association between each independent variable and the dependent variable. This method was

also used to determine the relationship between each of the independent variables.

Regression analysis was conducted using the data from the computer industry to determine

if any of the independent variables, response, timing, or size, correlated with future firm

performance (ROA). If such a relationship was found, the understanding of the influence of the

particular independent variable(s) on the ROA could be helpful for firms involved in disruptions.

Although the sample size was adequate for the parametric assumptions, two non-parametric

correlations (Kendall’s tau and Spearman’s rho) were also conducted. One significant correlation was discovered using these two tests. The detailed results of each of these analyses are discussed in the following chapter.

Reliability and Validity of the Findings

To judge the quality of this research design, three tests were applied: construct validity,

external validity, and reliability (Yin, 1994; Kerlinger, 1986). Construct validity is the test to

determine if the methods used in the study are a legitimate means of examining the subject at hand.

In other words, construct validity is achieved if there is a correspondence between the indicators employed in the research and the concept they are intended to represent. External validity is achieved if the inferences made can legitimately be generalized to a large, external population. And reliability implies that other researchers repeating the study would reach the same conclusions if they were to employ the same methodology. This section examines how the current work has met those tests.

The concept under examination in this work is that of disruptive technologies, a

phenomenon that exists in markets or industries. Construct validity challenges the researcher to test the appropriateness of the work as an accurate means of measuring and better understanding the concept. To achieve construct validity in this case, the researcher must prove that the methods used to measure market disruptions truly measure that concept and not something else.

The research design included two tactics to ensure construct validity was achieved: a specific definition of the phenomenon of disruptive technology that is both lexical and operational

(see p. 37) and data triangulation. The specific definition of disruptive technology developed earlier

in this chapter enabled the researcher to distinguish between markets that have truly been disrupted and those that have not. All products and industries progress through stages that constitute a life cycle (Patton, 1959; Wasson, 1974). The product life cycle (PLC) refers to the introduction and development of a new product category with the eventual emergence of a dominant design followed

by the incremental or generational improvement of that design, leading to maturity and eventually decline. Ostensibly, the cycle is managed by businesses to keep their product lines as healthy and productive as possible for as long as possible in the face of other products of the same product category or class (Day, 1981). This is without regard for or threat from the introduction of a new, disruptive technology. In order to focus the research conducted here on disruptive technologies and to avoid measuring elements of PLC, a definition of the disruptive phenomenon that is more exact and operational than those used previously was developed. By defining the disruption as pertaining to a nascent product category that surpassed the revenues of an existing product category and reduced sales of the incumbent technology to 50 percent of the previous peak, a distinction between

PLC and disruption was drawn. The PLC literature reveals (Patton, 1959; Wasson, 1974; Day,

1981) that a precipitous drop in revenues like the one described in the disruptive technologies definition, would not be typical of products progressing through a normal life cycle. By defining a strict difference between the two phenomena, construct validity was enhanced.

Many products—such as automobiles, television sets, and excavators—existed for decades without threat of disruption and only management of the PLC as an issue for executives. In terms of the technology cycle (see Figure 1 in Chapter 2), this means managing the products along one curve, without concern for or threat from the development of a second curve that could serve to disrupt. Disruption occurs if a new technology, a second curve on the technology cycle, is introduced and eventually displaces or even causes the existing product to become obsolete. Thus

the operational definition of disruptions employed in this research resulted in metrics that are analogous to disruptive technology but would not be valid for the study of the PLC.

The second means of ensuring construct validity employed was to use multiple sources of

data, or data triangulation. As Yin (1994) recommended, wherever sufficient sources were

available, triangulation was accomplished. Because case analysis involves one or a few cases, the

results cannot be validated statistically. Therefore, information is gathered from a variety of sources

and analysis is conducted to determine if the output produced from each converges. If there is

agreement among the sources, validity is implied. In this research, a database or set of quantitative

measures was utilized to examine each industry during the years that the market was disrupted.

Multiple databases from several reliable sources were often used to validate the data obtained. In

addition, interviews with industry experts were conducted and the information gathered was

recorded. The final source of data was archival references. The output from the analysis of each

source was compared for similarity. Where contradictions were identified further research was

conducted to confirm the correct interpretation.

In terms of external validity, the findings of this research are generalizable to the

phenomenon of disruptive technologies only. The limitations created by the small number of cases

analyzed prevent the conclusions from being statistically generalized and applied to a large, external

population. The intent is not to interpret the results as representative of all industries or every disruption, but to generalize to the concept of disruption to supplement the building of a theory initiated by other researchers. External validity in case studies is accomplished by testing the findings in additional cases (Yin, 1994). In this research, replication and validation were accomplished by applying the findings from the foundation industries to the computer industry.

Reliability, the concern that the results of the study would be repeated if other researchers

used the same procedures and methods, is an important test. Many steps were taken during this

research to insure reliability. As described in the preceding sections of this chapter, the sources of all data and a record of findings were provided in the data tables that were built and are available in

the Appendices. Also, the interview guides are supplied. In addition, the database information supplied by the FCC and MPAA are publicly available, as are all of the archival data, further enhancing the reliability of the design and research. The process of developing the graphs and charts used to evaluate the cases is explicit in the following chapter. The detailed explanation of every step throughout the research process ensures the possibility of replication.

CHAPTER 4

ANALYSIS AND FINDINGS

This chapter provides details of the analyses that were conducted and it progresses in the order provided by the research design. There are two main sections. The first delineates the analysis and findings relative to the foundation industries and the disruption range. The second explains the process and outcomes pertaining to the computer industry, the validation of the disruption range findings, and the testing of the strategic response propositions.

Telecom Industry Analysis (Propositions 1-6)

The beginning of the telecom disruption was marked by the FCC’s issuing of cellular

licenses in 1982 (Berresford, 1989). The dominant design in cellular telephony at that time was

analog technology. However, a new dominant design evolved with the introduction of digital in

1996. In the following analysis of telecom during the period 1982 to 2000, changes in the variables

indicate a turning point around 1991. There is much fluctuation in density prior to this year and

little after. Conversely, there is little movement of the intensity, incumbent revenue and entrant

revenue data prior to this year, and they become more dynamic after 1991.

Density (Propositions 1 and 2)

P1: The onset of the disruption range can be identified initially by a marked increase in population density (the number of rivals competing for the customer base).

P2: The ending of the disruption range can be identified by a sharp decrease in the population density of the market.

In the early years of the disruption range there was an increase in the number of telecommunications providers. According to Dun’s, the number of incumbent telephone firms

(non-cellular) rose from 51 to 63 between 1982 and 1985, then peaked at 78 companies in 1988.

The number of mobile phone providers was not available until 1990, as this was the first year that a unique SIC was created for mobile phones. In that year Dun’s listed only two companies in SIC 4812 (Radio Telephone Communications). The number of providers remained relatively constant, between one and three firms, for the first six years before increasing to five carriers in

1997.

Figure 4 reflects the total number of firms offering telephone service (SIC 4813 Telephone communications, not radio, and 4812 Radio Telephone Communications, combined) from 1985 to

1999. The shape of the curve generally supports both Proposition 1 and Proposition 2, there was an initial increase in density followed by a decline. The increase after the 1982 licensing of mobile phone providers is evident. There was a peak in the number of competitors in 1988, perhaps

signifying a midpoint prior to four years of decline in the number of rivals. The stability of the

number of rivals after 1992 could be considered the demarcation signaling the end of the disruption.

Figure 4 Total Number of Telecom Firms (Wireline and Cellular)

90 80 70 60 50 40 Count 30 20 10 0

1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999

ISIC 4812/4813 Source: Dun's Business Rankings

Therefore, the first two propositions were supported by the Telecommunications industry during the 1980s and 1990s. Data from this industry showed changes in the number of rivals over time with a marked increase in the number of competitors initially, followed by gradual decline and then a return to stability.

Intensity (Proposition 3)

Proposition 3 relates to population intensity, or the number Number of Rivals Needed to Constitute of firms needed to constitute 75 percent of the market share. Year 75% Market Share 1985 1 Specifically, it suggests that the intensity will be greater in 1986 1 1987 1 the beginning of the disruption range than it is later; i.e. 1988 1 fewer firms will be needed to constitute the 75 percent 1989 2 1990 2 market share initially. The implication here is that a small 1991 2 1992 3 number of firms controlling 75 percent market share 1993 3 indicates a concentration of power and an intensity of 1994 3 1995 3 competition. Therefore, a small number of competitors 1996 3 indicates high intensity and vice versa. For comparison, 1997 4 1998 3 between 77 and 34 firms shared the remaining 25 percent 1999 3 2000 3 during those years. 2001 4

2002 3

FCC, TSS 2004 9-1 and 9-7 Table 2 Telecom Industry: Intensity

Proposition 3 estimated that intensity would be greater in the early part of the range and

decrease over time. In telecom this proposition was weakly supported. AT&T’s grip on the market

was complete until 1988. From 1989 until 1991 AT&T shared control of the market with one other

competitor. Three rivals held a 75 percent share from 1992 through 1996, and subsequently intensity fluctuated between three and four firms (see Table 2).

Revenues (Propositions 4 and 5)

P4: An early and late phase of the disruption range is marked by significant decline in sales for incumbent technologies.

P5: Revenues for the nascent technology increase gradually in the early phase and the rate of growth increases in the later phase.

To control for the effects of increases in population on the demand for telephony, the

adjusted revenues in this industry were divided by the U.S. Census population estimates to

determine the revenues per capita for the incumbent and nascent technologies. Figure 5 and Table

3 exhibit the ch anges in per capita telecom revenues (adjusted for inflation) over time. The sales figures did not s upport Propo sition 4 that incumbent sales will significantly decline throughout the disruption. While wireline sales remained relatively flat from 1985 through 1991, they increased throughout the following decade even when adjusted for inflation and population growth. In fact, calculation of th e average fir st and second difference over the entire time (1985-2000) reveals slight growth (2.14) a t a decreasing rate (-3.63).

The incumbent reven ues per capita exhibited two landmarks where changes in the slope were evident. T he visual ins pection revealed the first noticeable change in 1991 and the second in

1998. These changes are quantified in Table 3 through the calculation of the first and second differences. From 1985 to 1991, wireline revenues fluctuated year to year, but the overall trend was flat to slightly declining. This is apparent from a visual inspection of the graph and supported by the calculation of the first and second difference (averaging 1.29 and -.41, respectively). From 1991 to 1998 revenues increased (average of first difference 13.07) at a decreasing rate (average of second difference -4.58). In the final years of the study, incumbent revenues declined (-19.39) at a decreasing rate (-16.82).

Figure 5 Per Capita Telecom Revenues (Adjusted for Inflation)

450.00 250.00 400.00 350.00 200.00 300.00 Wireline (000) 150.00 250.00 Revenues/Capita 200.00 100.00 Wireless (000) Wireline 150.00 Wireless Revenues/Capita 100.00 50.00 50.00 0.00 0.00 1985 1988 1991 1994 1997 2000 FCC Data

Table 3 Per Capita Telecom Revenues (Adjusted for Inflation) Wireline Wireless Wireline Wireline Wireless Wireless (000) (000) (000) Difference Difference Year Population Revenues/ Revenues/ 1st 2nd 1st 2nd Capita Capita 1985 237924 299.57 2.49 5.26 -17.38 2.07 -0.47 1986 240133 304.83 4.56 -12.12 14.77 1.60 1.94 1987 242289 292.71 6.16 2.65 2.86 3.53 1.35 1988 244499 295.35 9.69 5.51 -18.89 4.89 2.94 1989 246819 300.86 14.58 -13.38 11.10 7.82 -3.64 1990 249464 287.48 22.40 -2.29 25.70 4.19 6.36 1991 252153 285.19 26.59 23.41 -21.02 10.55 1.47 1992 255030 308.60 37.14 2.39 15.20 12.02 5.02 1993 257783 310.99 49.16 17.59 -2.79 17.04 0.43 1994 260327 328.58 66.20 14.80 17.47 17.47 10.83 1995 262803 343.38 83.67 32.27 -33.01 28.30 -2.01 1996 265229 375.65 111.96 -0.74 17.85 26.29 -13.31 1997 267784 374.92 138.25 17.11 -19.42 12.98 34.38 1998 270248 392.03 151.24 -2.31 -10.95 47.36 -11.05 1999 272690 389.71 198.60 -13.26 -29.33 36.31 -5.44 2000 282192 376.45 234.91 -42.59 -10.18 30.87 -17.20

Series 2.14 -3.63 16.46 0.72 Average: Wireline Wireless Average Average Range 1st Diff 2nd Diff 1st Diff 2nd Diff 1985-2000 2.14 -3.63 16.46 0.72 1985-1991 1.29 -0.41 4.95 1.42 1991-1998 13.07 -4.58 21.50 3.22 1998-2000 -19.39 -16.82 38.18 -11.23

Figure 6 Comparison of Change in CPI and CPI Telephone Services

15%

10%

-5%

-10%

Percent Change from Prior Year -15% 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002

CPI - All Items CPI - Telephone Services FCC Table 12.2

One explanation for increasing revenues in the face of the new competitor is that after 1988 the consumer price index consistently outpaced the consumer pho ne price index . FCC data exhibited in Figure 6 show that until 1 988 phone pr ices rose at ab o ut the same rate as the CPI, but the consumer phone index fell behind the CPI in every year therea fter. Compar ed to the infl ation in prices of o ther goods, tal king on the ph one became c heaper during this time.

Figure 7 shows a steady incre ase in the num ber of minute s of phone us age nationally .

Competiti ve pressure is a common inst igator of decreasing prices. This data ind icate that wh en cellular w as introduced, wireline provi ders reacted to the increased competition by lowering the price of la ndline, long-di stance minute s. The price re duction may have been the motivator of more phone usage. The increase in usage in turn explains the increasing revenues of the incumbent in the face of new competition.

Figure 7 Interstate Switched Access Minutes

600.0

500.0

400.0

300.0

200.0

100.0

0.0 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003

FCC: Chart 10-1 When considering the data relative to Proposition 5 (revenues for the nascent technology increase gradually in the early phase and the rate of growth increases in the alter phase), there was support for a change in the nascent technology revenues during the disruption range. The graph in

Figure 5 reveals 1991 to be a landmark year in wireless revenues as it was in wireline. Table 3

exhibits the first and second difference in the annual per capita revenues for wireless. The average first and second differences over the entire disruption range were 16.46 and .72 respectively, indicating strong growth with a small amount of acceleration. The average first and second differences for 1985 through 1991 were 4.95 and 1.42, respectively. Figure 5 shows a change in the curve for entrant revenues in 1998 as well. The pattern of growth that began prior to 1991 continued, but was more pronounced from 1991 to 1998 (average first difference 21.50, average

second difference 3.22). Growth from 1998 to 2000 was considerable but at a decreasing rate

(38.18, -11.23). Therefore, what can be witnessed visibly in Figure 5 can also be quantified using the calculation of the first and second differences. Thus, Proposition 5 was supported by the data in

the Telecommunications Industry. Two phases of revenue growth were detected based upon revenues and growth accelerated in the latter phase.

Summary (Proposition 6 and Definition)

Proposition 6 states that, based upon the findings in Propositions 1-5, two phases of the

disruption range can be identified: Phase 1 (the early phase) and Phase 2 (the late phase). As

Eisenhardt (1989) recommended, case analysis must reduce large volumes of qualitative

documentation from a variety of sources to a series of final conclusions. Figure 8 presents a stylized summary of the data simplified to standardized terms. In the telecom case the simplification needed to standardize the graphs of the four variables was to multiply the count and

intensity data by ten. This allowed the scale of the count to be similar enough to the other variables for the trends to be compared on a common graph. This summary is intended to portray a general tendency conveyed by the data rather than the strictly accurate details of the case (Koremenos,

Lipson, and Snidal, 2001).

