Innovative Auto-ID and LBS - Chapter Two Innovation Studies

Chapter II: Innovation Studies



This chapter will explore literature in the field of innovation in order to establish a conceptual framework for the auto-ID trajectory research. The primary aim of this review is to provide a critical response to the literature on technological innovation. The review will also serve to: (i) identify and understand widely accepted definitions, concepts and terms, born from past innovation research as a guide for further research; (ii) review theories, theoretical frameworks and methods adopted by other researchers doing similar innovation studies (especially in the area of information technology) in order to choose an appropriate approach for this study; (iii) understand what aspects of complex high technologies (high-tech) have already been explored by researchers and what aspects have been neglected and to discover any similarities or differences in existing findings.

Previous research will be examined in this chapter using a two-tiered approach; topical at the surface layer and chronologically organized therein. This type of analytical strategy is advantageous because similar patterns, trends, or findings can be uncovered and organized into clusters over time. Each study will be categorized according to the theory and research method used by the author(s). Additionally, findings of each study will be briefly highlighted for comparison. Seminal works will be treated at a greater length than smaller studies. The same emphasis will be attached to reporting accurate summaries, and responding critically to previous research. Overall, greater consideration will be given to reviewing contemporary innovation literature, as opposed to outdated research that was never conducted with the knowledge of information technologies.



Invention, Innovation and Diffusion

This section will be dedicated to defining the fundamental links between invention, innovation and diffusion as it applies to this research. The three terms are different, however, as Lindley (1997, p. 19) observes at the same time, the terms are also closely allied. Sahal (1981, p. 41) makes the distinction that an invention is the creation of a “new device” and an innovation is the “commercial application” of that device. Similarly Braun (1984, p. 39) argues that “ invention is merely an idea for a prototype of a new product or process and does not become an innovation until it reaches the market [diffusion].” Most inventions never become innovations; they fall by the wayside on the long road from idea to marketable product (Westrum, 1991, p. 150). For a thorough introduction into the diffusion of innovations see Rogers (1995).


Invention: Mutation, Recombination, Hybrid

As suggested by Jacob Schmookler, a patentable invention is a new product or process that shows a significant degree of originality and has some future use (1966, p. 6). A question often asked is, do all inventions fall into the same category? The answer according to Farrell (1993) and Mokyr (1996) is no: inventions may differ depending on how their formation came about. Table 1 shows that invention can be classified into three types, mutation, recombination, and hybrid (Mokyr, 1996, p. 69). Without reference to Farrell or Mokyr, Edquist (1997, p. 1) states, “[i]nnovations are new creations of economic significance... [that] may be brand new but are more often new combinations of existing elements.”


Innovation: Radical versus Incremental

Generally, an innovation can be described as “...a process or a product, a technical or an organizational change, an incremental improvement or a radical breakthrough” (Deideren, 1990, p. 123 quoted in Lindley 1997, p. 20). Since the 1900s the term innovation has undergone many revisions with the emergence of new theories in the field of economics. Schumpeter’s (1939, p. 87f) well-known definition of innovation is directly linked to neoclassical economic theory by means of the production function: “...we will simply define innovation as the setting up of a new production function. This covers the case of a new commodity as well as those of a new form of organization... this function describes the way in which quantity of product varies if quantities of factors vary. If, instead of quantities of factors, we vary the form of the function, we have an innovation... we may express the same thing by saying that innovation combines factors in a new way, or that it consists in carrying out New Combinations.” The production function indicates the “maximum amount of product that can be obtained from any specific combination of inputs, given the current state of knowledge. That is, it shows the largest quantity of goods that any particular collection of inputs is capable of producing” (Baumol & Blinder, 1992, pp. 507-510).

As Saviotti (1997, p. 184) comments, for Schumpeter new combinations gave rise to new products and processes that were qualitatively different from those preceding them. However, while the phrase ‘new combinations’ is still widely used today within the evolutionary theory of economics by researchers such as Lundvall (1992, p. 8) and Elam (1992, p. 3), innovation is no longer attributed to the setting up of a new production function. Rather, innovation is a natural process of ‘technical progress,’ a technology-specific process of ‘learning by experience’ (see Sahal, 1981, p. 37). Nelson and Rosenberg (1993, pp. 4f) interpret innovation broadly, adding an optional geographic context: “ encompass the processes by which firms master and get into practice product designs and manufacturing processes that are new to them, whether or not they are new to the universe, or even to the nation.”

