Technology, Innovation and Entrepreneurship
Part II: My Firm
By Patri K.Venuvinod
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Dedication
To Mrudula, my wonderful wife.
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Copyright Patri K. Venuvinod 2010
Published by Patri K. Venuvinod at Smashwords
Smashwords Edition, License Notes: This ebook is licensed for your personal enjoyment only. This ebook may not be re-sold or given away to other people. If you would like to share this book with another person, please purchase an additional copy for each recipient. If you’re reading this book and did not purchase it, or it was not purchased for your use only, then please return to Smashwords.com and purchase your own copy. Thank you for respecting the hard work of this author.
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Discover two associated titles by Patri K. Venuvinod at Smashwords.com:
Technology, Innovation and Entrepreneurship, Part I: My World, My Nation
Technology, Innovation and Entrepreneurship, Part III: My Startup
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Table of Contents
Chapter 9 – The Diffusion and Dynamics of Innovation
The Technology-Adoption S-Curve
Chapter 10 – Industry Development
The Value-Adding Chain of an Industry
Stages of Industrial Development
National Competitive Advantage: Porter’s Diamond
Chapter – 11 Competition: The Driver of Innovation
Chapter – 12 Competitive Forces
Porter’s Five Competitive Forces
Chapter – 13 Competitive Advantage and Positioning
Porter’s Generic Competitive Strategies
Chapter – 14 Strategy Development and Mapping
Dominant Schools of Strategy Development
Balanced Scorecard Strategy Maps
Chapter – 15 Research and Development
The Importance of Basic Research
A Two-Dimensional Model of Research
National Research Infrastructure
Changing Modes of Knowledge Production
The Rise of the Entrepreneurial University
National R&D Output Comparisons
Evolution of R&D Management Systems
Chapter – 16 Technology and Market Forecasting
Chapter – 17 Organizational Culture & Structure
What is Organizational Culture?
Hofstede’s Dimensions of Corporate Culture
Organizational Culture Profiles
Types of Organizational Culture
National Preferences towards Organizational Culture
Connect with Venuvinod (the Author) Online
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About this Book
Thomas Jefferson said "[E]very generation needs a new revolution." The revolution for the generations in the first half of the 20th century was socialism/communism. For the generations of the second half of the same century, it was the return to capitalism. For the current generation, it seems to be entrepreneurialism.
It has become clear in recent times that the key to economic growth is technology (T), that to technology growth is innovation (I), and a powerful contributor to innovation is entrepreneurship (E). Yet there seems to be a paucity of academic books covering the large variety of issues involved in TIE-exploitation in a contemporary manner. This book is the second part of a textbook-trilogy that seeks to fill this gap.
Part I is titled 'My World, My Nation' as it examines TIE interactions from a world-perspective but stressing nation-building. Part II (this book)—titled 'My Firm'—discusses how an established firm could prosper in the contemporary world of globalized competition. Issues of particular importance to the growing number of youth pursuing an entrepreneurial career are discussed in the third and final part—titled 'My Startup'.
The origins of this trilogy lie in the class notes compiled by the author while teaching 'Management of Technological Innovation' to science, engineering and business students at the bachelor's, master's, and doctoral levels. The final contents have been influenced strongly by the insights derived by the author while living in India, the UK, Hong Kong (including extensive travels to China), and the USA. Thus, rather than focusing just on the lessons to be learnt from the experiences of a developed country such as the USA (as most books on the themes examined do), this trilogy empathizes with the biases and concerns of the developed as well as developing parts of the world.
Among the topics examined in this book (Part II) are diffusion of technology, industry dynamics, competition, competitive advantage, competitive forces, strategy development, research and development, R&D management, technology and market forecasting, organizational culture, and organizational structure.
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Preface
The Indian town I grew up in had the distinction of housing what then was the largest sugar factory in Asia. Yet, the town didn’t even have a primary school which meant that I couldn’t receive formal education till I turned nine and was able to lug my school bag across water-laden paddy fields to a government run middle school in a larger, neighboring town. Fortunately, the informal education I received at my home was good enough to earn me entry directly into the fifth grade.
The difference between the two towns was palpable. In keeping with their rural setting, people in my school-town were mostly steeped in age-old traditions, and religious or caste rivalries. This was in sharp contrast with the people in the industrial township I lived who tended to temper blind belief with rationality, dogma with pluralism, and disorder with organization. This contrast provided me with my earliest practical lesson in the power of technology as a vehicle for bringing forth social transformation.
My technicism led to a dilemma, though, as I approached graduation from my high school and started thinking about what I could/should become. The choice was obvious for most of my classmates. A farmer’s son would become a farmer, a grocer’s a grocer, and a feudal landlord’s a landlord. Being a technologist’s son, none of these choices was immediately available to me. In any case, all were unexciting.
Meanwhile, independent India was struggling to find its road ahead. The “Father of the Nation”, Mahatma Gandhi, passionately advocated a bottom-up, village-oriented approach underpinned by altruism. Technology was accorded only a peripheral role, if at all.
But, Gandhi’s influence was already waning as that of Jawaharlal Nehru was rising. As India’s prime Minister for seventeen years, Nehru pursued a national development strategy based on socialistic principles and central planning. (During his formative years, Marx’s works were well-known while Schumpeter had not written his counter-thesis yet. Schumpeter gained some fame by the time Nehru became the prime minister. But, apparently, Nehru’s mind had set by then.) Nehru also acknowledged the central role of technology in development and created a range of public sector industries which became vehicles for technology transfer mainly from the Soviet bloc. Taking cue from this trend, I joined an engineering college in the state capital in the hope of eventually becoming a public sector employee.