Figure 8 Stylized Summary: Telecom

450

400

350

300

250

200

15 0

10 0

0 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000

Combined Count Industry Intensity Wireline Revenues/Capita Wireless Revenues/Capita

In telecom prior to 1991, the density of the market fluctuated considerably while the other three variables remained more constant. After 1991, density became more static and the other

variables, intensity, incumbent revenues, and entrant revenues experienced substantial increases. It

is therefore reasonable to estimate that two phases of a disruption took place: one before 1991 and

one after.

When data from the telecom case were compared to the operational definition of

technological disruption, the case proved to be a poor example of this phenomenon. The definition

states that entrant sales would surpass the incumbent. This was eventually true of cellular sales

versus wireline. Although it is not included in the figures provided here, investigation into the sales trends beyond 2000 revealed that wireless sales surpassed wireline for the first time in 2004.

However, the definition also states that the incumbent sales would be reduced to 50 percent of its previous peak year sales. This was not the case for the incumbent industry, wireline. The peak year sales, $106 billion in 1999, were not reduced to 50 percent of that amount as the industry returned to equilibrium. In 2004, 22 years after the licensing of cellular firms began, wireline sales were nearly $81 billion or 76 percent of the peak year sales. By 2000, the density and intensity of the industry had become relatively stable. Incumbent revenues continued to decline while entrant revenues increased at a decreasing rate. If this trend continues, telephony could eventually satisfy the stringent definition of disruption employed here.

Motion Pictures vs. TV Industry Analysis (Propositions 1-6)

The following is a detailed description of the analysis of the disruption of the motion picture industry by TV. Because the disruption began over 50 years before this research was undertaken, limited information was available and only three of the four variables outlined in the propositions could be measured. Utilizing these three, the analysis revealed that in approximately

1958 there was a milestone in this case, followed by a second turning point in 1961. Entrant revenues exceeded incumbent revenues for the first time in 1961 and the industry density, which had been increasing, became more stable thereafter.

Count and Intensity (Propositions 1, 2 and 3)

Dun’s could not be used for the analysis of this industry as Dun & Bradstreet did not begin collection of data until much after TV was introduced. The FCC provided a count of the number of licensed TV broadcasters, which was used to partially respond to Propositions 1 and 2. This was not a complete picture because, although the FCC kept data from the 1940s on the movie theatre business, data were inconsistently tabulated with different recording methods being used in different

years, eliminating the value of the data for longitudinal study. The Motion Picture Association

tracked box office gross revenues, number of motion picture screens, and number of movies

released, but not the number of movie production companies. Therefore, there is no count of the

number of motion picture production firms, the incumbent industry.

Although 1939, in many respects, marked the beginning of TV broadcasting with the

broadcasting of the New York World’s Fair and the first televised speech by a U.S. President, the

industry grew very slowly for the next ten years (Grolier Encyclopedia, Mitchell Stephens,

www.nyu.edu). The dramatic changes in the number of TV broadcasters throughout the disruption

are exhibited in Figure 9. Considering only the entrant firms, there was an early stable phase between 1941 and 1948, followed by a phase of steady growth in the number of firms (1949-1955).

The state of the industry was undoubtedly greatly influenced in the early years by the persistence

and aftermath of World War II. This could explain the lack of growth from 1941 to 1949. The

growth from 1949 to 1955 reflected the overall growth and strengthening of the postwar economy.

The rapid growth during the subsequent five years, as indicated by the large increases in the number

of broadcasters, can be attributed largely to the growth in the popularity of the television and its rapid penetration into households throughout the country. The final phase shows a slowing of growth in the number of entrant firms from 1959 to 1965.

Calculation and analysis of the first and second difference (see Table 4) support the conclusions drawn from the visual inspection and description of Figure 9. The first and second differences were relatively flat from 1941 through 1949, averaging 5.00 and 3.60 respectively and indicating little growth and little acceleration. From 1949 through 1954 the average first and second difference were 20.69 and 2.50, denoting growth, but very little acceleration. In 1955 the number of firms jumped and growth was high for four years (average first difference 68.80), although acceleration was negative (average second difference -6.60) before settling back to limited growth (average 11.86) with slight acceleration (4.43) from 1959 to 1965.

600

500

400

300

Count 200

100

5 7 49 51 953 1941 1943 194 194 19 19 1 1955 1957 1959 1961 1963 1965 Source: FCC Annual Reports

Figure 9 Number of Licensed TV Broadcasters

Year TV Licensed 2nd broadcasters Difference Difference 1941 2 2 0 1942 4 2 -2

1943 6 0 0 Average

1944 6 0 0 Range 1st 2nd 1945 6 0 0 1941-1949 5.00 3.60 1946 6 0 1 1949-1954 20.67 2.50

1947 6 1 5 1955-1959 68.80 -6.60

1948 7 6 28 1959-1965 11.86 4.43 1949 13 34 0 1941-1965 22.24 1.40 1950 47 34 -19

1951 81 15 -10

1952 96 5 -2 1953 101 3 30 1954 104 33 16 1955 137 49 109

1956 186 158 -75 1957 344 83 -35 1958 427 48 -42 1959 475 6 10

1960 481 16 -19

1961 497 -3 34 1962 494 31 -30 1963 525 1 32

1964 526 33 -34

1965 559 -1 38

Table 4 Number of Licensed TV Broadcasters, with differences calculated

The number of motion picture screens in the U.S. was used as a proxy for the number of incumbent firms. Figure 10 shows that from 1948 to 1954, a phase of growth for TV broadcasters, the number of movie screens was nearly constant. From 1954 to 1963, the number declined from over 18,000 screens to just over 12,000. The next four years were fairly flat before there was a slight increase again between 1967 and 1971. It is not known if movie theatre companies consolidated, closed, or if ownership otherwise changed during this time, but the number of screens did give some hint as to the pattern of competition among incumbents during this period. The first and second differences were not calculated for this proxy measure. Also, intensity figures could not be calculated for the analysis of this case because they are generated from market share and no market s hare data we re available .

Figure 10 Motion Picture S creens

20000

15000

10000

5000

0 1948 1954195 8 1963 1967 1971

Because of the incomple te data, it w as im possible to say whether propositions 1, 2, and 3 were sup ported. The re was some indication that two phases of disruption may have occurred, based on the TV broadcaster numbers, but this could not be determined with certainty.

Revenues (Prop ositions 4 a nd 5)

The Motion Picture Association of Ame rica (MPAA ) has trac ked revenues at the box office over the past decades. These are captured in Figure 11 and Table 5 and help to respond to P4: both phases of the disruption are marked by significant decline in sales for incumbent technologies.

There was a negative trend in box office gross from 1946 to 1962 (average first difference -612).

The visual inspection of the graph indicated 2 phases within that 15-year time span. The decline and the acceleration of decline were greater from 1946 to 1951 (averaging -1128, 473 respectively) than from 1951 to 1962 (average -322, 24). The decline in the motion picture industry revenues stopped in 1962. The number of picture screens and the box office gross flattened out and even began a weak recovery after this point. P4 was supported; incumbents did experience significant declines in sales in Phase 1 and Phase 2. Even when adjusted for inflation, box office gross revenues increased after 1962.

Figure 11 Box Office Gross Adjusted

18,000

16,000

14,000

12,000

10,000

8,000

6,000

$ in MM (Adjusted to 2003 $s) 2003 to (Adjusted MM $ in 4,000

2,000

0 4 6 2 8 5 5 5 58 6 6 70 946 9 9 9 9 9 1 1948 1 1952 19 1 1 1960 19 1964 1966 1 1

Table 5 Revenues (adjusted), with differences calculated Box Office TV Broadcast Box Office Gross TV Broadcast Gross Year Adjusted Adjusted 1st 2nd 1st 2nd Difference Difference Difference Difference Revenues Revenues 1948 11,498 114 -303 316 316 450 1949 11,195 430 -667 766 766 545 1950 10,528 1,196 -1,102 1,311 1,311 -435 1951 9,426 2,507 -226 876 876 154 1952 9,200 3,383 28 1,031 1,031 150 1953 9,228 4,414 -671 1,181 1,181 -631 1954 8,557 5,595 -291 550 550 153 1955 8,266 6,145 -656 703 703 -259 1956 7,610 6,848 -551 444 444 128 1957 7,059 7,292 -629 572 572 -199 1958 6,430 7,864 -69 373 373 199 1959 6,361 8,237 -242 571 572 -295 1960 6,119 8,808 -301 277 277 804 1961 5,818 9,085 -487 1,081 1,081 -281 1962 5,331 10,166 231 800 800 365 1963 5,562 10,966 62 1,165 1,165 -431 1964 5,624 12,131 461 735 734 426 1965 6,085 12,866 -25 1,160 1,160 -802 1966 6,060 14,026 55 359 359 927 1967 6,115 14,385 663 1,285 1,286 251 1968 6,778 15,670 -290 1,537 1,537 -1,635 1969 6,488 17,207 290 -98 1970 6,778 17,110 Series Average -215 814 773 -20 Box Office Gross TV Broadcasting Revenues Range Avg. 1st Avg. 2nd Diff Range Avg. 1st Diff Avg. 2nd Diff Diff 1948-1951 -575 817 1948-1961 718 35 1951-1961 -372 696 1951-1961 696 -7 1961-1968 84 1,015 1961-1968 1,015 -148 Source: FCC Annual Reports

FCC data was used to test P5 (revenues for the nascent technology increase gradually in

Phase 1 and the rate of growth increases in Phase 2), and determine that revenues for TV broadcasting companies increased throughout the disruption range (Figure 12). Unfortunately, TV revenue figures were not available until 1948. From 1948 to 1949, there was only a slight increase in revenues. It is reasonable to assume that, prior to 1948, revenues were fairly flat. One can hardly

imagine that revenues for TV broadcasters were large between 1940 and 1948 and then flattened

before increasing again. Furthermore, the number of TV broadcasters stayed constant in those early

years, which further supports the assertion that revenues too were constant. With this in mind,

Proposition 5 was not supported in this industry. Visual inspection of the graph (Figure 12) reveals

slight increases prior to 1951. After 1951, there is growth with an apparent break in the pattern of

growth arou nd 1961. C alculation an d analysis o f the first and second difference (Table 5) revealed

a change as well. There was growth from 1948 to 1961 as evidenced by the positive average first

difference (718), and the rate was fairly constant as revealed by a very sm all average second

difference (35). After 1961 the first difference was considerably larger (1015). This combined with

the negative average second difference (-148) after 1961, re veals more growth from 1961 to 1968

than in the p rior decade , but at a slow ing rate. This slowing rate is con firmed by th e downward

trend in the average sec ond differenc e as the years progress . This was not consisten t with

Proposition 5 where th e expectation was that th e rate would increase in Phase 2.

Figure 12 TV Broadcasti ng Revenues

20,000 15,000 10,000 5,000 0

4 48 50 52 54 56 58 60 62 6 66 68 70 9 9 9 9 9 9 9 9 9 9 9 9 1 1 1 1 1 1 1 1 1 1 1 1 Adjusted Revenues (000)

Summary (Proposition 6 and Definition)

The stylized summary (Figure 13) reveals that 1961-62 was a turning point in the history of competition between motion pictures and TV broadcasting. TV broadcasting continued to enjoy increases in revenues and in the number of licensed broadcasters after 1961, but the rate of growth

slowed significantly. The incumbent, motion pictures, suffered with significantly declining sales, but after 1962 revenues stabilized and began a slight recovery. (In this case the only simplification needed to standardize the graphs of the four variables was to multiply the density or count data by ten. This allowed the scale of the count to be similar enough to the other variables for the trends to be compared on the same graph.)

Figure 13 Stylized Summary: Motion Pictures vs. TV

20,000 18,000 16,000 14,000 12,000 10,000 8,000 6,000 4,000 2,000 0

6 8 0 2 4 6 8 0 2 4 6 8 0 4 4 5 5 5 5 5 6 6 6 6 6 7 19 19 19 19 19 19 19 19 19 19 19 19 19

Box Office Adj. Revenues TV Broadcasting Adj. Revenues

Count TV Broadcasters Movie Screens

Although the information on the early years of motion pictures and TV was scarce, that

which was available indicated two phases to the disruption. An initial phase prior to 1961 was

indicated by significant change in each measure used to compare the two technologies. After 1961,

the motion picture industry appeared to have reached a more constant state than it had encountered

for the previous 20 years, despite the continued growth of TV broadcast revenues. Therefore,

Proposition 6 (two phases of the disruption range exist) was weakly supported by these industries.

The application of the definition of disruption revealed that this market nearly qualified as

disrupted. TV broadcast revenues easily surpassed the box office gross. The box office gross in

1962, the year with the lowest sales, was 46 percent of the peak year sales, 1948. However, by

1965 box office gross rebounded to over 50 percent of the peak year sales.

Moviegoing vs. Video Rental Business (Propositions 1-6)

The second disruption of the motion picture industry came with the advent of the videocassette recorder (VCR) and the availability of home viewing of studio-produced movies.

Sony introduced the VCR in the U.S. in 1976, but because the VCR is one of many products offered by large electronics companies, it could not be studied as an example of a disruptive technology.

For example, both Sony and Panasonic produce VCRs. The effect of the VCR on these large companies was minimal, even while the effect of this new technology on the motion picture industry was extensive. However, another industry was created with the advent of the VCR technology. That industry was the video rental business. In 1990 Dun’s began tracking firms in the

SIC 7841, Video Tape Rental. This industry represents the type of pure play that allows for the study of the effect of the new technology on the incumbent industry, motion pictures. Pure play means that the companies offering videotapes for rent were largely start-ups in a single line of business and domestically owned and operated, making the study of the market clearer and more feasible. By the time of this second disruption, Dun’s had begun tracking the motion picture industry. Data from Dun’s were used for the following analysis.

Count (Proposition 1 and 2)

In the early years of home video availability the number of motion picture producers was fairly constant, ranging from 5 to 12 between 1982 and 1989 (Figure 14). The count increased dramatically to 27 in 1990. For the next decade there were on average 30 motion picture producers.

In 1990 the new SIC (7841) for Video Tape Rental was established. Figure 14 and Table 6 show

that from 1990 to 1996 only a very few rivals competed in this business. (Appendix B shows that

Blockbuster Entertainment and Blockbuster Video were involved in this industry beginning in

1991. In 1995, however, Blockbuster was in an acquisition stage and did not report to Dun’s,

therefore, no figures are available for the firm in that year.) After 1996 the number of video rental businesses doubled, although this still meant only four or five companies competed for the market.

Figure 14 Number of Firms: Motion Picture Producers and Video Rental

40 6 5 30 4 20 3 2 10 1 0 0

982 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 000 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2

Motion Pix Producers Video Rental

Table 6 Moviegoing vs. Home Movies: Count and Intensity Count Motion Picture Year Producers Video Rental Combined Intensity 1982 5 5 2 1985 6 6 2 1986 8 8 4 1987 5 5 2 1988 9 9 3 1989 12 12 4 1990 27 2 29 10 1991 19 2 21 8 1992 35 4 39 11 1993 37 1 38 9 1994 37 2 39 9 1995 37 37 8 1996 31 2 33 6 1997 30 5 35 7 1998 29 4 33 9 1999 29 5 34 9 2000 29 5 34 6

Industry density data strongly supported the presence of two phases of this disruption: one following the introduction of VCRs and another beginning in 1990 with the introduction of the SIC for videotape rental businesses. It was noticeable that there was relative stability in the number of

motion picture producers for over 10 years after the 1976 introduction of the VCR. It was not until

1990 that major changes took place in terms of the number of production studios competing for the customer base. This finding was partially supportive of the first proposition that the onset of the disruption is marked by an increase in the number of competitors. However, it indicated that other factors may have been in play that, in this case, caused a delay in the effect of the entrant on the incumbent competitors. Proposition 2 was not supported; there was not a sharp decrease in the number of competitors in this market (see Figure 14). This may indicate that the disruption is ongoing, or it may indicate the lack of a disruption.