There is some value in this geographic perspective either at the local, regional, or national level. In National Innovation Systems, Nelson and Rosenberg (1993, p. 3) state that “[t]here is a new spirit of what might be called “technonationalism” in the air, combining a strong belief that the technological capabilities of a nation’s firms are a key source of their competitive prowess, with a belief that these capabilities are in a sense national, and can be built by national action.” In selecting a preferred definition of innovation for this book, the more contemporary and balanced definition given by Edquist (1997, p. 16) is appropriate: “[t]echnological innovation is a matter of producing new knowledge or combining existing knowledge in new ways- and of transforming this into economically significant products and processes.”

It is often important to classify the impact of a product or process innovation in a way that can be useful for comparing one or more technologies. Admittedly this is very difficult, since the extent of an innovation is dependent upon the perspective taken by the researcher. However, using Braun’s terse definitions (see Table 2) one can distinguish one type of innovation from the other. Similarly Landau (1982, p. 54) believes that there are “...fundamentally two kinds of innovation: 1. The ‘breakthrough’; 2. The ‘improvement’.”



According to Rogers (1995, p. 5), “diffusion is the process by which an innovation is communicated through certain channels over time among the members of a social system. It is a special type of communication, in that the messages are concerned with new ideas.” Diffusion is characterized by a two way channel, where ideas are exchanged and feedback is provided. For Rogers, diffusion means a kind of “social change.” There are four main elements in diffusion including, innovation, communication channels, time, and the social system. For the application of the diffusion of innovation (DOI) theory to information technology, see Prescott (1995) and Prescott and Cogner (1995). The elements of DOI are similar to the qualities inherent in the systems of innovation framework, especially that element of time, feedback mechanisms, and change.


The Innovation Process: Product versus Process

Throughout innovation literature there is some confusion over the terms innovation process, product innovation and process innovation. First, the innovation process can refer to either products or processes. It is the stages or phases involved with getting an idea for an invention to a ‘finished product’ or ‘finished process’ to operation. Braun (1995, p. 61f) outlines these phases as: the idea or invention; development of the product; prototype; production; and marketing and diffusion. An example of a product innovation is the semiconductor microchip. An example of a process innovation is the automated assembly line, set up at an automobile manufacturing plant. However, it is not always clear whether a given innovation should be categorized as either a product or process; this is especially true in the IT sector (Edquist et al., 1998, p. 12). Irrespective, today it is more relevant to be concerned with the actual system of innovation. Now that the relationship between invention, innovation and diffusion has been presented it is necessary to allocate space to the actual innovation studies themselves. By reviewing the various types of conceptual frameworks and methodologies applied by other researchers, an appropriate approach can be chosen for this investigation. Significant findings in the way of emergent patterns or events in the innovation process will also be highlighted and explained.



Marx was a philosopher, sociologist and economist considered by many to be one of the most influential persons of all time. In his classic work Capital, Karl Marx (1818-1883) writes about the importance of products in the labor process. He stated that the result of this process was “...a use-value, a piece of natural material adapted to human needs by means of a change in its form...” (Marx, 1976, p. 287). Products were “...not only the results of labor, but also its essential conditions.” For instance, a finished product could assist in the innovation process of another new product. In this manner technological change was the force behind process changes within existing institutions or the force behind the establishment of new institutions. It seemed obvious to Marx that for economic growth to be achieved product and process innovations were required. In commenting on the capitalist system he wrote that: “...the capitalist has two objectives: in the first place, he wants to produce a use-value which has exchange-value, i.e. an article destined to be sold, a commodity; and secondly he wants to produce a commodity greater in value than the sum of the values of the commodities used to produce it, namely the means of production and the labor-power he purchased with his good money on the open market. His aim is to produce not only a use-value, but a commodity; not only use-value, but value; and not just value, but also surplus-value” (Marx, 1976, p. 293).

Marx’s long-lasting contribution was recognizing that product and process innovations had a social impact. Technology could be used to oppress a class or to empower an individual. This is an important observation that Marx has made about the power of technology. When considering auto-ID today this question is still relevant. In the Introduction, the growing dependence of humans on auto-ID devices was highlighted for this very reason. In discussing the evolution of digital technologies, Covell (2000, p. 5) notes a fundamental shift in the nature of the evolution: “[t]he difference is that digital technology is now being applied to enhance and extend human interaction.” Accompanied by Friedrich Engels (1825-1895), Marx conducted historical research on process innovations in England. He used original factory documents to draw conclusions on the life of a worker who was driven by the capitalist to create surplus value. Modern interpretations of Marx’s ideology have sought to reassess parts of his labor theory of value as a means to reveal its limitations (Habermas, 1981, p. 159).