One of the few non-technical subjects we had to study was Economics. One would have thought that the syllabus of this subject reflected the prevailing Marxist bias. As it happened, the books prescribed dwelt essentially on classical capitalism. Further, my teacher was an eloquent laissez-faire enthusiast. All this exposed to me to the flipside of Nehru’s strategy: it was ignoring the role of the individual through personal enterprise. In fact, individual entrepreneurship was being discouraged through elaborate licensing requirements. This didn’t bother me since I, like most of my compatriots, believed that no public good can come out of greedy individuals.
Immediately upon obtaining my engineering degree, I proceeded to one of the premier institutes of technology in the country to specialize in design and production engineering. The particular institute I joined was set up with Soviet collaboration, so a good number of my professors were from the U.S.S.R. I learnt a lot about mechanical technologies from them but little about the new developments that were occurring in electronics and computers. There was also little curricular emphasis on the human and market sides of engineering.
My association with Russians and the like didn’t end there as the UNESCO expert from the Soviet Union assessing my masters' thesis reacted favorably to it. He started persuading me to take up academic career at a newly established Regional Engineering College. The idea was that I would assist him on developing the curricula for eight post-graduate programs in technology across India. I agreed.
Over the next few years, I got associated with many more experts from the Soviet Union and Eastern Europe. From them I learnt more about technology and their countries where vertically integrated industries were producing the goods that the respective governments thought their citizens needed.
Next, I was selected to go to the U.K. as a UNESCO fellow to work on my Ph.D. The personal niche in technology (metal cutting) research I was to find there was to remain with me for the rest of my professional life. While in the U.K., I also spent some time at an ILO institute in Italy and secured a deeper appreciation of the role of technology in economic growth. These experiences helped me develop a more secular, and global outlook.
Upon receiving my research degree, I returned to my previous place of employment in India. The aura of my ‘foreign’ PhD helped intensify my research activity. It also made it easier for me to initiate several non-curricular learning activities amongst students. For instance, noting that the college’s curricula had not included management science as a subject of formal study, I organized interested students into what we called the Management Studies Group. Not everyone was happy, though, with our enthusiasm for management science. For instance, during an address to the group, the main message of our Principal was that ‘management’ was no more than a euphemism for worker-exploitation. Many others were also offended as the campus was rapidly becoming a hotbed of communism. The resulting tensions made me think about finding a place more conducive to academic pursuits.
A few years later, I moved to Hong Kong—then still a British colony. I worked at two different polytechnic-universities. At the first, I obtained a broad understanding of how Hong Kong ticked. Hong Kong was very different from India or the U.K. While India was still struggling to find its path and the U.K. was past its prime at least in terms of world domination in technology, Hong Kong was fast becoming a prominent ‘Asian Tiger’ despite being just a city state without any natural resources and little industrial history. It had already acquired international reputation in finance and manufacturing. In terms of manufacturing, it had developed well past the era of Productivity (P) into the era of Quality (Q). It achieved all this by pursuing free market capitalism based on thousands of horizontally integrated small and medium-sized private enterprises. The government assiduously pursued a hands-off policy believing that other social problems would be mitigated automatically as economic prosperity is achieved. The reliance on personal enterprise (entrepreneurship) seemed to infuse many a young person with confidence in the future. These observations made me more sensitive to the power of individual entrepreneurship in economic growth. I also became convinced of the importance of creativity and broad-based education in the preparation of youth for entrepreneurial careers.
All this preparation proved to be particularly useful when I became the founding head of the Department of manufacturing Engineering the at a newly formed polytechnic-university in Hong Kong. I promptly set in motion several curricular and pedagogic experiments. The results only confirmed my convictions.
My 25-year stay in Hong Kong also provided me with ample opportunities not only to learn about but also to interact with mainland China. When I first arrived in Hong Kong, China had just embarked on a journey that was to lift some half a billion people out of poverty within the next 30 years. I had the good fortune of being chosen as a member of the first international delegation organized by some Hong Kong elders to visit China after Deng Xiaoping had declared China’s “Open Doors Policy”. This was only the first of many similar trips to come.
When I first went to South China, I found the place in a shambles following the self-inflicted injuries during the Cultural Revolution. Yet, today, the region is a thriving industrial complex actively contributing to China’s well-earned reputation as the “factory of the world”.
As I noticed during my subsequent trips to different parts of China, this was mainly the consequence of technological advancement resulting from technology transfer underpinned by unprecedented openness. Equally importantly, it was because the government managed to release the entrepreneurial energies of individuals without putting overall political stability in serious jeopardy. China was also wise in adopting the unprecedented “one country, two systems” policy with regard to post-1997 Hong Kong. The policy has already yielded rich dividends—Hong Kong’s industrialists have been providing between 50 and 70% of FDI in China.
The above political developments suggested to us that our department’s programs and curricula would have to recognize not only the local aspirations of Hong Kong but also how the territory could contribute to the rest of China. In particular, we had to take into account the fact that Hong Kong needed to move on to the era of Innovation (I). Keeping this in mind, we sought to broaden our program portfolio beyond manufacturing engineering in a manner that would enable students to equip themselves for the coming era of innovation and entrepreneurship. We also introduced, for the first time in Asia, a bachelor’s program in Mechatronic Engineering and a master’s program in Engineering Management. The former emphasized the design of products and processes involving the integration of mechanical, electronic and computer elements. The latter sought to convert engineers into managers capable of conceiving and operating technology-intensive firms and startups. For over ten years, I personally taught the subject of Management of Technological Innovation (MTI) to both engineering and non-engineering students drawn from sub-degree to doctoral levels.
A major problem I encountered while teaching MTI was that there was no suitable textbook to support my teaching. Whereas I was seeking to examine technology, innovation and entrepreneurship (TIE) in fair detail and in an integrated manner, the existing text books focused on the management of the first while treating the latter two only in a cursory manner. Clearly, there was a need for a new book. It was then that I set upon writing this trilogy.