Intensity (Proposition 3)

The rivals in these two industries did not co-opt one another’s products as they did in telecom. For the most p art, motion pi cture producers and theatre owners did not open video rental businesses nor did Blockbuster get into theatres or motion picture production. Therefore, judging the intensity of this market must be considered carefully. In Table 6, for the sake of investigation, the number o f studios and video r ental firms w ere combined. Intensity, when considered from the perspective of the incumbent or the combined industries, changed after 1989. The majority of the change was the result of the incumbent firm count increasing, not the addition of the entrant companies. In the early years, 1982 to 1989, an average of three firms controlled 75 percent of the market. Afte r 1989 th ere were on average eig ht rivals competing to control 75 percent share. This was precisely what was expected based upon Propositi on 3. The intensity was greater at the beginning of the range than later. There was a very notable change in 1990 when intensity decreased significantly.

Revenues (Propositions 4 and 5)

Proposition 4, stating that incumbent sales significantly decline throughout the disruption, was not supported by data from this industry. Figure 15 shows that revenues for firms involved in motion picture production during the period 1982-1988, preceding the tracking of video rental business, were flat. From 1989 to 1992, however, even when adjusted for inflation, revenues of motion picture studios increased. One could argue that some of this increase was realized because of the video movie industry and not despite it, as many more films were produced to support the home video market.

Figure 15 Motion Picture Production: Revenues

160,000 140,000 120,000 100,000 80,000 60,000 40,000

u20,000 R u 000,000)

Adj sted even es ( 0

1982 1986 1988 1990 1992 1994 1996 1998 2000 Data Source: Dun and Bradstreet

Consideration of the box office gross revenues is more indicative of what was happening in

terms of the health of the theatre industry and the impact of home movie viewing on moviegoing.

Figure 16 and Table 7 show box office gross revenues adjusted for inflation. There was an increase

in box office gross from 1982 to 1984 and a decrease in 1985 and 1986. This was followed by

another upturn for the next three years. Much of this fluctuation reflected the fluctuations in the

economy overall and resulted in no growth (first difference, -2) and slight deceleration (-22). After

1993, revenues began a steady increase and were no longer following the cyclical changes of the

economy. Despite the fact that VCR penetration of TV households in 1986 was over 27 percent

and climbing (70 percent in 1990 and 86 percent by 2000—a good indicator of the spread of the

nascent technology) the box office gross revenues and the per capita number of movie admissions

increased from 1993 until 2002 (see Figure 16 and Figure 17). Box office gross revenues grew

based on the visual inspection, which was reinforced by the positive first difference of 353.

Although there was growth in the later phase, it was not accelerating (second difference, -43).

Figure 16 Box Office Gross Revenues

12,000

10,000

8,000

6,000

4,000 $ MM Adjusted 2,000

2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 97 97 97 97 98 98 98 98 98 99 99 99 99 99 00 00 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2

Data Source: Motion Picture Association of America (MPAA)

Table 7 Box Office Gross Revenues (Adjusted for Inflation) Year Box Office Gross Difference Adjusted 1st 2nd 1982 6,583 374 -193 Difference 1983 6,957 181 -907 Range 1st 2nd 1984 7,138 -726 657 1982-1992 -2 -22 1985 6,412 -69 615 1993-2001 353 -43 1986 6,343 546 -500 1987 6,889 46 489 1988 6,934 534 -934 1989 7,469 -399 -182 1990 7,070 -581 480 1991 6,489 -101 276 1992 6,388 175 -38 1993 6,563 137 -204 1994 6,700 -67 367 1995 6,633 300 65 1996 6,933 365 181 1997 7,298 546 -165 1998 7,844 382 -422 1999 8,226 -40 595 2000 8,186 555 442 2001 8,740 996 -1,244 2002 9,737 -248 -9,240 2003 9,489 -9,489 9,489

Figure 17 Per Capita Movie Admissions

10.00

8.00

6.00

4.00

2.00

0.00

0 2 4 6 8 0 2 4 6 8 0 2 8 8 8 8 8 9 9 9 9 9 0 0 19 19 19 19 19 19 19 19 19 19 20 20

Data indicated that despite the widely available home entertainment VCR, the average number of Americans going to the movies increased slightly from a demand perspective (movie

admissions) and more considerably from a revenue perspective after 1993. Proposition 4 was not

supported by the data.

Table 8 Video Rental Revenues (inflation adjusted and with differences calculated) YEAR Revenues 1st Diff 2nd Diff 1990 374 1,081 -318 1991 1,455 763 -1,835 Average 1992 2,218 -1,072 3,132 Range 1st 2nd 1993 1,147 2,060 1990-1992/1993 708 326 1994 3,206 1996-1998 1,680 1,242 *1995 1996 1,303 707 95 1997 2,010 802 -23 1998 2,812 779 3,653 1999 3,591 4,432 2000 8,023 Series Average: 1,194 784 Data Source: Dun’s *Blockbuster was undergoing a merger in 1995, revenue figures were unavailable.

Revenues of video rental businesses had a positive trend overall from 1990 to 2000

(average 1,194 first difference, 784 second difference). The data also supported the conclusion that there were two phases in the growth of this entrant technology. Figure 18 and Table 8 show that in the early 1990s sales growth and acceleration were positive, as indicated by the relatively small first

and second differences (708 and 326, respectively). However, after 1995, growth was stronger with larger positive first and second differences (1680 and 1242, respectively).

Figure 18 Video Rental Revenues (adjusted for inflation)

10,000 8,000 6,000 4,000 2002 $s 2,000 0 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 Revenues (000) adjusted to (000) adjusted Revenues

*Blockbuster was undergoing a merger in 1995, revenue figures were unavailable.

The evidence strongly supported Proposition 5, an initial phase of gradual growth was followed by a second phase of more rapid growth.

Summary (Proposition 6 and Definition)

When all of th e data fo r movie going versus home video rental were summarized, the

evidence did not support a disruption. The stylized summary (Figure 19) displays growth in both

the incumbent and entrant over time. Initially, all measures wer e flat; in creases began after 1990.

This ma y have supported the idea of two phases. However, they were not phases of the destruction

of the incumbent technology an d firms. This industry did not meet the definition of disruption

established as a means of testing each market. Video rental revenues were equal to those of box

office gross at the end of the period but box office gross had increased rather than decreased. (In

this case, the simplification needed to standardize the graphs of the four variables was to multiply

the count data by 100 and the intensity data by 1000. This allowed the scale of the count to be

similar enough to the other variables for the trends to be compared on the same graph.)

9,000 8,000 7,000 6,000 5,000 4,000 3,000 2,000 1,000 0

2 4 6 8 0 2 4 6 8 0 8 8 8 8 9 9 9 9 9 0 9 9 9 9 9 9 9 9 9 0 1 1 1 1 1 1 1 1 1 2

Box Office Adj. Revenues Video Rental Revenues Combined Count Industry Intensity

Figure 19 Stylized Summary: Moviegoing vs. Home Video

Disruption Range Description - Across Industry Analysis

To facilitate the cross-industry analysis, the visual comparison of the stylized summaries

(see Appendix D) for each market was augmented using the forced pair comparison analysis of the

data in Table 9. This examination revealed similarities between the telecom case and the case of

wireline and wireless and motion pictures vs. home video, and differences between these two disruptions and that of movies vs. TV.

Table 9 Summary – Cross-Industry Analysis Measure Wireline vs. Wireless Motion Pictures vs. TV Moviegoing vs. Home Video Proposition 1 (Count) Yes Yes – Weakly Yes Proposition 2 (Count) Yes Unknown No Proposition 3 (Intensity) Yes Unknown Yes Proposition 4 (Inc. Revenues) No Yes No Proposition 5 (Ent. Revenues) Yes No Yes Proposition 6 (Phases) Yes Yes Yes - Weakly Entrant Sales > Incumbent Sales Yes Yes Yes Incumbent Sales 50% of Peak No Yes & No No

Wireline versus wireless and motion pictures versus home video behaved similarly, differing on only one of the measurements studied (Proposition 2, industry density will decline in the second phase). Another similarity between these two cases is that each supported nearly all of the propositions. The third case, moviegoing versus TV, differed from the other two on three dimensions and upheld fewer of the propositions.

One of the goals of this research was to develop a more precise understanding of what happens in markets as they are disrupted by new technologies. The similarities between telecom

(wireline vs. wireless) and motion pictures versus home video revealed by the forced pair comparison allowed for the development of the description of a disruption range. The similar properties resulted in the following portrayal.

First, the disruption range was characterized by an initial increase in the number of competitors. The pattern in the number of rivals during the latter part of the time frame was not consistent and therefore could not be determined from this work. The intensity in the market decreased during the disruption with more firms sharing in 75 percent of the market as time progressed.

Second, incumbent revenues were initially relatively flat, but eventually enjoyed growth.

Entrants experienced accelerating growth throughout the time period. There appeared from the evidence to be distinguishable phases during the disruption range; an initial phase of moderate fluctuations in the variables studied followed by a later phase of more remarkable change. There was revenue growth for both classes of competitors, and there were changes in the number of competitors. The change in count was not consistent based upon t hese two industries.

Finally, entrant firm revenues did surpass the incumbe nt firms. However, the incumbent revenues were not decreased to less than 50 percent of peak ye ar sales.

The remaining foundation industry comparison, motion pictures versus TV, provided an interesting contrast and added valuable insight into the phenomenon of disruptive technologies.

Although the disruption supported many of the propositions, it differed from the other two foundation market disruptions on three critical variables. First, the entrant revenues grew at a more accelerated rate in the earlier phase of the disruption, not the later one as in wireless and home

video. Second, the incumbent revenues dropped precipitously, upholding Proposition 4. Third,

revenues did drop to less than 50 percent of peak year sales during the course of the upheaval.

In Chapter 3, a decrease in incumbent revenues to less than 50 percent of peak year sales

was established as an indication of disruption. This heuristic, developed to stimulate investigation

into the boundaries of the phenomenon of disruptive technology, was revelatory. In the foundation industries only one case, movies versus T.V., satisfied this limit; revenues dropped to 46 percent of

peak year sales. In the other two cases of disruption, incumbent revenues leveled off and then

began to increase again. Wireline revenues dropped to 76 percent and box office gross revenues to

85 percent of peak year sales. Each of the three foundation industries was substantially changed and

qualified as disrupted based on many of the other variables studied. Further investigation into the

patterns of decline in incumbent revenues could indicate a threshold that more precisely defines

changes in incumbent sales during disruptions.

As Eisenhardt (1989) stated, disconfirming evidence often provides the opportunity to

better understand the boundaries of a theory or phenomenon. It is fortuitous that the three industries

selected as “disrupted” actually produced two different patterns of results. The juxtaposition of

these results strongly suggests that different types of technological disruptions exist. This finding is

important if further research is to be conducted that meaningfully characterizes the phenomenon.

Once the boundaries are understood, this line of research can become more helpful to practitioners

attempting to develop strategies to deal with disruptions.

Usually, a description of a disruptive technology refers to a zero-sum discontinuity. Zero- sum indicates that the introduction of a nascent technology results in the nearly full substitution of the incumbent technology. There is a trade-off in the demand for the two products and in the end the new technology substantially replaces the established one. The portrayal of disruption

generated by the analysis of the foundation industries and supplied above is actually a description of

a different type of disruption. This type could appropriately be named a positive-sum disruption.

Positive-sum disruption refers to the discontinuity in the marketplace that is challenging for existing competitors because of the absence of predictability, but in the end serves to fuel the market for

both the incumbent and entrant technologies.

Positive-sum disruption is different from the more devastating disruption witnessed when

motion pictures suffered from the introduction of TV. With the dawn of TV, the incumbents faced

great uncertainty and lack of predictability, much like positive-sum disruptions, but this

technological discontinuity was more perilous and resulted in severely reduced revenues for the

incumbents.

None of the nascent technologies in the foundation industries created the type of

discontinuity usually described as disruptive. None of the entrants served to obliterate the existing

product lines or industries. In none of these cases was there an exchange of the nascent technology for the old that served to make the old extinct. The categories of disruption, identified through this

research, are discussed in greater detail below and in the following chapter.

Comparing the Findings to the Extant Literature

An iterative approach comparing the findings of case studies to the extant literature allows for better theory building (Eisenhardt, 1989). Therefore, in this section the results of the case analyses are compared to the prior research on disruptive technologies.

None of the three cases studied is nearly the full substitution that Foster (1986) and others

(Cooper and Schendel, 1976; Dahlin and Behrens, 2005) used to describe disruptive markets. There remains today an extremely strong market for landline phones and moviegoing. Although movie making and moviegoing were seriously harmed by TV, the motion picture industry remains today a

strong, $9 billion industry. Movies were not replaced by TV or the VCR, and the cell phone, although ubiquitous, has not replaced the wireline technology.

Analysis was conducted to determine if the new technologies studied here were what

Christensen (1997) and others referred to as sustaining technologies. Christensen defined sustaining technologies as technologies that “improve the performance of established products, along the

dimensions of performance that mainstream customers in major markets have historically valued”

(p. xv).

Based upon this description, some would categorize cell phones as sustaining. However,

this classification is not appropriate. In its infancy, the cell phone was not appealing to the mainstream wireline phone customer. In fact, it started in niches, had inferior performance, and was more expensive than traditional landline telephony. The expense, coupled with other quality issues like poor reception, dropped calls, and limited calling radius, caused cellular to be rejected by many mainstream customers. It more closely fit Christensen’s description of disruptive technology: appealing to a new value network, with features not previously considered valuable and a cost above that of the existing technology. Cell phones also fit Cooper and Schendel’s (1976) portrayal of a crude and expensive technology that transformed or destroyed existing technologies as it expanded through submarkets. Cooper and Schendel reported that the nascent technology sales surpassed the incumbent, usually within 5 to 14 years, as sales of the incumbent declined. Yet landline technology persists 25 years after the advent of the cell phone and the market remains sizeable ($80 billion market in 2000). Cellular technology created a discontinuity and transformed the telecommunications industry, but it certainly did not destroy the market for wireline technology.

When comparing the home video industry to the definition of sustaining technology, most mainstream customers did not perceive watching movies at home as an improvement on the existing technology in performance areas that they valued. Mainstream customers in this market

were moviegoers. They liked the silver screen and attended the theatre for entertainment. Home video more closely resembled a disruptive technology in that it satisfied a new value equation with a new feature and benefit offering. It was expensive when the investment in a VCR is included as

part of the cost of renting a movie. However, it did not fit the lexical or operational definition of a

disruptive technology completely. The VCR and home movie viewing transformed the motion

picture industry, but it certainly did not destroy it. Incumbent revenues did not drop below 50

percent of peak year revenues. In fact, in recent years box office gross revenues increased as the movie rental business grew. Here again, home video did not completely or clearly fit either the common definition of sustaining technology or disruptive technology.

Based upon the preceding data, some would qualify TV as clearly disruptive to moviegoing. TV was initially inferior to moviegoing and purchasing a TV was quite expensive compared to a theatre ticket. Although watching movies and shows in the home was probably viewed as substandard to watching the silver screen, the TV eventually penetrated nearly every U.S. household. Yet in 2005, over 60 years after the TV was introduced, the motion picture industry has been transformed but certainly not destroyed. TV was a substitute for movies for some people, but not for all. As stated previously, motion pictures are still a very substantial force in the world economy. Based on the successful persistence of both media, it is reasonable to speculate that consumers are not committed to a single movie source, but mix their consumption for variety. So was TV a disruptive technology? Yes. Was it disruptive in the same sense that the diesel engine was disruptive to the steam engine? No. Was it disruptive in the same sense that the VCR and home video were to movies? No.