As defined by Cohendet and Llerena (1997, p. 226) neoclassical economics “...examines the way through which market mechanisms select new technologies and eliminate those that have become obsolete.” One shortcoming of studies using neoclassical economic theory is that they focus primarily on business process innovations. Neoclassical studies are concerned with the manner in which technological innovations can enhance the productivity of a firm and decrease employment per unit of output (Edquist, 1997, p. 22). A fundamental weakness of neoclassical economic theory is that “[e]xchange takes place without any specification of its institutional setting. Only prices and volumes matter” (Edquist & Johnson, 1997, p. 48).


Joseph Alois Schumpeter

In the year that Marx died Schumpeter was born (1883-1950). While he was to eventually share many of Marx’s beliefs, particularly in the self-destruction of capitalism, he focused his efforts on the statistical analysis of the capitalist process. What he is probably best remembered for are his studies on the production function and his book titled Theory of Economic Development (1934). Schumpeter was a neoclassical economist who attributed higher amounts of capital per worker to technological change, which resulted in more for the profit receivers. Throughout his professional career, he was preoccupied with innovation as the main agent for entrepreneurial profit. Like Marx he too focused on business process innovations. The production function that Schumpeter wrote about “...expresses the relationship between various technically feasible combinations of inputs, or factors of production, and output... it is a specification of all conceivable modes of production in the light of the existing technical knowledge about input-output relationships” (Sahal, 1981, p. 16). However, discontent with the production function has led many economists to abandon the neoclassical approach. This study also, does not lend itself to this type of rigid analysis as the questions it asks are more exploratory than computational. Neoclassical economic theory allows only for purely economic factors to be considered, neglecting other significant aspects of innovation.



Evolutionary economic theory has not achieved the degree of articulation corresponding to neoclassical economic theory (Saviotti, 1997, p. 181). For one simple reason, it is more recent. It is an alternative to understanding technical change as something other than an attempt to maximize profits (Nelson & Winter, 1982). Unlike neoclassical economics, evolutionary theory is suited to both process and product innovations. It is also more contemporary, developed with an understanding of modern technological innovations. It has been applied to product innovations, such as semiconductors and satellites in the high-tech industry and is more suited to the investigation of auto-ID technologies. Numerous recent theoretical and empirical studies performed, have been conducted with the notion that technical change is an evolutionary process.

Devendra Sahal in his book Patterns of Technological Innovation (1981, p. 64) commented that evolution was not just a matter of ‘chop and change’; it related to the “...very structure and function of the object.” He stated that innovation was “...inherently a continuous process that [did] not easily lend itself to description in terms of discrete events” (1981, p. 23). Sahal is best remembered for his quantitative diffusion analytical strategies. While he made excellent evolutionary theoretical discoveries his methodology differs from contemporary researchers in innovation. In that same year Richard Nelson also suggested that due to the randomness and the time-consuming nature of innovation processes, evolutionary models of technological change were more realistic in understanding innovations than the models provided by neoclassical economics (Nelson, 1981, 1059f).

Perhaps one of the most significant publications, as suggested by Saviotti (1997, p. 181) was Nelson and Winter’s, An Evolutionary Theory of Economic Change (1982). It was not until this time, that a researcher had concretely stipulated that “technical change [was] clearly an evolutionary process” (Nelson, 1987, p. 16). While many innovation studies had challenged neoclassical economic assumptions during the 1970s, none had been so game as to suggest that evolutionary economic theory was more appropriate. At the time, Nelson (1987, p. 16) believed that the innovation generator kept making technologies superior to those in earlier existence. However, as later clarified by Charles Edquist (1997, p. 6) “...only superior in a relative sense, not optimal in an absolute sense.” Edquist affirms that “...technological change is an open-ended and path-dependent process where no optimal solution to a technical problem can be identified” (Edquist, 1997, p. 6). This perspective is embraced throughout this research study. It has very important implications as it shapes the context in which auto-ID is to be understood. Rather than concentrating on the progression from one auto-ID technology to the next in terms of ‘superiority’, the question is more about the actual path taken to develop, by firms, government and consumers. Who drives this path and the dynamic interaction between the stakeholders then becomes of interest. This idea is quite different and challenging.