It took me several years of personal research and learning to come to grips with the book’s contents. I embarked upon such an exercise immediately upon retiring from active service in Hong Kong and setting up residence in the U.S. My work was significantly helped by the fact that my immediate circle in the U.S. included several young, budding entrepreneurs. I learnt a lot by keenly observing their entrepreneurial trials and tribulations.
Upon retirement from formal teaching, I tried to disseminate in India the TIE lessons I had learnt abroad. I managed to bring together over twenty engineering colleges in and around Hyderabad to collaborate under the umbrella of International Organization of Developing Universities (IODevUni). One of the projects initiated by the Chapter was the application of the emerging e-learning technologies to facilitate the teaching of subjects for which member-colleges did not have enough experts.
E-learning enables students to learn anywhere at the pace, time and location of their choosing. The contents of an e-book itself can be updated frequently. One can also use the power of the Internet to build and sustain a learning community around the particular professor/subject. The learning community itself can contribute material such as case studies, adaptation to local and current conditions, and so forth. This is why this trilogy is being offered first in the form of e-books and a website called tecinnovent.com has been set up in its support.
This trilogy is based on five premises that seem to hold in any economy irrespective of the ‘ism’ being followed:
~ The key to economic growth is productivity improvement through improved technology.
~ Innovation drives technology growth.
~ Competition spurs innovation.
~ Entrepreneurship consummates innovation.
~ The above four premises are equally applicable at the levels of nation-building, managing an existing firm, as well as launching a new venture or a startup.
The first four premises resonate with the recent arguments made by Edmund Phelps, 2006 winner of Nobel prize for Economics, that general knowledge—encompassing business, technology, and the economic environment at large—is an important enabler of the virtuous circle of creativity, innovation, and growth.
Following the last premise, this work is organized into three parts, each devoted to one of these three levels. The picture on the cover page seeks to capture the way each part is addressed. The shape of the central structure in the picture is inspired by Wilson Hall of Fermilab situated close to the author’s residence in the suburbs of Chicago (see figure below). Till very recently, Fermilab had been housing the largest particle accelerator in the world. Thus it captures the central role of systematic science. Systematic science of course is the springboard for a great deal of modern technology.
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The central structure is made up of three parts labeled Technology (T), Innovation (I), and Entrepreneurship (E). This of course is in agreement with this trilogy’s title. However, the intention is not just to examine T, I and E as themes worth studying in their own right, but also to ‘tie’ them together in a purposeful manner. Nations, firms and professionals who understand how the three elements can be synergistically united will enjoy a clear competitive advantage in the modern, globalized world. This emphasis on pulling T, I, and E together so as to beat the competition is reflected by the black belt around the central structure’s ‘waist’.
Part I consisting of Chapters 1 to 8 is titled ‘My World, My Nation’ as it explores the theme of TIE from a world-perspective but stressing nation-building. As citizens of the world and of a specific nation we all engage in animated discussions about some aspect or other of current trends and events in the world. This part aims to make such discussions more informed and purposeful. The issues discussed should be of particular interest to public officials/workers and those at executive levels.
Part II (Chapters 9 to 17) is titled ‘My Firm’ as it discusses the TIE theme from the perspective of how an existing firm or organization could prosper in the contemporary world of globalized competition. The issues discussed should be of particular interest to professionals and managers at all levels.
Part III (Chapters 18 to 26), titled ‘My Startup’, focuses on issues of particular importance to the growing number of youth across the world seeking an entrepreneurial career. It should also be of interest to serial entrepreneurs and intrapreneurs (mentors of entrepreneurial employees).
Although much of the material covered in the present trilogy is available in other books, few have put all of them together. The trilogy also includes several segments drawing on the author’s research.
An examination of literature on the subject of TIE reveals a variety of discursive approaches. Some rely on a selection of case studies to find commonalities to arrive at a list of do’s and don’ts. Some choose a particular sociopolitical belief system, e.g., capitalism or socialism, and use it to theorize. The method adopted in this trilogy is neither. The term ‘evidence-based reasoning’ captures the preferred mode of discussion.
Although the trilogy adopts an academic writing style, it should be useful to working professionals as well as general readers in addition to university students and researchers. It is not necessary that all the chapters are covered in a single semester. Depending on the course objectives, one can pick and choose chapters. There is enough material in the trilogy to engage students for 2 to 3 semesters.
Patri, K. Venuvinod
Emeritus Professor
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Chapter 9
The Dffusion and Dynamics of Innovation
“Knowledge is not simply another commodity. On the contrary, knowledge is never used up. It increases by diffusion and grows by dispersion.”
—Daniel J. Boorstin
Schumpeter’s characterization of innovation as “creative destruction” carries the message that innovation and its effects are essentially dynamic in nature. It highlights the tensions between stability and change, optimism and pessimism, gain and pain, and so forth. It also raises many questions. What is being created and what is being destroyed? Do they balance, or is there a net gain/loss? How are creation and tension related temporally? Answers to these questions are mostly contained in literature carrying the label “The Dynamics of Innovation.” Another closely related body of literature concerns the diffusion of innovations which attempts to answer questions such as the following. Why do some innovations fail while others succeed? Amongst the successful, why are some adopted faster? Of these two fields, the latter is more classical, so we will start this chapter with a review of the theory of diffusion of innovations.
Theoretical Origins
The theory of diffusion of innovations attempts to explain the process by which a new idea or new product is accepted by the market. The development of the field can be traced back to the beginning of the twentieth century in certain German, Austrian and British schools of Anthropology and to a French sociologist and social psychologist by name Gabriel Tarde.
Towards the end of the nineteenth century Tarde was witness to many innovations, some of which were quickly adopted while others were ignored. He then set out to explain why it was so. His explanation hinged on his broader thesis that sociology is based on small psychological interactions among individuals with the fundamental forces being imitation and innovation (Tarde, 1890). He suggested that individuals learn about an innovation by copying it. The more similar the new idea to ideas that have already been accepted, the more likely is its adoption. Tarde also observed that the rate of adoption of a new idea usually followed an S-shaped curve over time and that the takeoff in this curve occurs when the opinion leaders in the society adopt the new idea.