Anatomy of Disruptive Technologies

Herein lies one of the main contributions of this work. The goal to more precisely understand disruptive technology was met. The cases studied indicated that there are degrees of disruption that should be classified. The industries studied were carefully chosen because they represented markets substantially changed by innovation. The detailed examination revealed however, that none of the nascent technologies obliterated, overwhelmed or nearly replaced the incumbent technology. In many cases, there were incumbent firms, such as AT&T, that were overwhelmed by the changes in the industry structure and severely hurt, but these were companies that suffered. None of the discontinuities reduced the incumbent technologies to that of near

obsolescence, novelty status, or a small fraction of previous sales and market share. Although the

nascent technologies created discontinuities that were threatening to the incumbent firms and

necessitated considerable adaptation, the threat of this disruption was that of more diverse and more

challenging competition, not the threat of obsolescence. In the final analysis of telecom and home

video viewing versus moviegoing, it was evident that the threat was not hazardous or dangerous to

the industry and the incumbent technology. The inability to adapt to the changes proved

devastating for some companies within the industry, but the industries themselves were enhanced

rather than decimated. This distinction is important to academics and practitioners alike.

Academicians can further examine these categories to determine if there are more dimensions upon

which the categories can be judged and thereby better understood. Practitioners should use the

information to carefully consider fluctuations in their industry structure and revenues when a

potentially radical innovation is introduced.

TV, primarily due to its more significant impact on movie theatre revenues, was an

example of a more devastating type of disruption. The rapid growth of TV revenues and the drastic

decline of box office gross revenues coupled with the changes in count and intensity described

before represent a competitive disruption; one that transforms an industry and puts incumbents in

great peril as they are forced to adapt to a new set of rivals offering a potential substitute for the current technology.

Figure 20 Categorization of Technological Innovation Radical Incremental Innovation Innovation

Zero-Sum Competitive P ositive-Sum Sustaining

Disruption Disruption Disruption Innovations

The conclusions drawn from the preceding analysis were that understanding new technologies is not as simple as categorizing them as: radical and incremental or disruptive and sustaining. In fact, there appeared to be a spectrum along which new technologies fell (see Figure

20). On one end of the spectrum was the zero-sum disruption followed by competitive disruption, positive-sum disruption, and, on the other end of the scale, the sustaining innovations.

To understand the difference, the traditional description of disruptive technologies should be altered. Rather than referring to the disruptive technology, each category should be referenced as a type of disruption. This more accurately represents the convergence of market factors and events that transpires to provoke or create the disruption. As stated in Chapter 2, the entrant technology itself is not disruptive; it is the combination of the market conditions, including the customer base, incumbent firms, incumbent technology, entrant firms, and nascent technology, which combine to create different types of disruptions.

The anatomy of disruptions then lends itself to the following structure and terminology.

Zero-Sum Disruption refers to a discontinuity in the marketplace when the introduction of a nascent technology results in the nearly full substitution of the incumbent technology. As in game theory, zero-sum implies an inverse relationship between the progress of the nascent technology and the decline of the incumbent. There is a trade-off or exchange in the market, and in the end only one of

the two contending technologies survives. The nascent technology appeals to new value networks with new features and benefits not previously considered valuable, even though the entrant technology is often initially inferior and more expensive. (This description is built almost solely from the extant literature and is therefore less detailed than those for competitive and positive-sum

disruptions. This is because none of the industries studied actually qualified as zero-sum

disruption.)

Competitive Disruption refers to discontinuity in the marketplace when the introduction of

an inferior and more expensive, nascent technology threatens the incumbent firms’ sales and

appears to increase density of competition by increasing the number and type of rivals. The entrant

firms’ revenues increase rapidly in the early phase of the disruption and the rate of growth slows in

the later phase. Incumbent revenues decline throughout the disruption, and revenues from the

nascent technology surpass those of the incumbent. Existing firms’ sales do not drop below 50

percent of peak year revenues. The term competitive is used to describe this category of disruption

because as in competitive matches, the rivals vie for the winning position, but in the end both

survive. The entrant does not obliterate the incumbent; the two coexist in a market that has been

transformed.

Positive-Sum Disruption refers to discontinuity in the marketplace when the introduction of

an inferior and more expensive, nascent technology initially flattens revenue growth for the firms

offering the incumbent technology. The mainstream customer base initially stays loyal to the

incumbent as the nascent technology is appealing to a niche and expands through submarkets. The

introduction of the new technology reduces the predictability in the industry, increases the density

or number of competitors, and decreases the intensity of competition. The entrant firms’ revenues

increase throughout the disruption and surpass those of the incumbent, but the existing companies

regain momentum and eventually incumbent revenues increase. This is termed a positive-sum

disruption because the introduction of the new technology actually enhances demand for both the incumbent and nascent technologies. So both types of rivals benefit from the introduction; in effect the pie is made bigger by the introduction of the nascent technology.

Sustaining Innovation refers to incremental or generational changes to the existing technology, which do not cause the incumbents to alter or transform the way they do business. The sustaining new technologies appeal to the incumbent firm’s mainstream customers, are generally

superior to the incumbent technology, and are comparably priced. (This description is built almost

solely from the extant literature and is therefore less detailed than those for competitive and

positive-sum disruptions. This is because none of the industries studied actually qualified as zero-

sum disruption.)

Mainframe Computers vs. Personal Computers (PCs): Disruption Range

In this section of Chapter 4, the conclusions drawn in the previous section relative to the propositions and the category descriptions were applied to the computer industry to test the inferences drawn. Specifically, data from SIC 3573 (which later become SIC 3571) called

Electronic Computers were analyzed.

Analysis and Categorization of this Disruption

Proposition 1 was well supported in the computer industry. The number of firms competing in SIC 3573/3571 shifted radically from 1982 to 1986. The number of firms increased from 33 to 52, thus supporting Proposition 1. The number then dropped precipitously in 1989 to 33, and then began a slow but steady decline over the years that followed (see Figure 21). This was the only industry studied where the evidence supported the marked decrease in count at one point during the disruption. Thus, this industry and none of the others that were studied supports

Proposition 2. It should be noted that data on movie picture producers was not available so it is possible that the motion picture industry also supported this proposition.

Figure 21 Number of firms in the computer industry

100 80 60 40 # of Firms 20 0 1982 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 Data Source: Dun's

The intensity measure, as seen in Figure 22, shows a marked increase in intensity after

1989 (fewer firms controlling 75 percent market share indicates higher intensity). The pattern in intensity closely mimicked the pattern in density, each indicating a change in the market after 1989 followed by relative stability. The intensity data did not support Proposition 3, which suggested

that intensity would be greater during the first phase than the second.

Figure 22 Computer Industry: Intensity

9 8 7 6 5 4

# of Firms 3 2 1 0 1982 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996

Propositions 4 and 5 were more difficult to judge in the computer industry than in the others. This was because PCs were not assigned a new SIC when they were introduced. They were

included in 3571 as “Electronic Computers,” the same category as the incumbent mainframes.

Therefore, the analysis here was initially conducted on all firms in 3571 (previously designated as

3573). This revealed a gradual but steady increase in revenues (adjusted for inflation) from 1982 to

1993 (Figure 23). Thereafter, revenues declined and then flattened.

Figure 23 Computer Industry Revenues (Adjusted for Inflation)

300,000,000

250,000,000

200,000,000

150,000,000

100,000,000

50,000,000 $s adjusted 2003 (000) 0

2 5 6 7 8 9 0 1 2 3 4 5 6 8 8 8 8 8 8 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 1 1 1 1 1 1 1 1 1 1 1 1 1

The Computer Industry Almanac (Juliussen and Juliussen, 1994) did provide revenue data

from Computer Intelligence Infocorp categorizing U.S. shipments of PC Products, Desktop PCs,

Large Systems, Midrange Systems, and Workstations. According to these data, the combined

revenue of the two PC categories was $9.9 billion in 1990, and the sales in the other three categories

combined were $323 million. These data clearly indicate that PC sales had surpassed non-PC sales

by 1990. Unfortunately, the Almanac does not date back to the early part of the PC revolution, and the data from Computer Intelligence Infocorp were proprietary and unavailable. Therefore, no early data on PCs versus mainframes were accessible and a trend line could not be established.

Because there was not a unique SIC for PCs, a set of five entrant firms (Datapoint, Apple,

Compaq, Dell, and Gateway) recognized historically as powerful drivers of change in the industry were used as an entrant set. Revenues for this group were graphed in Figure 24. These were compared to the set of “all others,” which is the industry total less these five firms. This all other set was used here to approximate the incumbents.

Figure 24 Computer Industry Revenues: Incumbents vs. Entrants

180,000 160,000 140,000 120,000 100,000 80,000 60,000 Incumbents 40,000 20,000 Entrants 0 Revenues (000) Adjusted 2003 $s 2 5 6 7 8 9 0 1 2 3 4 5 6 198 198 198 198 198 198 199 199 199 199 199 199 199

Using these estimated groups, a visual inspection revealed that the two groups’ revenues

followed a nearly inverse pattern. The entrant firms enjoyed an uptick in revenue at the same time

the incumbents’ revenues declined. Again, around 1992-93, while the entrants’ revenues moved up,

the incumbents decreased. From that point forward the entrants’ revenues grew. The incumbents’ revenues fell until 1995, and then rebounded in 1996.

It is important to note that this was only a rough approximation of what happened. The

incumbents that were included in the all other data set also introduced PCs in many cases. Detailed

revenue history breaking out the incumbents’ sale of PCs versus mainframes was not available so it

was impossible to know with confidence what the revenue trends from the different categories of

computer products were during this time. The rate of change in mainframe sales was not known.

However, based on the Computer Industry Almanac (Juliussen and Juliussen, 1994), it is reasonable

to say that P4 was supported, as the incumbent mainframes experienced significant revenue decline

during the disruption.

The data regarding the entrant firms did show specifically the revenue trends of PCs as

none of these manufacturers offered mainframes. The magnitude of the market was quite different

from what is exhibited here, but the shape of the curve is fairly representative. Given these data, the

computer industry did support P5; revenues for the nascent technology did increase throughout both

ranges with a greater rate of increase in the latter part of the disruption.

Although Figure 24 exhibits that entrant firms remained considerably smaller than

incumbents, the additional data from the Computer Industry Almanac revealed that revenues for the

category of PC product (rather than the PC firms) did exceed the revenues of other types of

computers.

Table 10 Summary Table Measure Wireline vs. Motion Pictures vs. Moviegoing vs. Computer Industry Wireless TV Home Video Proposition 1 (Count) Yes Yes – Weakly Yes Yes Proposition 2 (Count) Yes Unknown No Yes Proposition 3 (Intensity) Yes Unknown Yes No Proposition 4 (Inc. Revenues) No Yes No Yes Proposition 5 (Ent. Revenues) Yes No Yes Yes Proposition 6 (Phases) Yes Yes Yes - Weakly Unknown Entrant Sales > Incumbent Sales Yes Yes Yes Yes Incumbent Sales 50% of Peak No Yes & No No Yes

100

Table 10 summarizes all of the industries studied and facilitates the forced comparison

between the computer industry and the foundation industries. Based upon this comparison, the

computer industry discontinuity most closely fits the category of competitive disruption. It was

more similar to moviegoing versus TV than either of the other two disruptions that were studied.

Furthermore, using the findings from this industry the previously develop description of

competitive disruption can be augmented as noted in the depiction below.

Competitive Disruption refers to discontinuity in the marketplace when the introduction of

an inferior and more expensive, nascent technology threatens the incumbent firms’ sales. It appears

that there is an initial increase in density followed by a decrease in density. Intensity remains fairly

constant in the first phase of the disruption but declines in the later phase. The entrant firms’

revenues increase rapidly in the early phase of the disruption and the rate of growth slows in the

later phase. Incumbent revenues decline throughout the disruption and revenues from the nascent

technology surpass those of the incumbent. Existing firms’ sales do not drop below 50 percent of

peak year revenues.

Mainframe Computers vs. PCs: Strategic Response and Firm Performance

The strategic response of the firms in the computer industry was compared to the performance of each firm. The first section below describes those firms that were considered for statistical analysis, but could not be included due to lack of available data. The second section is a description of the data used in the statist ical analysis and the final component explains the outcome of the statistical analysis.

101

Qualitative Analysis

Many companies from the combined set of early and late leaders could not be included in the statistical analysis because of incomplete data. Of this group there were eight about which substantial, although not complete, information was obtained. The limited available data were analyzed from a descriptive perspective for contextual understanding of the industry. Of the eight firms with incomplete data there were three incumbents, two new entrants, and three other firms,

qualified here as Opportunistic Dabblers. The term Opportunistic Dabblers was used to qualify

firms that were ongoing enterprises at the time of the PC revolution, but the primary business was

not mainframe computer so they were not included as incumbents. These firms were involved in

fields adjacent to mainframes and so a different category was designated.

Data regarding the incumbents were available from news stories and analysts reports at the

onset of the computer revolution and to varying degrees throughout the 1980s. Although the three

incumbents were reported to be fairly sizable and meaningful competitors in the mainframe

computer industry, none of the firms entered the PC market and none of them remains in business

today.

One of the three incumbents, Amdahl persisted offering computers other than PCs for some

time until being acquired in 1997 by Fujitsu (www.fujitsu.com; Thomson Research). Another incumbent, Prime Computer Inc. remained in mini-computers from 1972 until the early 1990s and attempted to exploit a niche using Computer Aided Design, but was unsuccessful. Apparently, around 1990, the firm ceased to operate as no financial filings or news stories could be located after that date (Thomson Research). The third rival in this group was Magnetic Peripherals Inc. It

appeared from the limited available information that this firm was a joint venture of Honeywell

Discs and Control Data Corporation. The firm pursued the disk market and ignored the PC

category (http://febcm.club.fr/english/bull_peripherals.htm).

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Sufficient evidence was available on most of the entrants that were in the selected set of

firms to be analyzed; those were therefore included in the statistical analyses and discussed below.

One, however, Datapoint Corporation, was among the original producers of PC type products, developing a programmable terminal used as a PC. In the early 1980s the firm was one of the

Fortune 500, but subsequently accumulated sizeable losses. According to the Datapoint website, the firm changed directions during the 1970s and refocused the business on call center technology and telephony (datapoint.com/about/history.asp, Thomson Research). Thus, there was at least one entrant that exited the PC market and found other technological areas to pursue.

The final group in the set of companies for which there was insufficient data for statistical analysis was the Opportunistic Dabblers. The name was selected because these are companies that

were in existence when the PC revolution began in industries adjacent to mainframes, but

recognized the potential that PCs represented and attempted to take advantage of the opportunity.

Zenith, for example, from the electronic entertainment industry, made a brief foray into PCs but

quickly exited, returning to electronics and remaining healthy (Pollack, 1981). Storage Technology

Corporation pursued the same pattern, briefly venturing into PCs, while maintaining its primary business before exiting PCs and concentrating solely on electronic storage (www.storagetech.com).

The third Opportunistic Dabbler was Rolm Corporation. After a brief attempt at the PC market

Rolm merged with IBM and became the telecom subsidiary for this giant

(http://www.laynetworks.com/history4.htm).