In contrast refer to Charles Darwin’s (1809-1882) writings (ch. 4) on ‘Natural Selection; or the Survival of the Fittest’ (1960, p. 53). His fundamental argument is “...[t]hat as new species in the course of time are formed through natural selection, others will become rarer and rarer, and finally extinct. The forms which stand in closest competition with those undergoing modification and improvement will naturally suffer most.” Refer also to the sections in ch. 4 on “divergence of character” (p. 53) and “convergence of character” (p. 62). When this Darwinist approach is applied to economic affairs, Allaby (1996, pp. 130-132) calls it “social Darwinism”. He believes that this theory is deeply flawed: “[i]ts first error lies in its equation of evolution with progress, the idea that later forms are better than earlier ones. This is a value judgment, for what do we mean by ‘better’? If we mean ‘better at surviving’ we are being tautologous.” More recent observations by Dr Jerome Swartz (1999), founder and former CEO of Symbol Technologies, present a whole new approach to understanding auto-ID technologies. He writes (p. 21): “[n]ot long ago, I recall the heated debates about which technology was best- which would bring the most benefits, prove the most reliable or the cheapest. The implied question was, “Which will emerge as the real winner at the end of the day?” I believe that “competitive” framework asked all the wrong questions and clouded a better understanding of how the technologies could exist side-by-side.” These most insightful observations serve as a calling for further research to be conducted in the field of auto-ID innovation using evolutionary economic theory.


Case Studies and Qualitative Research

A landmark study becomes obvious to the researcher who has read a plethora of literature in the field he or she is studying. Margaret Sharp’s, Europe and the New Technologies: Six Case Studies in Innovation & Adjustment (1985), is one of these landmark studies. She later follows up with Strategies for New Technologies: Case Studies from France and Britain (1989) that is equally impressive. Space will be dedicated to the former because it was without a doubt a paragon for future research in the field. Sharp uses evolutionary theory and a case study methodology to examine six new technologies in Europe. The methodology chosen for this study is advantageous in that it gives Sharp and her fellow contributors the flexibility to explore the many diverse issues surrounding the central thesis. New industrial activities are examined rather than individual industries or sectors. The research which was focused on computer-aided design (CAD), advanced machine tools and robotics, telecommunications, videotext, biotechnology and offshore supplies was very successful, and finally conclusions were drawn from recurring themes identified in the case studies. In summary, Sharp (1985, p. 271) concludes that: “[t]he process of change is evolutionary- new industrial activities emerge from the body of old industrial activities, the decisions are incremental as firms adjust their product/process mix to opportunities which present themselves, and, as this happens, so firms progressively redefine the nature and boundaries of the industry itself.”

More precisely, Sharp believes that the concept, technological trajectory, is useful in the context of her case studies. Her discovery is very significant and is quoted in full below. “A new technology very often, and certainly, in the cases we have been studying, is subject to continuous improvement over a number of years. Firms which develop the capability to make these continuous improvements, that is to move along the trajectory, are often the most successful. As well as continuities, there are discontinuities. Major new technical or marketing innovations present such discontinuities. A discontinuity halts progress along the existing trajectory but simultaneously opens up a new one. In assimilating major technological change, a firm in effect changes gear and shifts to a new trajectory. In this sense, the discontinuity may be regarded as a revolution. Whereas evolution, development along the trajectory, is an everyday occurrence, revolutions are quite rare” (Sharp, 1985, p. 272).

Sharp’s study is an excellent model for this investigation. It shows how a research project such as the one that these researchers are undertaking, is likely to lead to some valuable results. And results, that are applicable to more than just that group of technologies (in this case auto-ID) being investigated. The key terms that Sharp uses, evolutionary, continuous improvement, discontinuity, technological change, technological trajectory, will be used throughout the main body of this work. Friedman (1994), like Sharp (1982) applies evolutionary concepts to his study on the IT field. Of particular interest is his emphasis on the technological trajectory of IT which he breaks down into four phases of historical change: 1) hardware capacity constraints, 2) software productivity constraints, 3) user relations constraints, and 4) the future. Friedman also makes the useful distinction between a technology field and technological paradigm. “First, the focus of the technology field is on people, institutions, and organizations. The focus of the technological paradigm is on designs or patterns of solutions. The technology field encourages a much wider set of people to be analyzed. The technological paradigm contains practitioners working in organizations supplying the technology, and possibly scientists and technologists working in associated research institutes and universities (Clark, 1987).”