Tarde’s insights could not be followed up immediately as social scientists of the day lacked the methodological tools needed to conduct quantitative diffusion studies. It was only after forty years that his ideas were resurrected by Ryan and Gross in their seminal study of the diffusion of hybrid seed among two corn-farming communities in Iowa (Ryan & Gross, 1943; Ryan, 1948). A summary of this study is provided in Box 9.1. A major finding of the study was that the rate of adoption of the new type of seed did follow the S-shaped diffusion curve graphed earlier by Tarde (see Figure 9.1).
Another finding was that “the adoption of innovation depends on some combination of well-established interpersonal ties and habitual exposure to mass communication.” This suggested that diffusion was a “social process through which subjective evaluations of an innovation spread from earlier to later adopters rather than one of rational, economic decision making.” Further, the adoption process could be divided into five major stages: awareness, interest, evaluation, trial, and adoption.
The credit for creating a comprehensive framework for diffusion studies however goes to Everett Rogers whose1962 book, Diffusion of Innovations, has been recognized as the most definitive work on the subject. Interestingly, Rogers was also an Iowan and his childhood was linked with the hybrid seed story of Ryan and Gross. As it transpired, Everett’s father was one of the Iowan farmers who had resisted hybrid seed corn planting. Unfortunately, there was a drought in that year and the Rogers’ farm withered. Apparently, this episode played a role in Rogers’ subsequent decision to undertake Ph.D. studies on the diffusion of innovations at Iowa State University.
Rogers defined an artifact of innovation as “an idea, practice, or object perceived as new by an individual or other unit of adoption…Newness in an innovation need not just involve new knowledge. Someone may have known about the innovation for some time but not yet developed a favorable or unfavorable attitude towards it, nor have adopted or rejected it. [Newness] of an innovation may be expressed in terms of knowledge, persuasion, or a decision to adopt.”
Rogers then reviewed the diffusion patterns reported in over 3,000 previous studies covering a variety of innovations including: water boiling in a Peruvian village, scurvy-control measures by the British navy, hybrid seed corn in Iowa, hard tomatoes in California, miracle rice in Bali, modern mathematics in Pittsburgh, drugs such as Tetracycline, the QWERTY keyboard, bottle-feeding in the third world, mid-water trawling by U.S. fishermen in the Pacific, etc. The studies reaffirmed the general applicability of the S-type technology-adoption curve and the bell-shaped frequency distribution curve shown in Figure 9.1. From these reviews, Rogers developed a generalized framework for the study of diffusion of innovations. We will use this framework as the basis of our discussion in this chapter.
The Technology-Adoption S-curve
One of the major findings by Rogers was the ubiquity of the S-shaped curve shown in Figure 9.1. More importantly, the bell-shaped frequency distribution curve could be approximated by the cumulative curve of a Gaussian normal distribution. In other words, the technology-adoption S-curve can be assumed to be a cumulative normal distribution. Note that we have already come across the term ‘Technology S-curve’ in Chapter 6. There, our intent was to study progress in the performance of a given technology over time. By contrast, our intent here is to study the rise in the number of adopters over time. To distinguish between the two, we will use the term Technology-adoption S-curve while referring to a graph depicting growth in the number of people adopting an innovation.
Rogers suggested that a theoretical explanation for the normal approximation lies in the fact that the cumulative influences upon an individual to adopt or reject have a tendency to increase over time. If the first adopter discusses it with two other members of the system, and each of the adopters passes it two other peers, and so forth, the resulting distribution follows a binomial expansion. Now it is a well-known consequence of the Central Limit Theorem of statistics that, as the number of trials (adoptions) increases, the binomial distribution increasingly approximates a normal distribution.
Rogers however warns against putting undue faith in the normal approximation for the reason that the members of the system might not have completely free access to interact with one another owing to status differences, geographical barriers, social taboos, and so forth. In such a situation, members of the system would find it increasingly difficult to tell about the innovation to a new adopter, since such “nonknowers” become increasingly scarce. This would result in a technology-adoption curve that starts leveling off after about half the individuals in the system have adopted the innovation.
Another caveat is that the S-shaped technology adoption curve only describes cases of successful innovation. Many innovations however are not successful because the innovation is rejected after only a few adopters. In such a situation the S-curve may level off well before the take-off point or, even, nosedive if the innovation is discontinued. Such effects may also be triggered by the appearance of a more powerful innovation thus wooing away potential adopters of the current innovation. It is possible that the current innovation itself gets progressively modified through the efforts of its users (adopters). Yet another reason for the deviation from the normal S-shaped curve is the lock-in caused by path dependence, as in the case of the QWERTY typewriter keyboard (recall Box 6.1). The diffusion process is particularly sensitive to all these effects until the number of adopters has reached 10% to 20% of all potential adopters. However, once the takeoff point has been reached, it is usually impossible in the short run to stop the spread of the innovation.
Adopter Categorization
According to Rogers, the process of adoption of an innovation can be broken down into five stages. The first is the awareness stage during which the individual is exposed to the innovation but has incomplete information about it. Mass media can play a major role in enhancing adopter awareness.
Once the individual has become interested in the new idea, he/she starts seeking additional information about it. This is called the information stage. The knowledge sought in this period is essentially of the ‘how to’ type although some adopters may also look for an understanding of the functional principles underlying the innovation.
After sufficient information has been acquired, the individual starts to mentally apply the innovation to his/her present and anticipated future situations with a view to deciding whether or not to adopt the innovation (evaluation stage). If the decision to adopt is positive, he/she would fully apply it (trial stage). Finally, if the results are up to expectation, the individual continues to adopt the innovation (adoption stage).