Statistical Analysis

Description of the data. Many sources were consulted to gain an understanding of the

strategic response of the 20 firms analyzed in the statistical analysis. These are listed in Appendix

F. The data gathered about these firms and their respective size, response, and timing of response

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are listed in Appendix G. The set includes five entrants and 15 incumbents. Firms that did not exist prior to the introduction of a PC or PC-style product and came into being with a personal computer as their flagship product were qualified as entrants. Ongoing enterprises that had been in the computer industry offering mainframe or minicomputers, primary SIC 3573, prior to the introduction of PCs were qualified as incumbents. As was explained in Chapter 3, the data used for this determination were gathered from Dun’s Business Rankings and archival references such as industry reports, news reports, and annual reports.

The firms in the statistical analysis ranged in size from $0 revenues to over $29 billion.

Because the entrants were entrepreneurial start-ups, the size of each was classified as $0. When an entrant firm began offering PCs it was new and had either no revenue are nearly no revenues. Most entrants were privately held initially and had small revenue figures that were unavailable. These then set the bottom of the range for size.

Incumbents ranged in size from $2.8 million in revenues (Control Data Corporation) to over $29 billion (IBM). The size of these firms, as describe in Chapter 3, was based upon the company’s revenues at the time that the firm introduced its first PC. For those firms that chose not to introduce a PC, size was based upon the company’s revenues in 1982.

Analysis of the responses of incumbents to PCs yielded interesting, although not

statistically significant, results. The most commonly pursued strategic response was internal R&D,

utilized by nine of the incumbents. Six of these were very large firms with revenues over $1 billion

(IBM, Litton, Digital Equipment Corp., NCR, Texas Instruments, and Wang). Data General,

Hewlett Packard, and Commodore were smaller.

When this research was proposed it was expected that some firms would have used a strategic business unit (SBU) to introduce a new, disruptive technology. Thorough review of available sources revealed no firms that used this response to the personal computer revolution.

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Some, like IBM, did locate PC operations away from company headquarters, but the PC subsidiary was under close control of the parent company. The IBM PC was a project in the works at an IBM facility that had existed since the 1960s. The facility reported to IBM headquarters, utilized IBM executives and managers and was funded on an ongoing basis by IBM. There was no evidence from the company records that an SBU was established for IBM or any other incumbent.

There were three existing competitors that did not enter the PC market. Two of the companies that never entered, Burroughs and Sperry, later merged to form Unisys, but still did not pursue the PC market. The third incumbent that did not introduce a PC was Control Data

Corporation. The data in Appendix G reveal that each of these three was among the smallest

incumbents.

Xerox and Honeywell each used a joint venture to enter the PC market. The joint venture

was not between these two incumbents but between each and an outside firm. Also of note is that both of these firms eventually exited the computer industry completely.

Only one firm, Sun Microsystems, utilized acquisition as a means of entering the PC

market. Although the firm was founded in 1982 and might be considered an entrant, the PC was

not the flagship product for the firm. It was not until 1989, following the acquisition of a patent, that it introduced a PC.

Correlation and regression analysis. As explained in Chapter 3, SPSS Version 10.1, a

statistical analysis package, was used to check for correlation between variables and to build a linear

regression. As an additional test for an association between each independent variable and the

dependent variable, a Pearson correlation test was conducted. This method was also used to determine if a relationship existed between each of the independent variables.

One of the primary goals of this research was to determine if any of three variables influenced a firm’s performance when the firm faced a disruption. Regression analysis was

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conducted using the data from the computer industry to determine if response, timing, or size, correlated with future firm performance (ROA). If such a relationship was found, the understanding of the influence of the particular independent variable(s) on the ROA could be helpful for firms involved in disruptions. The regression model indicated, based upon a very small coefficient of determination (R2), that little to no relationship existed between the independent and dependent variables.

This lack of relationship indicates a lack of support for Propositions 7, 8, and 9. Based upon the proposal, it was expected that there would be a correlation between how incumbent firms responded to the nascent technology and the company’s ROA. The expectation was that incumbent firms utilizing a Strategic Business Unit (SBU) to introduce the nascent technology would realize, on average, better ROA (P7) (Gilbert and Bower, 2002). This was not the case. However, the

assumption regarding the SBU and positive performance was not actually tested since none of the

firms in the sample utilized this approach. It was interesting to note, however, that in 2002 the top market share holders among PC vendors were not incumbents. Dell held 27.9 percent and Hewlett-

Packard/Compaq held 16.8 percent. Although Hewlett-Packard initially used R&D to enter the market it had not previously been in mainframe computers; it had been in printers and peripherals.

IBM held the fourth largest share at 5.3 percent in 2002, a substantially smaller share than either of the market leaders. Furthermore, IBM was the only true incumbent to occupy any of the top five

market share positions (Thompson and Gamble, 2003). This leads one to speculate that a different

strategic response may have lead incumbents to greater success with the new technology. Had IBM

or one of the other incumbents, created an independent SBU to manage the PC business, would the

outcome have been different? A market that included many companies that created an independent

SBU might be difficult to find, as it seems that giving up the control is often a stumbling block for incumbents.

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Proposition 8 was based largely on Schnaars’ (1994) findings that imitators, not pioneers had the advantage when new technologies were introduced. The proposition speculated that later responses to the disruption would correlate with stronger performance. Based upon the computer industry, no correlation was found. In the case of the computer revolution, Schnaars’ conclusions cannot be supported. The data did not contradict Schnaars; however, they did not suggest that earlier entry was correlated with better ROA as he had asserted.

Finally, based upon the findings of Christensen, Suarez, and Utterback, (1998), P9 proposed that larger firms would fare better than smaller firms. Again this was not supported by the data from the computer industry. There were no contradictory findings; there was simply no correlation between the size of the firm and the ROA.

A Pearson correlation test was conducted to further verify the relationship, or lack of relationship, between each of the independent variables and between each independent variable and the ROA. The results of these tests aligned with the results of the regression analysis. No significant correlation was identified. The implications of the lack of correlation and the lack of support for the Propositions are discussed in Chapter 5.

The lack of findings utilizing these tests instigated further investigation through the use of non-parametric tests, with the goal of offering an alternative view of the relationships. There was one correlation, between the size of the firm and the type of response that was determined to be significant at the .05 level using Kendall’s tau and Spearman’s rho. It is reasonable to expect that the larger, incumbent firms were more likely to utilize internal R&D to enter the market and that the smaller firms were the entrants. The non-parametric tests supported this expectation. Despite this finding, the lack of correlation between independent and dependent variables was the more important outcome of these tests.

CHAPTER 5

CONCLUSIONS AND STRATEGIC IMPLICATIONS

In this chapter Eisenhardt’s (1989) iterative approach is again employed in order to enhance the theory-building efforts of this research. Historical disruptions studied by previous researchers are reconsidered using the categorical distinctions established in this study. The second section draws conclusions about firms’ strategic responses and the impact on their performance. Strategic

and managerial implications are then discussed followed by the limitations of the study and

opportunities for future research.

Disruption Range

Interpreting the Analysis

The detailed study of the three foundation industries used herein revealed that there are degrees of competitive disruption created by new technologies. Understanding the competitive dynamics was not as simple as qualifying upheavals as disruptive or not. From this research three categories of disruption were identified and differentiated from sustaining technologies:

a) Zero-sum Disruption

b) Competitive Disruption

c) Positive-sum Disruption

Each category had a different impact on the existing market. (Other types of innovations exist, such as fads or failures, but these have minimal impact and are therefore not considered here.

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As exhibited in Figure 20, page 93, zero-sum, competitive, and positive-sum disruptions caused the incumbent industry to be, at the least, transformed or, at the most, nearly fully replaced. These are therefore qualified as radical technological change. Sustaining technologies are those that created incremental or generational change.

In the following section, historical disruptions evaluated by previous researchers (Cooper

and Schendel, 1996; Schnaars, 1994; Shanklin and Neff, 1997; and Christensen, 1997) are

reconsidered using the categorical distinctions established in this research. Upon reexamination of those historical accounts, it was determined that all fell into the categories of zero-sum or sustaining technology; none qualified as competitive or positive-sum. Some would therefore question the legitimacy of creating the latter categories and delineating the differences. If one considers the foundation industries that qualified as competitive or positive-sum, it is apparent that understanding the nature of these disruptions is important for strategists at incumbent firms and those companies

just entering a market. Less frequent occurrence does not reduce the reality and challenge that the

impact creates, nor does it eliminate the need to understand these types of discontinuity. It is likely

that competitive and positive-sum disruptions have been theoretically underdeveloped because they

occur with less frequency. The lack of theoretical development indicates a need for more

investigation and analysis.

Zero-sum Disruptions A zero-sum disruption is clearly illustrated by the example of

ballpoint pens, and the impact this product category had on the demand for fountain pens. Although

the ballpoint pen initially was of lower quality and higher cost than fountain pens, the new technology progressed to the point where the ballpoint pen virtually replaced the fountain pen, forcing the latter into the realm of an exclusive, novelty, low-volume item. Although this change in

consumer-product technology did not have a far-reaching impact on the state of the economy, the effects were considerable for those firms producing fountain pens and ballpoint pens. The impact

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on both employees and owners of fountain pen manufacturers was devastating, while those involved in the production and supply of ballpoint pens enjoyed great success.

This product category and many others were studied by Cooper and Schendel (1976) as examples of industries subjected to technological threat. Using the data from their work and the

anatomy of disruption developed here, many of the industries they studied qualified as zero-sum

disruptions. Locomotives have been replaced by diesel-electric trains, vacuum tubes by the

transistor, and propeller planes by jet engines. In all of these cases Cooper and Schendel described

the entrant as “limited in application or crude at first” (p. 64). In each case, incumbent sales fell and

the entrant eventually surpassed the incumbent. Although Cooper and Schendel did not consider

the measure of whether incumbent sales fell to less than 50 percent of peak years sales, it is fair to

judge from the historical record that this did happen. The understanding of the potential of the

technologies that disrupted these industries could have lead to very different managerial decisions

on the part of incumbents and entrants. Incumbent strategists might have more effectively managed

their dependence on the mainstream customer base, while determining other avenues for the growth

and health of the organizations. With a keener understanding of the magnitude of change that was

underway, entrant firms might have more boldly pursued market share growth or other means of

expansion.

None of the industries studied in this dissertation actually qualified as zero-sum disruptions.

Most were in a state of change for decades, and yet the incumbent industries persisted. In all cases, the incumbent industries were forced to change, and in some cases were severely transformed, but in no cases did the entrant supplant or nearly fully replace the incumbent. Therefore, the description of this category was crafted from extant literature rather than in terms of the propositions and variables studied in this research. However, setting the boundaries of the other categories of

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disruption helped to identify what zero-sum disruption was not, and therefore a more precise understanding of discontinuities of all kinds was gained.

Competitive Disruptions. Although the data reported were not specific, Cooper and

Schendel’s (1976) investigation of the discontinuity created by the introduction of vinyl as a substitute for leather satisfied, in broad terms, the parameters established herein for competitive disruptions. The incumbent revenues did decline over time. However, while vinyl served to replace shoe sole leather, it did not replace leather in many other applications. Clearly, this disruption was not zero-sum as worldwide sales of leather in 2004 continued to be substantial. Yet

the sudden and continued fall in leather sales, the relative inferiority of vinyl, and the appeal to non-

mainstream segments tend to qualify the disruption as competitive.

The U.S. Postal Service provided another example of a hazardously disrupted incumbent.

The widespread use of electronic mail and electronic transactions has diverted business away from

this huge institution. The new method of communication took hold in small segments and crept

through submarkets, eventually becoming quite common and encroaching upon the use of the postal

system. The United States Postal Service delivered nearly 200 billion pieces of mail in 2003, yet

over a three-year period this represented a 5-billion-piece drop in volume and a revenue decline of

over $4.5 billion (Potter, 2004). Despite this precipitous drop, nearly 200 billion pieces of mail are

still delivered annually. It is clear that the incumbent in this case has not been obliterated, nor does there seem to be a threat of the postal service becoming obsolete, but the existing technology has certainly been threatened and challenged by the hazards of the new technology.

Competitive disruptions are created by new offerings that promise performance that is unlike the incumbent and peaks the interest of a non-mainstream customers that are under-served or over-served by the current technology. This description matches closely with Christensen’s

explanation of value networks. The new customers to whom the nascent technology appeals are

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outside the incumbent’s value network. They are beyond the reach of the known supply chain and are foreign to the established customer performance preferences. However, with competitive disruptions the entrant firms stop short of eviscerating the incumbents. They hurt the existing firms

and cause them to radically change their approach, but they do not disrupt in a zero-sum sense.

They do not cause the obsolescence or near disappearance of existing product categories or

technologies.

According to the research conducted here, the industries that incurred this type of

disruption were characterized by increases in density of competition, but the intensity may have

increased in the latter part of the time period. Incumbent revenues fell drastically to less than 50

percent of peak year sales as entrant revenues accelerated over time and surpassed the incumbent.

Positive-sum Disruptions. Positive-sum disruptions involve new technologies that

compete alongside the incumbent in what Adner (2002) referred to as competitive isolation. The

incumbent and entrant technologies have unique market segments that each satisfies with some

overlap between the two sets of customers. An example of this would be the introduction of

Personal Digital Assistants (PDAs). The first PDA was introduced in 1984 by a software company.

The original design was intended to help individuals get organized by combining capabilities that

included electronic address books, clocks, task lists, and date books among other things. PDAs

have been developed over time and now have many capabilities in common with personal

computers (PC), but are not a threat to PC manufacturers. There is some overlap in demand for the

two technologies. Although many consumers have both, some have only a PC, and a much smaller segment has only a PDA. Despite the similarities of the function of the two, the threat of the PDA

disrupting the PC market is small.

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The data in this study showed that, in the cases of video rentals and mobile telephony, discontinuities actually enhanced the revenues of the respective incumbents. Specific research should be conducted to confirm the assumption that the PDA had a similar impact on sales of PCs.

Positive-sum technologies create a discontinuity for incumbents in that the new technology causes incumbent firms to transform the way they do business, but in the end the introduction of the new technology increases sales of the incumbent. As discussed in the next section, sustaining

technologies prolong the life cycle of the incumbent by improving and extending the capabilities of

the incumbent technology, but in the end the iteration or new variant of the existing product

replaces the original version. This is not the case in positive-sum disruptions. The new offering is

discontinuous, it is not built based upon the incumbent technology; it is actually a nascent

technology, an invention.

The research conducted here illustrated that positive-sum technologies tend to occur in conjunction with increases in density and intensity of competition. Increases in entrant revenues are gradual initially and then accelerate and eventually surpass incumbent sales, but incumbent sales decreases are not so significant as to reduce them to less than 50 percent of peak year revenues.

Sustaining Innovations. At the opposite end of the spectrum from zero-sum disruptions,

were new products that extended the capabilities of the incumbent and caused the market to

continue a growth pattern. Christensen called these sustaining new technologies. The transition in

telecom from analog to digital technology that was mentioned in Chapter 3 was an example of an

innovation that sustained the growth of existing competitors. Digital added superior quality to the

existing product in the form of greater clarity and consistency of connections. This improvement

was meaningful to the mainstream customer base and the price of the improved technology was

comparable to the existing technology. The improved quality also drew in new customer segments.

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An important distinction between sustaining innovations and positive-sum innovations is that the

former replace the existing technology while positive-sum innovations co-exist with the incumbent.

Intel’s continuous development of new generations of microprocessors, from the 286

through the 486, and Microsoft’s progression through the versions of Windows both represent incremental change and sustaining innovations. In each of these cases, shortly after the new technology was developed, the production of the original technology ceased. These new generations of products provided added uses for the existing technology or increased the customer base and therefore sustained, rather than disrupted, the growth of the incumbent. These changes were incremental to the incumbent rather than radical. Although they eventually replaced the incumbent, they were usually introduced by the incumbent firm and were therefore very manageable for these companies.

Applying the Interpretation to the Selected Industries

The industries in this research, when closely studied, actually provided two examples of competitive disruptions (TVs and PCs) and two of positive-sum disruptions (home video viewing

and cellular telephones).