Fundamental Concepts

As identified by Carlsson and Stankiewicz (1995, p. 23) the major strength of the evolutionary approach is its “...ability to bring within a single framework the institutional/organizational as well as cognitive/cultural aspects of social and economic change”. This corresponds with the second objective of this book, in that many issues, not just economic will be analyzed to identify the factors influencing auto-ID innovation. The key terms used in this framework are highlighted in Table 3.


Technological Trajectories

The term technological trajectories, also known as natural trajectories, can be attributed to Dosi (1982) as being a pattern of innovation. While there have been several definitions given for this term, in the context of this book von Hippel’s interpretation (1988) is appropriate: “[t]echnological trajectories consist in the continuous improvements of products in terms of performance and reliability and in the tailoring of products to specific users’ needs, within specific application contexts.” For a thorough explanation of the term technological trajectory see Durand (1991). He makes the important link between the terms technological trajectory and technological forecasting which is extremely pertinent to this study. In quoting Dosi (1982), Durand makes the distinction between continuous changes along the same paradigm versus discontinuities which are associated with a new emerging paradigm. Hirooka (1998) makes the distinction between the technological trajectory of a product and its diffusion trajectory. The paper focuses on three cases of innovation paradigm, synthetic dyestuffs, electronics and biotechnology. Through these examples Hirooka provides evidence that the technological trajectory of an innovation spans about 20-30 years upon which point it joins the diffusion period. Banbury (1997, p. 13) writes “[t]he concept of a technological paradigm enables us to delineate the boundaries of technological change cycles (paradigms) and to delineate the direction of change (trajectories)”.

What should be highlighted here is the focus on products and their continuous improvements, tailored to specific users’ needs for specific applications. Each firm follows a technological trajectory in search of improvements to their existing products (Breschi & Malerba, 1997, p. 146f). In this manner a firm’s technological understanding is enhanced and one dominant design can emerge. Each firm pursues “...a single technical option and, over time, become[s] increasingly committed to a single technological trajectory” (Saxenian, 1994, p. 112). The case study that Saxenian examines is the regional economy of Silicon Valley. In this instance, learning and technological change are cumulative in nature. Firms secure their knowledge base and then attempt to build upon it seeking new opportunities. In contrast the emerging auto-ID industry has a knowledge base that is still in its early development and the future impact of auto-ID product innovations is still very much speculative. Thus the need has arisen to look ahead and propose a map or attempt to understand the path dependency of these technologies. Foresight is necessary because it also allows us to see the potential long-range effects of technical change (Westrum, 1991, p. 344). The question of why it is important to forecast and what is to be gained by it, is very important to this investigation. The researchers believe that forecasting is essential, even if the predictions arrived at may not eventuate or even if some unexpected events happen that were not anticipated. It is better to make some logical predictions based on the evidence one has and be prepared for what lies ahead, than to find oneself completely unprepared.


Selection Environment and Other Terms

Having revealed the importance of technological trajectories, it is now appropriate to understand the concept of selection environment. As the term suggests it is the process that involves the interaction between the product and its environment. Lindley (1997, p. 25) phrases it well when she states that: “[t]he selection environment acts to influence the path of innovation and the rate of diffusion generated by any given innovation, and at the same time generate feedback to strongly influence the direction and type of R&D programs that firms might invest in.” Sahal can be credited with the popularization of the term technological guidepost. He stated that the basic design of a technological innovation acts as a guidepost charting the course of future innovation activity. To prove this he used an arbitrary example, highlighting that one or two early models of a product or process usually stand out above all the others in the history of an industry and their design becomes the foundation for the evolution of many innovations. “In consequence, they leave a distinct mark on a whole series of observed advances in technology” (Sahal, 1981, p. 33; see also Anderson & Tushman, 1990). This led Sahal to the principle of creative symbiosis, the case where “...two or more technologies combine in an integrative fashion such that the outline of the overall system is greatly simplified... when it [happens], totally new possibilities for further evolution present themselves” (Sahal, 1981, p. 75). This phenomenon has occurred in the auto-ID industry and will be discussed in the main body of the book.