Not all individuals, though, go through all the five stages. At any given stage, the individual can reject or discontinue the innovation either because he/she is disenchanted (disenchantment discontinuance) or because a new and better idea has arrived on the scene (replacement discontinuance). Even when an individual goes through all the five stages, there can be significant variation in the times spent at each stage.
These insights lead us to the notion of innovativeness defined as “the degree to which an individual or other unit of adoption is relatively earlier in adopting new ideas than the other members of the system.” Combining this notion with the normal approximation of the adopter frequency distribution, Rogers advocated the classification of adopters into the five categories illustrated in Figure 9.2. The categories are delineated on the basis of the mean time of adoption, tm, and the standard deviation, s, of the normal approximation of the frequency distribution curve. Let us examine now the natures of these five categories.
Innovators: These are the adopters before time tm -2s, so they make up just 2.5% of the total number of adopters. They are labeled “innovators” because they are the first to be attracted to the innovation. They usually have substantial financial resources. This enables them to undertake rash, daring, or risky ventures. Thus, venturesomeness is their main characteristic. Owing to their obsessive interest in new ideas, they seek cosmopolite social relationships extending beyond the local peer networks. They communicate regularly with other innovators thus forming cliques that may extend over long distances. Owing to these characteristics this group of adopters has been labeled “enthusiasts” in other literature (Moore, 1991).
Early adopters: These are the adopters in the time-period tm-2s to tm-s, thus constituting about 13.5% of the total number of adopters. By contrast with innovators, early adopters tend to be more integrated into the local social system. They are judicious in making adoption decisions, so their peers respect them highly and seek their opinions and advice. Thus they constitute the opinion leaders and serve as role models for many other members in the social system. Consequently this group is generally sought by change agents and other missionaries trying to speed up the diffusion process. Owing to these characteristics this group is also known as ‘visionaries’ (Moore, 1991).
Early Majority: These are the adopters in the time-period tm-s to tm, thus accounting from about a third of all adopters. These individuals tend to adopt the new idea just before the average member of the social system. They prefer not to be the first trying out the new. At the same time, they do not want to be the last to lay the old aside. They frequently interact with their peers but not to the degree that the early adopters do. Their main role in the diffusion process is to act as a link between the opinion leaders and the late majority. The innovation-decision period of this group is longer than that of the two earlier groups. Owing to these characteristics this group of adopters is also called ‘pragmatists’ (Moore, 1991)
Late Majority: These are the adopters in the time-period tm to tm +s, so about a third of all adopters would belong to this category. Their resources are usually limited, so they tend to be a skeptical and cautious lot. Consequently they do not adopt new ideas until most others in the system have done. They need peer-pressure before they are able to take the plunge. Owing to these characteristics this group of adopters is also referred to as ‘conservatives’ (Moore, 1991).
Laggards: These are the adopters in the time-period beyond tm+s, hence accounting for about 16% of all adopters. Their resources are usually limited severely, so they tend to be the most localite in the system. Many are near isolates. The point of reference for them is the past, so they interact mainly with those having traditional values. As a result, their innovation-decision process is lengthy, as in the case of Everett’s father. They tend to adopt a new idea only if it is an economic necessity. However, prior conditions other than innovativeness can affect the innovation-decision process, e.g., previous practice, felt needs and problems, and the norms of the social system.
Rate of Diffusion
Although most innovations have an S-shaped curve of adoptions, how upright the “S” is varies from innovation to innovation. This raises the question why some innovations have a rapid rate of adoption while others exhibit a lazy S-curve. The rate of adoption of an innovation may be measured by the length of time required for a certain percentage of the members of a social system to adopt it. This means that we need to consider the nature of the innovation itself as well as how it is communicated within the social system in question.
Recall that, from the viewpoint of diffusion, it is not necessary for an innovation to be new in an objective sense. It just needs to be perceived as being new by the members of some social system. Such perceptions may be rooted in the innovation or in the halo created around it through clever advertising. Whatever be the case, the perceptions can relate to five features. The first is the perceived relative advantage of the innovation over the one it is replacing. The relative advantage may be with respect to cost, ease of use, ease of storage.
The second is the compatibility between the innovation and the social system within which it is expected to diffuse. For instance, birth control pills are not compatible with the religious beliefs of some communities. Likewise, what works in a developed society may not work in a severely underdeveloped society. Such considerations have prompted many a developing country (e.g., India) to initiate projects aimed at “appropriate” technologies. More recently, following the 2008 recession, India’s IT industry refined the notion and packaged it into a new management buzzword, Jugaad. In Hindi, this word means inexpensive innovation on the fly, i.e., innovation driven by scarce resources while focusing on customer’s immediate needs. Ford Model T and the recently unveiled Tata Nano have been cited as examples of Jugaad.
The third important feature of an innovation is its complexity. The greater is the perceived complexity, the slower will be the rate of diffusion. For instance, many people are intimidated by the idea of using the internet. Much technophobia is of this nature.
The next is trialability which refers to the ability of the consumer to give the innovation a test run before deciding to adopt it. The easier it is to try out a product before purchase, the higher is its rate of adoption.
The final feature of importance is observability which is the degree to which the results of the innovation are visible to others. For instance, movements promoting safe sex as a preventive measure countering the spread of HIV/AIDS face an uphill battle because it involves unobservable and ambiguous practices such as sexual abstinence and monogamy.
We now turn to the social features affecting the rate of diffusion. According to Rogers, a social system is “a set of interrelated units that are engaged in joint problem-solving to accomplish a common goal. The members or units of a social system may be individuals, informal groups, organizations, and/or subsystems.” For instance, the corn farmers of Iowa were jointly deciding how to react to the idea of substituting their open-pollinated seeds with the new hybrid seed corn. Every social system has its norms, communication channels, opinion leaders and change agents. All these affect the rate of adoption.