Competitive Disruptions: Television and Personal Computers. Of the foundation

industries the disruption of the motion picture industry by TV proved to be the most threatening, but

stopped short of zero-sum disruption. Drops in admissions, box office revenue, and number of

firms competing for the market were significant. There was fear among motion picture providers

that the demand for movies would cease, and that the TV would replace moviegoing. After many

years of steady decline, the saturation of U.S. households with TVs was complete and the demand

for movies leveled off. The motion picture market remains, and the residual is a healthy, major

industry in the United States and around the world.

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The same is true for mainframe computers and PCs. The radical upheaval of the market

cannot be denied. However, after experiencing significant declines in revenues and adjusting their businesses to the new competitive structure, mainframe firms adapted to the onslaught of PC technology. Many, as stated previously, failed in the attempt to adjust, but others prevailed and new firms were founded. Today, mainframes provide an important function for businesses around the world, while sustaining new technologies continue to aid in the growth of both mainframe and PC technologies.

Positive-sum Disruptions: Home Video and Cellular Telephony. It is understandable that when the VCR was invented, movie theatre operators anticipated a decline in demand similar to the one experienced in the 1940s and 1950s following the introduction of the TV. Because demand for movies in the 1970s was already low relative to the 1940s, many assumed that another sizeable slide was eminent and would be catastrophic for movie theatres. This, however, proved to be wrong. In fact, the VCR and availability of home movies had a positive effect on movie admissions and box office revenues. Therefore, this new technology, which was seen as threatening and probably disruptive, had a positive impact on the incumbent. Based upon the trends in admissions and revenues during the emergence and establishment of the VCR, this new technology paralleled an increased demand for moviegoing.

In telecommunications, revenues for cell phones increased significantly and radically

changed the industry. Although the entrant sales eventually surpassed the incumbent sales, wireline

recovered and sales actually began to increase after the initial decline. The competitive pressure has

not resulted in the nearly full substitution of cell phones for landline phones. The data show that

usage and revenues for the incumbent technology, wireline, have actually increased since the

introduction of cell phones. This disruption was therefore qualified as positive-sum.

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Strategic Response in the Computer Industry

The most remarkable thing about the findings of the statistical analysis was the lack of

significant findings. The only significant finding (at the .05 level using Kendall’s tau and

Spearmans’ rho) was between the size of the firm and the type of response. Larger firms, the

incumbents, tended toward internal R&D while the smaller firms, of course, were the entrants. It is

reasonable to see how size and type of response were related.

A more important finding was that there was no correlation between any of the independent variables and the dependent variable, return on assets. Several insights can be interpreted from this

lack of correlation. Unless the new technology involves huge capital start-up costs, size is not

destiny. Researchers have developed significant theory around the subject of entry barriers and the

significant role that assets, resources, reputation, and capital play in helping large, established firms

and thwarting smaller ones (Christensen, 1997; Bain, 1956; Caves and Porter, 1977). The

significance of size was not supported by the data from the computer industry during the PC

revolution.

The conclusions drawn in this research align with some of Christensen’s findings.

Christensen found that entrant firms have an advantage in pioneering in disruptive markets because

they are pursuing a course of action and business model that is unmanageable for incumbents. By

definition, incumbents have been in existence and time allows them to grow in size and power. The

considerable resources that the incumbents possess, which could be used to create barriers for the

upstart firms, are not employed because the inferior, more expensive technology does not fit the

existing firms’ need. The firms need to satisfy investors and customers, and the existing product

line does that. The firms need to protect their businesses from competitors and do not view the

crude, new products and upstart companies as competition. The considerable strengths of the large companies are not marshaled against the entrants because the nascent technology is considered

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inconsequential using the incumbents’ paradigm. Factoring in Schumpeter’s (1950) assertion that incumbents have the advantage when development of the nascent technology requires high capital investment tempers Christensen’s assertions. Perhaps the explanation for why size did not correlate to success in this study was that firms that had a size advantage did not use it. Another explanation could be that firms could enter the PC field without significant capital investment, as design, assembly, and sales of PCs were based more on intellectual capital and operating costs than financial capital. In this research, size was not a consistent predictor of firm performance.

Schnaars noted a nearly perfect, inverse relationship between order of entry and success in

the market in the case of ballpoint pens. In aggregate, the examination of 28 cases of new

technology introduction revealed 19 cases in which the large firms replaced the small, pioneering

firms. In no cases were the large firms the pioneers; nor were there any cases where the small firm

replaced the large. In the research conducted for this paper, the firms that entered the PC market

early (pre-1982) achieved a negative average ROA of -6.95. The later entrants’ average ROA was

positive 3.21. Qualitatively this data appeared to support Schnaars’ conclusions regarding imitators fairing better than pioneers. However, the statistical analysis of the computer industry data revealed

no significant correlation. The statistical significance of Schnaars’ work is unknown; because the

research was descriptive and qualitative, no statistical analyses were conducted.

Further inquiry drove the test for a relationship between time of entry and continued

participation in the new technology. (Did the firm continue to produce or market PCs at the time

the research was conducted?) There was no correlation between the year that firms introduced a PC

and whether or not those firms were still offering PCs in the marketplace in 2005.

Larger firms tended to use internal R&D as a means of developing emerging technologies,

but there is no evidence that internal development was the only choice or the best choice. From the

data, it is reasonable to say that no one strategic response was better than another, leaving it open

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for the incumbents to acquire the needed technology, enter through a joint venture, or utilize the customary means of research and development.

Strategic and Managerial Implications

Eye of the Beholder, Redux

The categorization of disruptions has meaning for both entrant firms and incumbents. The true meaning of the word disruption, to break or split apart, is reinforced by the findings of this research. New technologies do create a splintering of markets. Sometimes that splintering is destructive to incumbent firms and incumbent technologies, other times it is less so. In all of the categories created by the disruption, the innovative technology supplies firms, both incumbent and entrant, with new and different opportunities.

The category of disruption that is created is still a function of the five elements examined throughout this work: incumbent firms, incumbent technology, entrant firms, nascent technology, and segmentable customer base. As stated previously, it is a convergence of these factors that creates disruption. Therefore, since no one competitive entity controls all five factors, the type of disruption that ensues is largely out of the control of the strategist or entrepreneur. However, firms’ understanding of disruptive technologies can be strengthened by the information provided from this research. From the perspective of incumbents, the understanding that some, but not all disruptions may become destructive could force the firms to be more objective about determining the potential impact of an innovation. Core rigidities might be more successfully managed with additional data to bolster the firm’s analysis of a new rival. For example, although in this research cell phones were found to enhance the market for wireline phones, AT&T was devastated. Once AT&T sold the wireless unit of the business, the remaining firm could not adjust to the new competitive

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structure. The question remains: if the monolith had realized the positive-sum nature of cell phones, could it have behaved differently and fared better?

From the eye of the entrant firm, the clearer understanding of the types of disruptive technologies allows them insights into the relative promise of the invention. A nascent technology with the potential to destroy the existing technology would be managed differently from one that has a positive-sum or competitive potential. Research in this area aids the entrepreneur in understanding that it is not up to them to determine if their invention will destroy. It is the convergence of the market factors that combine to create the different types of disruptions.

Expansion of the Implications

This research adds clarity to the phenomenon known as disruptive technologies. While

researchers continue to study markets for an ever more precise understanding of technological discontinuities, strategists and managers must face disruptions and determine appropriate actions.

Although the results of this research are not statistically generalizable, they do suggest to managers that more than one type of discontinuity exists; managers should consider this when trying to gain an accurate understanding of the market in which they compete.

This study reinforces that which has been suggested by researchers before: when in business look carefully at would-be substitute technologies. Porter (1980) outlined five forces of competition that serve to pressure the profitability of firms: rivals, buyers, suppliers, potential new entrants, and substitutes. Managers must watch not only immediate rivals, suppliers, buyers, and potential new entrants, the obvious current competitors, but must observe technologies and companies in peripheral markets. The difficulty for managers is that there can be many peripheral markets and watching all of them can prove difficult or impossible.

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From the literature review it became apparent that the customer base is a good indicator of which peripheral markets to watch. An important method of monitoring developing technologies is

to carefully observe the behaviors of and new product adoptions by various segments of customers.

Those segments that are over- or under-served by the current, dominant design should be

thoughtfully monitored. Those that are on the fringe, that are often discounted as unimportant to

incumbent industries because they do not contribute significantly to revenues, should be watched

vigilantly. These segments hold extraordinary value as leading indicators of change relative to new

technologies.

The term inferior has been used to describe new disruptive innovations, but this is a relative term applied when the new technology is considered through the eyes of the mainstream customer base. Managers must watch the segments of the customer base that do not have the same needs as the mainstream. These are the customers that are less loyal to the dominant design and more willing to try something new. By monitoring the interest of these groups and their adoption of innovations from outside the mainstream product offering, firms have the opportunity to anticipate discontinuities earlier in the technology cycle. This is not to suggest that the incumbent firms will be able to stop the disruption. Based upon historic examples, one fact appears undeniable,

disruptive technologies will persist. However, observing and correctly interpreting the leading indicators gives incumbent managers more time to construct a thoughtful and appropriate strategic response.

Although results of case analysis are not statistically generalizable, they do enlighten

theory, and the results can be used to provoke managers to ask important questions as a disruption

unfolds. Managers must be brutally honest when analyzing:

• The interest level of all customer segments, not just the mainstream customer.

• The rate of sale of the entrant technology.

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• The rate of sale of the entrant relative to other technologies that have threatened the incumbent historically.

• The changes in the rate of sale of the incumbent technology.

If, over time, a variety of threats confront an incumbent technology, differing rates of adoption may occur. These relative differences could be indicative of the difference between a zero-sum, competitive, or even positive-sum technology. Managers experiencing increased density, decreased intensity, decreasing incumbent revenues, and increasing entrant sales in the face of a new technology might, at some level, consider the possibility that a disruption could be at hand.

The results of this research reinforce this logic and suggest that managers consider carefully the rate of change in incumbent and entrant sales. If the incumbent revenue growth is slowing but does not appear to being dropping precipitously, the managers may be facing a positive-sum disruption rather than one that is zero-sum or competitive.

If the changes in intensity and density are accompanied by a precipitous drop in incumbent sales, the threat is likely to be one of zero-sum or competitive disruption. The managers facing these more destructive disruptions do have options. According to the statistical analysis of the computer industry, there was no one acceptable response or timing of response to these disruptions.

That is, incumbents must accept that the demand for the existing technology will significantly decline. A firm should consider its capabilities and determine the best approach for replacing the lost revenues. It would be unwise, however, to abandon the existing technology for the nascent one as the innovation may prove to be competitive rather than zero-sum. If revenues of the incumbent technology drop below 50 percent of the peak year sales and then begin to flatten, there is a chance that the demand for the incumbent will persist for years to come. In these cases, the cases of competitive disruption, firms need to determine the best ways to co-opt the new technology while defending or growing the firm’s share of the shrinking market for the incumbent technology.

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Should Me r rill-Lynch, for example, abandon its business model to become an electronic-

trading firm? Now, it is obvious that it should not; there is still a great deal of profit to be made

from the business model the firm traditionally used. However, when electronic-trading first

appeared in the market, many would have advised Merrill-Lynch to discard the traditional model in

favor of the new.

If the established firm believes, due to the continued, precipitous decline in revenues, that

the disruption will prove to be zero-sum, then the strategy changes to one aimed at harvesting what

can be salvaged from the existing products and rapidly developing new capabilities in other

markets.

Although more research is needed, this study sheds light on the nature of different types of

disruption. It suggests the questions that managers should be posing as they monitor rivals,

mainstream customers, and those customers who are on the margin and may very well lead change.

Future Development of this Research

Based upon this research, an improved, more complete understanding of disruptive technology was developed. The research proposal was to better understand the range of time during which markets are disrupted. In the end, the industries studied helped to identify the different types of disruption and created several new avenues for inquiry.

The makeup of the customer base has critical influence on entrant technologies and incumbents alike. Further study of the differences in demand for products under conditions of zero- sum, competitive, and positive-sum disruption should be investigated in search of additional descriptive detail that would help categorize a nascent technology. For instance, what was it about home video viewing that eventually lead to an increase in box office sales? Are there similarities in the demand curve for cell phones versus wireline and for movie rentals versus moviegoing?

122

Did the incumbent’s reaction to the disruption in these markets help to create the enhancing

effect or is it the nature of the nascent technology? With these different categories of disruption

identified, it would be helpful to test the strategic response of firms during different types of

disruption. Do any of the variables tested here matter significantly if a firm is experiencing a

positive-sum disruption rather than a competitive one? Does the strategic reaction, size, or timing

make a difference in future firm performance if the firm is undergoing a zero-sum reaction versus a competitive one? For instance, in the case of wireless disruption of wireline, the disruption may have been positively affected by the incumbents’ response to the new technology. Many wireline firms co-opted the cellular technology while significantly lowering the price of wireline. The price

elasticity of wireline and the aggressive price adjustment made by the incumbents may have lead to

the overall increased usage of phones of all types. By comparison, the U.S. Postal Service reacted

to the increased competition created by email and overnight carriers by increasing the price of

postage. The competitive disruption in the delivery of mail may or may not have been impacted by the choice of the response. The positive-sum disruption that came about after the advent of cell

phones may or may not have been affected by the response of the incumbents. Herein lies an

opportunity for future research.

In-depth analysis into each of the categories of disruption could prove enlightening. Other

variables could be tested to determine if they vary differently depending on the type of

discontinuity. Further research should be conducted to see if there is a property of the technology

that leads to that type of disruption. This might reveal that a property of technologies that cause

competitive disruptions is that they are slower to evolve and therefore slower to displace the

incumbent, for example.

In the qualitative analysis, there were three incumbents that did not enter the PC market and

ceased to exist sometime after the PC revolution. They were not included in the statistical analysis

123

because information regarding critical independent and dependent variables could not be located.

The disappearance of three incumbents that did not enter the PC market and subsequently went out

of business leads to speculation that no entry was a bad strategy. This was not born out in the

statistical analysis but may indicate an opportunity for further research.

The primary limitation of this work is the one indigenous to any case study: the results are

generalizable to theory only, not to general populations. This does not remove the value of the

findings, but does limit the application. Another limitation is that none of the industries studied fell

into the category of zero-sum disruption. It would be advantageous to describe this category in the

same terms used to describe the competitive and positive-sum disruptions. To do so, an industry or

industries that have incurred a zero-sum disruption and about which sufficient data are available

should be analyzed. Finally, a limitation to the investigation of the strategic response and its effect

on firm performance is that no firms in this study used the Strategic Business Unit (SBU) response

to a disruption. When planning the study, it was assumed that this would be a significant variable

that would separate firms’ performance. Additional work in markets where the SBU approach was

used would be revelatory.

APPENDIX A: DISRUPTION RANGE DATA SOURCES

Disruption Range Data Foundation Industry #1: Telecom Incumbent = Wireline Entrant = Wireless Data Source PROPOSITION #1 Population Density Shares of Total Toll Service FCC, SCCC Table 1-5 # of Telecom Providers Dun's Business Rankings

PROPOSITION #2 Population Density Shares of Total Toll Service FCC, SCCC Table 1-5 # of Telecom Providers Dun's Business Rankings

PROPOSITION #3 Population Intensity Shares of Total Toll Service FCC, SCCC Table 1-5

PROPOSITION #4 Incumbent Revenues Revenues FCC, 1995 SCCC Table 1.4, 1992-2002 TTS Table 15.4

PROPOSITION #5 Nascent Revenues Revenues CTIA 2004, Annual Wireless Survey

Foundation Industry #2 Incumbent = Motion Pictures Entrant = Television Data Source PROPOSITION #1 Population Density Theatre Industry - # of estbmts Gov't Statistical Abstract # of screens MPA Licensed TV Broadcasters FCC, Annual Reports

PROPOSITION #2 Population Density Theatre Industry - # of estbmts Gov't Statistical Abstract # of screens MPA Licensed TV Broadcasters FCC, Annual Reports

PROPOSITION #3 Population Intensity Not Available

PROPOSITION #4 Incumbent Revenues Box Office Gross MPAA

PROPOSITION #5 Nascent Revenues Broadcast Revenues, need 1966-1971 FCC, Annual Reports

124

125

APPENDIX A: DISRUPTION RANGE DATA SOURCES (CONT.)