The systems of innovation (SI) approach is a conceptual framework that can be used to study technological innovations. See Systems of Innovation: Technologies, Institutions and Organizations, edited by Edquist (1997) and Systems of Innovation: Growth, Competitiveness and Employment, edited by Edquist and McKelvey (2000). The approach, admittedly not an established theory, has been gaining prominence in the last decade. SI defines innovation as an evolutionary process, not as a process for achieving optimality. Edquist et al. (1998, p. 21) explain that “the notion of optimality is absent from the SI approach. The notion of optimality stems from static equilibria and therefore is not applicable to processes of technological change… [this] is a major contribution of evolutionary theory, which the SI approach has adopted.” SI is described well in the ‘Innovation Systems and European Integration Policy Statement’ (Edquist et al., 1998b, p. 3f): “The Systems of Innovation (SI) approach for understanding innovations in the economy stresses that firms do not normally innovate in isolation but in interaction with other organizational actors (other firms, universities, standard setting organizations, etc.) within the framework of existing institutional rules (laws, norms, technical standards, etc.). Institutions are not organizations. Rather, they constitute the rules of the game or framework conditions for interaction. In contrast, organizations are the entities (actors) that interact. From this perspective, innovation is a matter of interactive learning.”

The origin of this approach is well documented as proceeding from theories of interactive learning and evolutionary theories of technical change. Among researchers like Carlsson and Stankiewicz, Nelson and Rosenberg, as well as Lundvall and his colleagues, there is support for this approach stemming from its close affinity with the evolutionary theory (Edquist, 1997, p. 7). The “system” that Sahal once referred to has been defined in the SI approach (Edquist, 1997, p. 14f): “One way of specifying ‘system’ is to include in it all important economic, social, political, organizational, institutional, and other factors that influence the development, diffusion, and use of innovations. Potentially important determinants cannot be excluded a priori if we are to be able to understand and explain innovation. Provided that the innovation concept has been specified, the crucial issue then becomes one of identifying all those important factors. This could- in principle- be done by identifying the determinants of (a certain group of) innovations. If, in this way, innovations could be causally explained, the explanatory factors would define the limits of the system. The problem of specifying the extent of the system studied would be solved- in principle.” Using these definitions it becomes a simpler task to understand auto-ID innovation. These factors could be causally explained, limiting the scope of the actual system and thus presenting a conceptual framework within which to perform the research.


The Value of the SI Approach

The attractiveness of the SI framework is that it is a “holistic and interdisciplinary” approach which “encompasses all or most determinants of innovation” (Edquist et al., 1998, p. 20). Fundamental to its doctrine is that “history matters” since innovation processes take time to evolve. Queisser’s (1985) historical account of The Conquest of the Microchip highlights this point well. In the Foreword of this book, Robert Noyce (p. viii) writes regarding the microchip: “[t]hose of us who have been involved in the development of this technology recognize that the terms technological revolution and breakthroughs are used to attract public attention to the progress being made, in reality progress is almost seamless, with pieces of the puzzle continually being put in place until a coherent picture emerges.” Noyce is suggesting that the microchip revolution happened through evolutionary steps, i.e. the notion of revolution through evolution. These two words are often used at different levels by technologists in auto-ID. One expert may refer to the smart card revolution and another may refer to the evolution of card technology from magnetic-stripe to smart card.

Thus by understanding the past one is better equipped for the current and future patterns of innovation. This approach is also used by some futurists like Adrian Berry. He writes “[s]o that my subsequent chapters will be intelligible, I must explain what has been happening in the past five hundred years and how it relates to the present” (Berry, 1996, p. 19). In a paper titled ‘Unfaithful offspring? Technologies and their trajectories’, S. Hong (1998, p. 262) challenges the notion that a technology’s trajectory is autonomous or its development unpredictable and uncontrollable. Hong (1998) chooses to examine three technologies including the Triode, the numerically controlled (NC) machine tool and the Internet. He concurs with an underlying philosophy of SI; that it is rather an “imperfect historical understanding of a technology [that] largely contributes to the idea that the technological trajectory is uncertain, and, therefore, autonomous.” In examining the high-tech industry in Sweden, McKelvey et al. (1998) traced the historical changes that occurred in the Swedish mobile telecommunication system from the 1970s till the 1990s to attain a better understanding of the industry dynamics. More recently McKelvey has shifted her focus to studying modern biotechnology (McKelvey, 2000; McKelvey, Rickne, & Laage-Hellman, 2004; McKelvey & Orsenigo, 2006). In like manner this study will explore auto-ID innovation. The SI approach embodies nine characteristics, among which is that they may “employ historical perspectives” (Edquist 1998, p. 8).