Norms are the established behavior patterns for the members of a social system. Traditional norms are characterized by a preference for less complex technologies, low levels of literacy and education, little communication between the social system and outsiders, apparent lack of economic rationality, and a one-dimensional way of adapting and viewing others. By contrast, modern norms are characterized by developed technology with complex jobs, a tendency for placing strong importance placed on education, acceptance of free thought and new ideas, great importance being placed on economic considerations, and a desire to see and understand other people’s situations. Empirical data show that societies with modern norms accept and adapt to innovation faster and easier than those with traditional norms.
In the context of diffusion of innovations, communication is the process by which participants create and share information with one another in order to reach mutual understanding. A communication channel is the means by which messages get exchanged between units of the social system. Two common types of such channels are mass media such radio, television and newspapers, and interpersonal channels which involve face-to-face information exchange between two or more individuals. Mass media are quite effective in spreading knowledge of innovations to a large audience rapidly. They may also be able to change weakly held attitudes.
However research shows that firm attitudes are developed only through interpersonal communication which are more trusted and have greater effectiveness in dealing with resistance or apathy on the part of the communicatee. The effectiveness of interpersonal communications is determined by the degree to which they are similar in beliefs, education, social status, and so forth. Societies exhibiting a high degree of similarity are said to be homophilous. However, one of the most distinctive problems in the diffusion of innovations is that the participants usually are quite heterophilous. A common approach to overcoming this problem is to persuade opinion leaders (early adopters) to foment positive attitudes toward the innovation. Positive attitudes can also be fomented by professionals from an external agency such as a governmental unit promoting a particular technology or the marketing division of the company that has produced the technology. Rogers calls these professionals change agents. A change agent usually seeks to obtain the adoption of the innovation being promoted and discourage the adoption of undesirable competing innovations. In this he/she uses local opinion leaders as his/her lieutenants.
In view of the importance of the mass media in the diffusion of contemporary innovations, it is useful to know about two classical models of mass communication flows. The first is the hypodermic needle model which emerged from the Marxist Frankfurt School of intellectuals in the 1930s mainly to explain the rise of Nazism in Germany before World War II. The model postulated that the mass media had direct, immediate, and powerful effects on mass audience. The media were pictured as being omnipotent and hence capable of conveying messages to atomized masses of individuals (Katz & Lazarsfeld, 1955), as in the case of Goebbels’ propaganda campaign to promote Nazism in Europe during World War II. It was suggested that a very large group of people can be influenced directly and uniformly by “injecting” them with appropriate messages designed to trigger a desired response—hence the hypodermic needle analogy. A problem with this model, however, is that it does not recognize any escape. People are seen as sitting ducks. This of course is not true at least in many modern, democratic societies (Lazarsfeld et al., 1944). For instance, empirical data concerning political decisions in the U.S. showed that ideas often first flow to opinion leaders through mass media and from these to the less active sections of the society (Lazarsfeld & Menzel, 1963). This two-step flow model was subsequently found to be quite accurate for a variety of communication behaviors, including the diffusion of innovations. This model implied that mass media were neither so powerful nor direct as suggested by the hypodermic needle model. The communication model suggested by Rogers however is far more fine-grained owing to the introduction of notions such as homophily and change agents.
Finally in this section we draw attention to the case study on the diffusion of Broadband internet in South Korea presented in Box 9.2. We leave it to the reader(s) to examine how far this case is in agreement with the diffusion theories presented above.
One thing is clear however: The time periods for technology diffusion have been falling owing to the development of more effective communication channels (e.g., Facebook, Twitter). For instance, a 2002 study by MIT’s Entrepreneurship Center noted that the length of time needed to reach 25% of U.S. households from the time of invention has been falling significantly: automobile (1886) 56 years; electricity (1873) 45 years; telephone (1876) 36 years; microwave (1953) 31 years; television (1926) 26 years; internet (1975) 23 years; the cell phone (1983) 14 years (Kaplan & Morse, 2002).
Adoption of High-Tech Products
Rogers’ model is particularly applicable when, as in the case of continuous innovations, the innovation does not force a significant change of behavior on the part of the customers. This stipulation is however not satisfied in the case of discontinuous innovations such as is the case with some high-tech innovations. A model more closely suited to the latter case is that proposed by Geoffery Moore (1991).
Moore pointed out that “Our [high-tech] marketing ventures, despite normally promising starts, drift off-course in puzzling ways, eventually causing unexpected and unnerving gaps in sales revenues, and sooner or later leading management to undertake some desperate remedy... The point of greatest peril in the development of a high-tech market lies in making the transition from an early market dominated by a few visionary customers to a mainstream market dominated by a large block of customers who are predominantly pragmatists in orientation.” Moore called the gap between these two markets a “chasm” (see Figure 9.2) and suggested that crossing this chasm must be the primary focus of any long-term high-tech marketing plan. It is only after passing the chasm that one reaches the tipping point (Gladwell, 2000) signaling the establishment of a self-reinforcing feedback loop toward the new idea.
According to Moore, the chasm arises mainly because of the nature of the “pragmatist” group of adopters. Statistics, research and even facts don’t sway pragmatists. Their purchase decisions are generally based on references within the given vertical market. More importantly, for them, references should come from other pragmatists, not early adopters (visionaries). Visionaries alienate pragmatists because of their lack of respect for experience, their greater interest in technology than industry, and their failure to value the existing infrastructure, and their tendency to take all the credit while not sticking around to make things work in the long run. For all these reasons, one can’t sell high-tech products to pragmatists the same way as to enthusiasts and visionaries. In other words, the use of visionaries as references (as Everett suggested) is likely to lead to a plunge into the “chasm.” Moore then studied successful campaigns conducted by companies such as Apple, Tandem, Oracle and Sun and came up with the several guidelines for high-tech companies preparing to cross the chasm (for the guidelines, see Moore, 1991).