Foundation Industry #3 Incumbent = Motion Pictures Entrant = Home Movie Viewing Data Source PROPOSITION #1 Population Density Theatre Industry Dun's Business Rankings Video Rental Business Dun's Business Rankings

PROPOSITION #2 Population Density Theatre Industry Dun's Business Rankings Video Rental Business Dun's Business Rankings

PROPOSITION #3 Population Intensity Theatre Industry Dun's Business Rankings Video Rental Business Dun's Business Rankings

PROPOSITION #4 Incumbent Revenues Box Office Gross MPAA

PROPOSITION #5 Nascent Revenues Video Rental Business Dun's Business Rankings

Revelatory Industry Incumbent = Mainframe Computers Entrant = Personal Computers Data Source PROPOSITION #1 Population Density Computer Industry Dun's Business Rankings

PROPOSITION #2 Population Density Computer Industry Dun's Business Rankings

PROPOSITION #3 Population Intensity Computer Industry Dun's Business Rankings

PROPOSITION #4 Incumbent Revenues Computer Industry Dun's Business Rankings Incumbent Revenues estimate derived from Dun's Business Rankings

PROPOSITION #5 Nascent Revenues Computer Industry Dun's Business Rankings Nascent Revenues estimate derived from Dun's Business Rankings

APPENDIX B: SOURCE DATA – FOUNDATION INDUSTRIES

SIC 4813 – Telephone Communications Except Radio Phones (not cell phones)

4813 Telephone Communications Except Radio Phones

(000) (000) (000) (000) (000) (000) (000) (000) (000) (000) (000) Sales Sales Sales Sales Sales Sales Sales Sales Sales Sales Sales 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 AT&T Comp 35,200,000 36100000 37285000 63089000 64904000 67156000 75094000 79609000 52184000 51319000 53223000 GTE Corp 16,500,000 17400000 18374000 19621000 19984000 19748000 --- 19957000 21339000 23260000 25473000 Bellsouth Corp 13,600,000 14000000 14345400 14445500 15201600 15800300 16844500 17886000 19040000 20561000 --- Nynex Corp 12,700,000 13200000 13585300 13228800 13155000 13407800 13306600 13406900 8752100 8752100 8752100 Bellsouth Telecomms Inc 7259500 12767400 13182100 13579600 14039900 14540000 14776000 15346000 16622000 Bell Atlantic Corp 10,900,000 11400000 12298000 12279700 12647000 12990200 13791400 13429500 13081400 30193900 --- At&T Comms Inc 14,300,000 11982000 11502600 11502600 11502600 11502600 --- Ameritech Corp 10662500 10818400 11153000 11710400 12569500 13427800 14917000 15998000 --- US West Inc 9,220,000 9690000 9957300 10577200 10293600 10293600 10953000 11746000 12911000 11479000 12378000 Pacific Telesis Group 9,480,000 9590000 9716000 9895000 9935000 9244000 9235000 9042000 6211100 6438600 6438600 Southwestern Bell Corp 8,450,000 8730000 9112900 9331900 10015400 --- Pacific Bell 8,750,000 8690000 8651000 8854000 8749000 8894000 8917000 8862000 6040200 9938000 --- Sprint Corp 8345100 8779700 9230400 11367800 12661800 12765100 14044700 9000000 17134300 MCI Comms Corp 7680000 8433000 8349000 11921000 13338000 15265000 18494000 30000000 --- US West Comms Inc 8080000 8100000 8092800 8164400 8323700 8655900 8998000 9284000 9831000 10083000 10882000 New York Telepohone Co Inc 7470000 7450000 7486300 7696800 7746400 7847000 7736600 7937100 4780600 7957300 8269900 MCI Telecomms Corp 1760000 2441400 7602000 8349000 --- Southwestern Bell Telephone Co 7260000 7370000 7465700 7424100 1960500 8072900 8376200 8937700 9733000 10313000 10752000 Sprint Comms Co LP 5040582 5377606 5369535 2827000 2827000 7000000 8000000 --- Tele-Comms Inc 3625000 3827000 3574000 --- New England T&T Co 3730000 3732300 3805700 3921200 4070200 4202200 4313800 2325700 4565700 4804500 NJ Bell Telephone Co 2890000 2990000 3090900 3098600 --- Bell Telephone co of Penn 2680000 2820000 3011200 3037500 --- GTE California Inc 2969061 2960588 2921018 2874778 2881730 2801333 3141166 3322436 3322436 Illinois Bell Telephone Co 2710000 2830000 2847100 2885900 2946400 4041000 3278000 3414000 3663100 3808200 4078700 Michigan Bell Telephone Co 2460000 2520000 2617900 2575300 2678900 2746800 2854700 2964400 3238300 3384800 --- GTE North Inc 2190000 2350000 2352312 2390016 2461347 2602247 2759905 2861163 2988803 3113636 3146900 Contel Corp 2960000 3110000 2801200 2287100 1190000 2287100 2287100 --- Ohio Bell Telephone Co 1990000 1990000 2011700 2014100 2041400 2100300 2178600 2213300 2260700 2339900 2389300 Bellsouth Enterprises Inc 1670000 2000000 2000000 19273000 1927300 1927300 2439600 2439600 2439600 2439600 TCI Development Corp 2000000 --- Chesapeake and Potomac Telephone Co of Maryla 1560000 1630000 2000000 1779354 --- Alltel Corp 1070000 1230000 1573785 1747764 2092120 2342087 2961717 3109725 3192148 3263563 Chesapeake and Potomac Telephone Co of Virgini 1540000 1570000 1654598 1715719 --- Southern NE Telecomms Corp 1619300 1632800 1614400 1653600 1717000 1838500 1941900 --- Southern NE Telephone Comp 1370000 1430000 1393600 1402600 1442400 1476300 1508500 --- GTE Florida In 1250000 1230000 1229279 1254541 1211115 1306522 1401948 --- 1575513 Centel Corp 1090000 1090000 1148323 1180527 1191421 1191421 1636000 1636000 --- GTE Southwest Inc 1210000 120000 1143273 1134028 1181785 1162365 1553441 --- 1787200 Cincinnati Bell Inc 90000 1012893 1087947 1136348 --- 1228223 1336100 --- US Telecom Inc 3446778 3446778 3729855 3729855 3835000 2827000 2827000 2827000 Bell Atlantic - NJ Inc 3149300 3218900 3384300 3438800 3584500 3753900 3603600 Bell Atlantic - Penn Inc 3127900 3192900 3355300 3427600 3535600 3320500 3320500 Bell Atlantic - Maryland Inc 1836939 1871682 1950542 2031405 2146910 2047900 --- Bell Atlantic - Virginia Inc 1748717 1829816 1940321 1984533 2156478 2071100 2071100 SBC Comms Inc 10690300 11618500 12669700 23500000 24856000 28777000 LDDS Comms Inc 1144714 --- Worldcom Inc 2220765 --- 4485130 --- Frontier Corp 2143691 2575569 2352886 2593558 Alltell Georgia Inc 3192148 3192418 3192418 Utelcom Inc 2827000 2827000 2827000 Bell Atlantic Network Services Inc 2000000 2000000 2000000 America Online Inc 1685228 2600000 4777000 MCI Communications Corp 5140000 6470000 --- 7711000 7711000 MCI Worldcom Inc 17678000 Alltell Communication Products Inc 6192536 Frontier Telephone of Rochester Inc 2593558 Qwest Comms Intl Inc. 2242700 Centurytel Inc 1577085 American Info Techs Corp 9900000 10200000 --- United Telecoms Inc 6490000 7550000 --- South Central Bell Telephone Comp. 4930000 5040000 --- Mountain States T&T Comp 3800000 3740000 --- US Sprint Comms Inc 3410000 4320000 --- General Telephone Co of CA 2890000 2970000 --- Northwestern Bell Telephone Comp. 2300000 2400000 --- Pacific NW Bell Telephone Co 2020000 2060000 --- Souther NE Telecomms Corp 1580000 1670000 1378900 --- Indiana Bell Tel Co Inc 986000 1020000 --- WI Bell Inc 959000 960000 ---

126 127

APPENDIX B: SOURCE DATA – FOUNDATION INDUSTRIES (CONT.)

SIC 4813 cont.

GTE South Inc 869000 920000 --- Southern Bell T&T 7070000 --- Northeast Comms of WI Inc 2750000 --- Bell Atlantic Enterprises Intl 1422600 --- Metromedia Inc 1000000 --- Central Telephone Company Inc 831697 ---

COUNT 41 44 41 41 40 38 37 37 37 35 34

--- designates that the firm that dropped was crosschecked in the alphabetic index and confirmed to be out. Confirmation was made in the first year a firm dropped, but not repeated for subsequent years. If a firm reappeared at a later date, they were once again included. Source: All SIC data in Appendix B fromDun's Business Rankings

128

APPENDIX B: SOURCE DATA – FOUNDATION INDUSTRIES (CONT.)

SIC 4812 – Radio Telephone Communication (includes cell phones)

4812 Radio Telephone Communications (includes cell phones) 1990 was first year for this SIC, prior to that cell svc group with comm, nec (4899) (000) (000) (000) (000) (000) (000) (000) (000) (000) (000) (000) Sales Sales Sales Sales Sales Sales Sales Sales Sales Sales Sales 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 GTE Moblenet Inc 2800000 3300000 686000 --- 2000000 --- McCaw Cellular Communications 311000 504000 1037453 1365571 1743336 --- US West Comms Group Inc 3468500 3468500 9484000 10319000 3468500 GTE Mobile Comms Inc 2000000 2000000 2347000 2549000 --- Airtouch Comms 1235400 1618600 2251700 3594000 --- Motorola Cellular Service Inc. 2081300 --- AT&T Wireless Services Inc 3470000 4330000 5400000 Airtouch Cellular 2200000 --- Airtouch Intl Inc 3594000 5181000 ITT Comms Infoi Svc 215000 COUNT 2 1 222035553 Source: Dun's Business Rankings

129

APPENDIX B: SOURCE DATA FOUNDATION INDUSTRIES (CONT.)

Company data not available for this industry at this time.

TV Broadcasting Year Industry Revenues 1940 $168,005,638 1941 $193,281,378 1942 $205,343,606 1943 1944 1945 $299,338,133 1946 $322,552,771 1947 1948 $8,700,000 1949 $34,329,956 1950 $105,914,968 1951 $235,684,000 1952 $323,594,000 1953 $431,777,000 1954 $592,937,000 1955 $744,700,000 1956 $896,900,000 1957 $943,200,000 1958 $1,030,000,000 1959 $1,163,900,000 1960 $1,268,600,000 1961 $1,318,300,000 1962 $1,486,200,000 1963 $1,597,200,000 1964 $1,793,300,000 1965 $1,964,800,000 1966 $2,203,000,000 1967 $2,275,000,000 1968 $2,520,900,000 1969 $2,796,200,000 1970 $2,808,200,000 Source FCC Annual Reports

Due to the age of the data certain annual figures were unavailable.

130

APPENDIX B: SOURCE DATA – FOUNDATION INDUSTRIES (CONT.)

Motion Picture Association of America US Theatrical Statistics 1946-2002 Box Office ($ Admissions Per Average Ticket Year MM) Admissions (MM) Week (MM) Screens Price Films Produced Films Released* 2000 7,660.7 1,420.8 27.3 37,396 5.392 683 475 1999 7,448.0 1,465.2 28.2 37,185 5.083 758 461 1998 6,949.0 1,480.7 28.5 34,186 4.693 686 509 1997 6,365.9 1,387.7 26.7 31,640 4.587 767 510 1996 5,911.5 1,338.6 25.7 29,690 4.416 735 471 1995 5,493.5 1,262.6 24.3 27,805 4.351 631 411 1994 5,396.2 1,291.7 24.8 26,586 4.178 572 453 1993 5,154.2 1,244.0 23.9 25,737 4.143 459 462 1992 4,871.0 1,173.2 22.6 25,105 4.152 324 480 1991 4,803.2 1,140.6 21.9 24,570 4.211 336 458 1990 5,021.8 1,188.6 22.9 23,689 4.225 346 410 1989 5,033.4 1,262.8 24.3 23,132 3.986 349 501 1988 4,458.4 1,084.8 20.9 23,234 4.110 479 510 1987 4,252.9 1,088.5 20.9 23,555 3.907 462 509 1986 3,778.0 1,017.2 19.6 22,765 3.714 406 451 1985 3,749.4 1,056.1 20.3 21,147 3.550 264 470 1984 4,030.6 1,199.1 23.1 20,200 3.361 262 536 1983 3,766.0 1,196.9 23.0 18,884 3.146 232 495 1982 3,452.7 1,175.4 22.6 18,020 2.937 169 428 1981 2,965.6 1,067.0 20.5 18,040 2.779 183 239 1980 2,748.5 1,021.5 19.6 17,590 2.691 214 233 1979 2,821.3 1,120.9 21.6 16,901 2.517 217 215 1978 2,643.4 1,128.2 21.7 16,251 2.343 241 191 1977 2,372.3 1,063.2 20.4 16,041 2.231 261 199 1976 2,036.4 957.1 18.4 15,832 2.128 232 220 1975 2,114.8 1,032.8 19.9 15,030 2.048 258 233

131

APPENDIX B: SOURCE DATA – FOUNDATION INDUSTRIES (CONT.)

Motion Picture Association of America US Theatrical Statistics 1946-2002 1974 1,908.5 1,010.7 19.4 14,417 1.888 242 278 1973 1,523.5 864.6 16.6 14,420 1.762 277 286 1972 1,583.1 934.1 18.0 14,428 1.695 319 317 1971 1,349.5 820.3 15.8 14,055 1.645 304 314 1970 1,429.2 920.6 17.7 N/A 1.552 279 306 1969 1,294.0 911.9 17.5 N/A 1.419 289 251 1968 1,282.0 978.6 18.8 N/A 1.310 294 258 1967 1,110.0 926.5 17.8 12,187 1.198 N/A 264 1966 1,067.1 975.4 18.8 N/A 1.094 N/A 257 1965 1,041.8 1,031.5 19.8 N/A 1.010 N/A 279 1964 947.6 1,024.4 19.7 N/A 0.925 N/A 242 1963 925.0 1,093.4 21.0 12,652 0.846 N/A 223 1962 874.9 1,080.1 20.8 N/A 0.810 N/A 237 1961 945.5 1,224.7 23.6 N/A 0.772 N/A 1240 1960 984.4 1,304.5 25.1 N/A 0.755 N/A 248 1959 1,006.0 1,488.2 28.6 N/A 0.676 N/A 254 1958 1,010.0 1,553.8 29.9 16,354 0.650 N/A 352 1957 1,078.0 1,727.6 33.2 N/A 0.624 N/A 382 1956 1,125.0 1,893.9 36.4 N/A 0.594 N/A 346 1955 1,204.0 2,072.3 39.9 N/A 0.581 N/A 319 1954 1,251.0 2,270.4 43.7 18,491 0.551 N/A 369 1953 1,339.0 2,630.6 50.6 N/A 0.509 N/A 404 1952 1,325.0 2,777.7 53.4 N/A 0.477 N/A 389 1951 1,332.0 2,840.1 54.6 N/A 0.469 N/A 433 1950 1,379.0 3,017.5 58.0 N/A 0.457 N/A 483 1949 1,448.0 3,168.5 60.9 N/A 0.457 N/A 490 1948 1,506.0 3,422.7 65.8 18,631 0.440 N/A 444 1947 1,594.0 3,664.4 70.5 N/A 0.435 N/A 426 1946 1,692.0 4,067.3 78.2 N/A 0.416 N/A 400 MPA Worldwide Market Research

*Includes new and reissued films. All distributors represented as of 1982.