Industry Dynamics
When we say something is dynamic we mean it changes with time. Faster the change, the more dynamic is the phenomenon. In other words, what is constant is change—a notion that is being repeated ad nauseam in recent management literature. Another way of interpreting this notion is that everything has a life cycle. Nothing stays the same indefinitely. Everything has a finite lifecycle. Everything is born at some time and is gone after some time. What matters is what happens in between.
The life cycle of any entity can be divided into the following stages: birth, embryonic stage, growth, maturity, decline, death. The entity of interest for us here is an industry or, more specifically, an industry sector. At any time, a given industry sector can be seen as an aggregate of firms serving a loosely defined market through the provision of the associated products and services. Industry lifecycle theory links the intensity of competition in a particular market with the time since the breakthrough (radical) innovation(s) that made that market possible.
Incumbents versus New Entrants
The birth of an industry segment is commonly correlated with the cycles of some product or process innovation. Other factors that may launch it include government intervention or deregulation, the liberalization of external trade and lower transportation costs. However, the most common cause is the emergence of a breakthrough or radical innovation.
By definition, a radical innovation is one that fundamentally changes the structure of an industry by creating new market segments. If the innovation is from an already established firm (an incumbent), that firm will of course gain substantial competitive advantage over the rest of the incumbent firms. The only thing the other incumbents can do is to start imitating and improving upon the original radical innovation. But an industry is not a closed system. Others, including established firms from other industries as well as totally new upstarts are watching the industry. Any one of these can jump on to the bandwagon. Things get more interesting when this happens, which is usually the case. Now the competition is extended to include the new entrants. Things get even more interesting when the original radical innovation is from a small, entrepreneurial firm from outside the industry. The battle then resembles more like that between David and Goliath since the new entrants and the incumbents have very heterogeneous resources and capabilities (Chandler & Hikino, 1990; Freeman & Soete, 1997).
A key to the understanding the dynamics of a given industry during its lifecycle is to examine how the number and size of firms constituting the industry changes over time. The first formal model in this regard was that by a French economist by name Robert Gibrat (1931). Gibrat examined a broad range of data of the size and distribution of French agricultural, commercial, and industrial establishments in the period 1896 to 1921 and came to the conclusion that the expected value of the increment of firm size in each period is proportional to the firm size. This principle is now commonly referred to as Gibrat’s rule of proportionate growth or, simply, Gibrat’s Law.
An implication of Gibrat’s law is that the growth rate of a firm is independent of its size. If a company with sales of $10m doubles in size over a period of time, it is likely the same will happen for a company beginning with sales of only $1m. Subsequently, the “law” prompted several alternative models. The overall conclusion is that, although it has several flaws, Gibrat’s Law can be used as a first approximation in many situations (Sutton, 1997).
However, the contemporary view of industry dynamics following a pioneering innovation owes much to the works of Abernathy and Utterback whose findings are neatly summarized in Mastering the Dynamics of Innovation published by Utterback in 1994. This book contains several graphs illustrating how the numbers of firms entering and exiting a range of U.S. industries varied over their lifecycles. Although there are some variations in the patterns for different industries, we may base our general discussion on the stylized patterns shown in Figure 9.3a.
The number of firms active in an industry at any given time is the cumulative number of firms entering the industry from the time of its birth less the cumulative number of firms exiting. As shown in the figure, the industry lifecycle can be divided into five stages:
~ The dormant stage during which a small number of competitors enjoy high monopoly profits.
~ The takeoff stage during which there is soaring entry and virtually non-existent exit from the market.
~ The high turnover stage during which a large number of firms enter and leave the market.
~ The shakeout stage with mass exit via mergers, bankruptcies, etc.
~ The stabilization stage during which a stable oligopoly emerges.
Stages I to III constitute what is known as the entrepreneurial regime in which new entrants are favored because the stock of industry-specific knowledge is low and the knowledge that is critical to innovation lies outside the incumbent firms.
In stages IV and V, the industry has matured into a routinized regime during which innovation is determined by non-transferable, internalized, market-based knowledge. As a result, the knowledge-based advantage of new entrants over incumbents reverses when the industry moves into the routinized regime (Sarkar et al., 2006).
Some Stylized Facts
Another highly cited contribution to the understanding of industry lifecycle is the list of stylized facts prepared by Gort and his associates after studying the historical development of a large number of products in terms of their sales, price, output, and the counts of producers over their respective lifecycles (Gort & Klepper, 1982; Agarwal & Gort, 1996). Here are the main stylized facts identified by them (see Figure 9.3b):
~ Sales and output of a product grow at a rate that declines rapidly with that product’s age, and the rate tends to converge to zero.
~ Product price declines fairly steadily and at a decreasing rate as the product ages.
~ After the product is born, first there is a rapid entry of firms, then a mass exit, a shakeout, and finally stabilization in the number of firms at a level of about 40% below the peak number.
~ Innovation activity as evidenced by patenting does not fall off as the industry matures. Neither does it decline with the age of the product. However, innovations occurring while the product is young matter more than those occurring later.
~ A firm’s exit hazard declines with the age of the firm.
~ A firm’s exit hazard rises with the age of the industry.
Stylized facts (i) and (iii) are particularly heartening. Output from an industry continues to increase over its lifecycle as prices decrease. The reason lies in fact (iv) which suggests that innovation activity persists over the entire industry lifecycle. New entrants play a significant role in innovation across the industry. There is compelling evidence showing that the market share of new entrants over a period of time is strongly related to the TFP (Total Factor Productivity—see Chapter 4) of the industry (Bessen, 1998). All this implies that creation wins over destruction despite the painful churning that industries experience.
Insights from Abernathy & Utterback
Let us now turn to the insights provided by Abernathy and Utterback concerning the nature of industry dynamics following a pioneering innovation. Although their initial work had focused on the automobile industry (Abernathy, 1978; Abernathy & Utterback, 1994), similar patterns were observed when they examined other assembled product industries such as typewriter, electric lamp, personal computer, television and television tube, transistor, electronic calculator, integrated circuit, and disk drive. On the other hand, some major differences were found in non-assembled product industries such as plate glass-making (Box 9.3), petroleum cracking and rayon. So they chose to discuss patterns of technological growth in terms of these two extremes while recognizing that there is a broad middle ground of products exhibiting some characteristics of both (e.g., color photographic film). An excellent review of their findings interspersed with numerous case studies is available in (Utterback, 1994).
Changes in Assembled Product Industries
Figure 9.4a shows some common patterns of change in assembled products industries following the introduction of a new technological paradigm. When a pioneering firm introduces a radical product, it triggers a growing market around it. New competitors enter the market with alternative product versions hoping to capture as big a chunk of the market as possible. However, at this stage, none of the versions is perfect and none of the competitors has yet mastered the related production and distribution processes. At the same time, the customers are not sure of what they are looking for. Both producers and consumers are learning as they move along. One may therefore say that the industry is in a fluid stage. An implication of the fluidity is that the capital and technical barriers to new entrants are not too daunting, so many aspiring entrants start experimenting with the new idea.
It is common to think that, in the fluid stage, new entrants to an industry are smaller firms. This might or might not be the case since there are numerous examples of large firms entering young industries, e.g., Remington’s entry into the typewriter industry in 1873 and IBM’s entry into the PC industry in 1981. Whatever be the case, initially, technical progress is slow as there are only a few firms participating. As larger firms enter the scene, technical progress and productivity pick up. On the other hand, since industry standards are not yet firmed up, new entrants start coming up with bold new designs. Meanwhile established firms continue to perfect their original designs and keep coming up with improved new models. The response of the customers to various new products acts as feedback stimulating producers to take corrective actions in the next product introduction (Klein, 1977). However, no single design captures customer allegiance. Thus the fluid stage is one of experimentation and competition.
During the fluid phase of an assembled product industry, firms compete mainly on the basis of product innovation rather than on lower cost or higher quality. So process innovation lags behind product innovation (Figure 9.4a). The processes specific to each new product remains a mixture of skilled labor and general purpose machinery. Since there is no single design to market on a large scale, it does not make economic sense to develop specialized tools and machines.
At some point, one of the designs wins a level of consumer allegiance that forces competitors and other innovators to start mimicking it. Such a design is called the dominant design. The dominant design is usually a synthesis of individual technological features sought by specific groups of customers, so it seeks a compromise through the interplay of a range of technical possibilities and market choices.
The notion of dominant design can be applied to products as well as processes. When its dominant design arrives, the industry starts undergoing significant structural transformations. If the market leader had originated the dominant design, that firm’s position is considerably strengthened. If it was from an industry outsider, the incumbent’s market leadership is severely compromised. Table 9.1 lists the major dominant design sequences identified by Utterback (1994, 2003) in a selection of industries.
A dominant design may persist for a considerable period of time, even though it might not represent the best technical solution (e.g. VHS versus Betamax). Typically, it significantly reduces the number of explicitly stated performance requirements by making them implicit in the design itself. Thus, for instance, everybody now assumes that the car they are purchasing would include indicator lights and windshield wipers, though not necessarily seat warmers. At the same time, the performance criteria that had remained ill-defined during the fluid stage become well-articulated. Many of the preferred features are already set by the marketplace, so the product innovation focus moves to product variation rather than to radical innovation. The result of all this is the flurry of radical product innovation that had characterized the fluid stage ends after the emergence of a dominant design. Hence the period of industry development around the introduction of a dominant design is called the transition stage leading up to the specific phase to be described later. Table 9.2 summarizes the typical changes occurring in industry following the emergence of a new dominant design.
The emergence of a dominant design is not simply a matter of technological progress since it can be influenced by many other factors. For instance, governments can influence the process by favoring specific technologies for political and social reasons. Likewise, firms in possession of greater collateral assets such as brand image and strong communication channels with customers can make the market swing either way (Teece, 1986).
If they are in command of the dominant design, they can use their market clout in favor of their product. If they are in possession of the dominant design, they can use their communication channels to persuade customers not to move over to the new design. Firms can also influence the emergence of the dominant design through strategic maneuvering as illustrated by the story of the rivalry between JVC and Sony in Box 9.4 (Christensen et al., 1992).
Two characteristics of transition phase are worthy of note. Firstly, while product innovation takes the backstage, firms start investing more and more into process innovation (Figure 9.4a). With the clear articulation of desirable product features as embodied in the dominant design, the remaining firms can look forward to a mass- market. As a result, specialized sections and some automated islands start appearing in the plants. The source of innovation moves from industry pioneers and product users to manufacturers and users. Organizational control which was informal and entrepreneurial during the fluid phase starts relying more and more on structured project and task groups.
Secondly, the number of competing firms starts decreasing significantly as the market swings more and more in favor of firms possessing the dominant design. At some point the number of firms reduces to just a few, so the market resembles classic oligopoly with relatively stable market shares. Because there are relatively few participants in this type of market, each oligopolist is aware of the actions of the others. The decisions of one firm influence, and are influenced by, those of other firms. Each firm is producing a very specific range of products that are essentially undifferentiated and standard. So this final phase is called the specific stage. The only way firms can now compete is on the basis of price, so the scope for innovation has reduced to cumulative improvements in productivity and quality. Through learning, production processes become efficient. Since scale is the main source of price reduction, plants become more capital intensive with high reliance on special-purpose and automated equipment while the role of labor is reduced mainly tending and monitoring the plant. Owing to the resulting rigidity, cost of change becomes even higher, thus inhibiting further product or process innovation. Table 9.3 summarizes the changing character of innovation in the fluid, transition, and specific stages of an industry.
Changes in Non-assembled Product Industries