132

appendix B: source data – foundation indsutries (Cont.)

SIC 7813: Motion Picture Production, not television (sample of data set)

(000) (000) (000) (000) (000) (000) (000) (000) (000) (000) Sales Sales Sales Sales Sales Sales Sales Sales Sales Sales 1982 1985 1986 1987 1988 1989 1990 1991 1992 1993 Walt Disney Company Sony Corporation of American Disney Enterprises Inc. News America Inc Fox Entertainment Group Fox Inc 742,400 2,657,741 Sony Pictures Ent. Inc 1,900,000 905,700 Time Warner Entertainment Company Inc 3,476,700 Paramount Pictures Corp. 1,000,000 ------1,280,000 1,310,000 News Publishing Australia Ltd 1,930,200 Universal City Studios Inc 500,000 1,350,000 1,430,900 1,430,900 Rank America Inc 500,000 600,000 600,000 New Line Cinema Corp 133,141 225,686 Columbia Pictures Industries 691,000 ------365,000 364,500 364,500 Fox Kids Worldwide Inc Fox Family Worldwide Spelling Entertainment Group Inc 122,748 Walt Disney Pictures and TV 383,800 Studios USA Inc Twentieth Television Twentieth Century Fox Film Corp 1,865,390 Amblin Entertainment Inc New Line Home Video Inc Turner Entertainment Group Inc Turner Entertainment Networks Inc MGM Inc 157,600 Saban Entertanment Inc Dreamworks LLC Primedia Workplace Learning Inc. News America Holdings Inc 1,540,000 3,500,000 4,291,822 4,281,822 Universal Studios Inc Seagram J E Corp News America Publishing 2,500,000 3,000,000 3,580,610 3,563,056 Hallmark Entertainment Inc Westcott Communication Inc Live Entertainment Inc Lucasfilm Ltd 160,000 Cinergi Pictures Ent Inc Group W Broadcasting Inc Warner Communications Inc 2,050,000 3,990,000 2,020,000 2,230,000 2,230,000 3,400,000 4,210,000 4,210,000 4,206,100 4,206,000 New World Communications Group Inc Showtime Networks Inc 421,000 420,000 1,132,000 113,200 Rank Video Services America 230,000 350,000 300,000 300,000 New World Pictures Inc/Entertainment Ltd. 188,000 188,000 ------MGM Group Holdings Inc MGM Holdings Inc Metromedia International group inc MCA Inc. --- 1,590,000 1,650,000 2,090,000 2,440,000 2,580,000 3,020,000 3,380,000 3,382,344 3,283,400 Fox Circle Productions WGBH Educational Foundation 121,047 121,047 Pacific Rim Productions Inc 122,800 --- Toe to Toe Productions Inc 113,200 Mark John Film Corp --- Sony Pictures of America --- Paramount Comms Inc 3,060,000 3,390,000 3,869,000 4,264,900 Andrews Group Inc 261,100 304,600 Whittle Communications Lmtd Partnership --- Orion Pictures Corp (7823) 469,000 485,000 584,153 143,700 Endline Productions Inc --- Children's TV workshop 103,975 Carolco Pictures Inc 103,000 165,000 --- 269,145 141,463 Goldwyn Samuel Company Inc --- Empty Chair Productions Propoganda Films Inc Nest Entertainment Inc Heritage Entertainment Inc 92,500 Lyrick Corp --- Warner Brothers Inc 1,200,000 --- 2,760,000 3,000,000 NA Spelling Aaron Productions 103,000 --- 158,025 --- Paramount Communications Reality Corp --- Warner Home Video Inc 113,200 113,200 Worldwide Televideo Enterprises Inc ---

133

APPENDIX B: SOURCE DATA – FOUNDATION INDUSTRIES (CONT.)

SIC 7841: Video Tape Rental

(000) (000) (000) (000) (000) (000) (000) (000) (000) (000) (000) (000) (000) (000) (000) (000) (000) Sales Sales Sales Sales Sales Sales Sales Sales Sales Sales Sales Sales Sales Sales Sales Sales Sales 1982 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 Blockbuster Entertainment Corp. 403000 632670 868003 1200494 na Blockbuster Videos Inc 600000 850000 --- 1300000 na 894400 894400 1655615 1865300 1865300 Blockbuster SC Video Holding Corp 178900 178900 --- MGA Inc 240000 240000 250000 250000 Movie Gallery Inc 240000 254395 260356 --- Hollywood Entertainment Corp 149430 302342 500501 763908 Wherehouse Entertainment Inc 327425 496459 Blockbuster Inc 3893400 Erols Inc 156000 --- Video Superstore Master Ltd. 89800 --- UI Video Holdings Inc 65600 --- UI Video Stores Inc 63500 --- Total: 000000245800100300016117708680032500494---10733001702730245235232035827269067 Count 0000002 2 41 21 2 5 4 5 5 1995 Blockbuster in the process of acquisition, therefore the count of '1' is an estimate

APPENDIX C: SAMPLE INTERVIEW GUIDE

TOPIC FOR DISCUSSION: Telecommunication Industry 1 Estimate the dawn of the age of the entrant technology as a commercially viable business. Explain.

2 Do you think the penetration of cell phones in the U.S. has reached its peak? CTIA - wireless subscriber penetration to increase to 73% by 2008. 73% of what, do you think? Do you know how this compares to the current penetration? Source?

3 Consider the attached table and categorize the FCC terms as best you can.

4 Consider Table 9.7 on page 5B, AT&T, MCI and Sprint = Long Distance, wireline, toll service? "all others…" used as surrogate for "wireless"? Please explain.

5 Tables 15.3 and 9.4, Total Toll Svc. Providers? What about holding companies? Table 9.1 includes wireless firms. How would you suggest counting # of providers? What about those listed in 9.7-9.9, scc table 1.1 and 1.2 What about tts vs sccc?

6 What do you think of using AT&T and AT&T Wireless as surrogates for the industry?

134

APPENDIX D: STYLIZED SUMMARIES

Telecom

450

400

350

300

250

200

150

100

0 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000

Combined Count Industry Intensity Wireline Revenues/Capita Wireless Revenues/Capita

Moviegoing vs. Video Rental

9,000 8,000 7,000 6,000 5,000 4,000 3,000 2,000 1,000 0 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 Box Office Adj. Revenues Video Rental Revenues Combined Count Industry Intensity

135 136

APPENDIX D: STYLIZED SUMMARIES (CONT.)

Moviegoing vs. TV

20,000 18,000 16,000 14,000 12,000 10,000 8,000 6,000 4,000 2,000 Adjusted Revenues (000,000) 0 1946 1947 1948 1949 1950 1951 1952 1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970

Box Office Adj. Revenues TV Broadcasting Adj. Revenues Count TV Broadcasters* Movie Screens

Computer Industry

18000 16000 14000 12000 10000 8000 6000 4000 2000 0 1982 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996

Industry Count Industry Intensity Incumbents Adj. Revs (/10) Entrants Adj. Revs. (/10)

APPENDIX E: STRATEGIC RESPONSE DATA SOURCES

Strategic Response Data Revelatory Industry Incumbent = Mainframe Computers Entrant = Personal Computers Data Source PROPOSITION #7 Strategic Response: Qualify each firm's response Archival Sources and Annual Reports Record each firm's post disruption performance Compustat

PROPOSITION #8 Timing: Estimate the timing of the firm's response Archival Sources and Annual Reports

PROPOSITION #9 Size: Record size of firm at time of response Annual reports based upon sales

137

APPENDIX F: SOURCE DATA – COMPUTER INDUSTRY

Firm Name Sources of Data Apple Computer Pollack, A. (August 23, 1981). Apple expects big earnings decline. The New York Times.

Linzmayer, O. (1999). Apple confidential [electronic resource] : the real story of Apple Computer, Inc. San Francisco, CA: No Starch Press. Commodore International Victor's Drive to be No. 3 in the U.S. (January 24, 1983). Business Week.

Burgess, J. (May 9, 1994). Adios, Amiga and Commodore. Washington Post. Compaq Hoovers Guide to Computer Companies 2nd Edition (1996) Austin, TX. Control Data Corporation Norris, W. C. (1983) New Frontiers in Business Leadership. Minneapolis: Dorn Books. Data General 1983 Annual Report

Hoover’s Guide to Computer Companies 2nd Edition (1996) Austin, TX.

Suppliers battle it out for space on the desk (August 8, 1983). Business Week.

Wierzbicki, B. (August 15, 1983). Data General plunging into the micro fray. Business Week. Dell Inc. Hoover’s Guide to Computer Companies 2nd Edition (1996) Austin, TX. Digital Equipment Corporation Annual report, 1982

Hoover’s Guide to Computer Companies 2nd Edition (1996) Austin, TX.

DEC yields to pressure, builds IBM compatible (November 19, 1985). InfoWorld. Hewlett Packard Annual report 1981

Hoover’s Guide to Computer Companies 2nd Edition (1996) Austin, TX.

Office Computers (August 13, 1981). New York Times. Honeywell Annual Report 1986

http://www.honeywell.com/sites/honeywell/ February 2005

Spencer, E.W. (1986). Honeywell after 100 years. Princeton University Press. International Business Machines Annual Report 1980

Annual Report 1981

138

APPENDIX F: SOURCE DATA – COMPUTER INDUSTRY (CONTD)

Litton Industries Annual Report 1982

O'Green, F.W. (1989). Putting technology to work: the story of Litton Industries. Princeton University Press. NCR Annual Report 1983 Osborne www.naturalscience.com February 2005 Sun Annual Report 1989

Hoover’s Guide to Computer Companies 2nd Edition (1996) Austin, TX. Tandy Annual Report 1977

Annual Report 1978

Pollack, A. (August 23, 1981). Apple expects big earnings decline. The New York Times. Texas Instruments Annual Report 1979

Pollack, A. (August 23, 1981). Apple expects big earnings decline. The New York Times.

TI gets set to move into home computers (March 17, 1979). Business Week. Unisys New Frontiers in Business Leadership (1986). Princeton University Press.

Wang Annual Report 1983

Kenney, C.C. (1992) Riding the Runaway Horse. Boston: Little Brown & Company Xerox Annual Report 1981

Pollack, A. (August 23, 1981). Apple expects big earnings decline. The New York Times.

Xerox stops making its micros (April 1, 1985). InfoWeek.

139

APPENDIX G: STRATEGIC RESPONSE DATA – COMPUTER INDUSTRY

FIRM RESPONSE SIZE TIMING ROA APPLE 1 $0 -5 (0.57) Burroughs/UNISYS 0 $2,850,000 10 (1.80) Sperry/UNISYS 0 $5,300,000 10 (1.80) COMMODORE 4 $0 -5 (88.02) COMPAQ 1 $0 2 4.64 CONTROL DATA CORPORATION 0 2,800,000 10 5.58 DATA GENERAL 4 $828,904,000 1 (2.93) DELL 1 $0 3 17.39 DIGITAL EQUIPMENT CORP 4 $3,880,800,000 0 (0.05) HEWLETT PACKARD 4 $3,578,000 -1 9.52 HONEYWELL 2 $5,378,200 4 7.53 IBM 4 $29,070,000,000 -1 7.42 LITTON 4 $4,941,820,000 0 4.34 NCR 4 $3,730,951,000 1 (5.41) OSBORNE 1 $6,000,000 -1 0.00 SUN 3 $1,786,536,600 7 7.23 TANDY 1 $949,267,000 -5 11.00 TEXAS INSTRUMENTS 4 $3,224,126,000 -3 3.84 WANG 4 $1,538,000,000 1 (6.21) XEROX 2 $8,691,000,000 -1 1.21 Entry Year: 1977 = -5 1978 = -4 1979 = -3 1980 = -2 1981 = -1 1982 = 0 1983 = 1 1984 = 2 1985 = 3 1986 = 4 1987 = 5 1988 = 6 1989 = 7 No Entry = 10 Response: Never Entered = 0 Entrant = 1 Joint Venture = 2 Acquisition = 3 Internal R&D = 4 Size Sales $s the year of entry or 1982 Sales $s if no entry.

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APPENDIX H: REGRESSION SUMMARY TABLES

Descriptive Statistics

Mean Std. Deviation N ROA -1.3545 21.22540 20 Entrant .25 .444 20 Jointv .10 .308 20 Acquis .05 .224 20 RandD .45 .510 20 Early .50 .513 20 Size 2.9E+09 6567256733 20

Correlations

ROA Entran Jointv Acquis Rand Early Size Pearson ROA 1.000 .219 .092 .095 -.317 -.183 .125 Entran .219 1.000 -.192 -.132 -.522 .115 -.247 Jointv .092 -.192 1.000 -.076 -.302 .000 .074 Acquis .095 -.132 -.076 1.000 -.208 -.229 -.041 Rand -.317 -.522 -.302 -.208 1.000 .302 .327 Early -.183 .115 .000 -.229 .302 1.000 .335 Size .125 -.247 .074 -.041 .327 .335 1.000 Sig. (1- ROA . .177 .349 .345 .086 .221 .300 Entran .177 . .208 .289 .009 .314 .147 Jointv .349 .208 . .374 .098 .500 .379 Acquis .345 .289 .374 . .190 .165 .432 Rand .086 .009 .098 .190 . .098 .080 Early .221 .314 .500 .165 .098 . .075 Size .300 .147 .379 .432 .080 .075 . N ROA 20 20 20 20 20 20 20 Entran 20 20 20 20 20 20 20 Jointv 20 20 20 20 20 20 20 Acquis 20 20 20 20 20 20 20 Rand 20 20 20 20 20 20 20 Early 20 20 20 20 20 20 20 Size 20 20 20 20 20 20 20

141

Model Summary

Adjusted Std. Error of Model R R Square R Square the Estimate 1 .461a .213 -.150 22.76581 a. Predictors: (Constant), Size, Acquis, Jointv, Entrant, Early, RandD

ANOVAb

Sum of Model Squares df Mean Square F Sig. 1 Regression 1822.169 6 303.695 .586 .736a Residual 6737.667 13 518.282 Total 8559.835 19 a. Predictors: (Constant), Size, Acquis, Jointv, Entrant, Early, RandD b. Dependent Variable: ROA

Coefficientsa

Unstandardized Standardized Coefficients Coefficients Collinearity Statistics Model B Std. Error Beta t Sig. Tolerance VIF 1 (Constant) .656 13.144 .050 .961 Entrant 11.932 18.180 .250 .656 .523 .418 2.391 Jointv 4.314 21.715 .063 .199 .846 .611 1.638 Acquis 4.663 26.336 .049 .177 .862 .787 1.271 RandD -7.878 17.270 -.189 -.456 .656 .351 2.849 Early -10.501 12.335 -.254 -.851 .410 .681 1.468 Size .000 .000 .331 1.195 .254 .789 1.267 a. Dependent Variable: ROA

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APPENDIX I: INDUSTRY EXPERTS

EXPERTS INTERVIEWED

Telecom

Regional Manager - Operations AT&T Wireless Communications District Market Manager Alltel Corporation Vice President – Public Affairs AT&T

Movie Industry

Vice President/Executive Director National Association of Theatre Owners

143

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