The SI approach successfully brings together the conventional teachings of various experts in the innovation field from all over the world. It has not only been adopted by the Europeans but also by researchers in Asia and North America. To understand its origin one must first look at the many case studies that have been conducted using a systems view of evolutionary theory. The term ‘national systems of innovation’ was first used by Chris Freeman in 1987. Lundvall then used it as a chapter heading in Dosi (1988). In 1989, Brown and Karagozoglu published a paper titled, ‘A systems model of technological innovation’. Following in 1992, Lundvall titled his book, National Systems of Innovation: Towards a Theory of Innovation and Interactive Learning.

The research that acted to launch the SI framework was National Systems of Innovation: A Comparative Study (1993) by Nelson and Rosenberg that will be critically analyzed below. Lipsett and Smith (1995) then published a paper titled, ‘Cybernetics and (real) National Innovation Systems’ challenging some of the ways that Nelson and Rosenberg treated their subject matter. The former made some very good points that are worthwhile noting but were supportive of the approach used overall. They called for researchers using the national systems of innovation to improve their information base and chosen metrics of analysis, and for more participants to get involved in discussions. They also emphasized the need to understand that system dynamics are different when humans are involved and relationships are established. In 1995, Carlsson and Stankiewicz completed defining the technological systems (TS) approach. The TS program focused on both theoretical studies and empirical studies. At the fifth International Conference on FACTORY 2000, Keating, Stanford and Cope (1997) also contributed to the idea of systematic technology innovation.

In retrospect most of these publications carried the word ‘national’ in their titles. See also the ISE policy statement (Edquist et al., 1998, p. 20): “[i]nitially the SI approach was dominated by the national level. However, other systems of innovation than those defined by a country criterion, should be, and are being, identified and studied… Leaving the geographical dimension, we can also talk about ‘sectoral’ systems of innovation (i.e. ‘technological’ systems that include only a part of a regional, national or international system).” The focus of this book is to make an enquiry at the specific auto-ID ‘technology system’ (TS) level. More recently, the validity of national innovation studies has been questioned (Edquist, 1997, p. 11). The terms sectoral innovation system (SIS) and technological system (TS) are often used interchangeably by researchers. The reason for this is that both focus on the firm as the central actor. There is however a subtle difference, TS is in actual fact a subset of SIS. For instance, auto-ID is the TS system being analyzed in this book and the industry belongs to the larger sector of electronics and IT. See examples of SIS case studies in section two of Henry (1991, pp. 129-239) and part three of Rosenberg (1994, pp. 159-250). It is the opinion of these researchers that TS studies can influence organizations more directly than national studies which are often aimed at government bodies. It is much easier to shape an industry than a whole nation.

What is so special about SI is that it “...allows for the inclusion not only of economic factors influencing innovation but also of institutional, organizational, social, and political factors. In this sense it is an interdisciplinary approach” (Edquist, 1997, p. 17). SI should be looked at as a whole system because its elements are directly or indirectly related to each other. One advantageous aspect of SI is that each part of the system can be examined on its own or in relation to another one. To study one subsystem can also contribute to the understanding of the whole, to deal with individual elements whether they are technological, educational, organizational, social, cultural, economic or institutional in nature.


National Systems of Innovation and Other Technology Systems

National Systems of Innovation: A Comparative Study (1993), edited by Nelson and Rosenberg, was another landmark study propelling innovation thought forward. Like Sharp, Nelson and Rosenberg used a case study methodology but instead of choosing specific new technologies, the national systems of innovation of fifteen countries were investigated. Most of the case studies were conducted by resident researchers in each country. This may have complicated matters a little because different authors held different interpretations of the same concept of ‘national system of innovation’ (Edquist, 1997, p. 4). Nevertheless, the book was intended to emphasize empirical evidence first, then to confirm theory. This research spanning fifteen countries was a much larger undertaking than Sharp’s earlier projects in 1985 and 1989. The resources and efforts required, to collect the data and present it in a coherent way with a set number of features for each country was a mammoth task, obviously outside the scope of this study.

Findings from the case studies suggested that it did make sense to think of national innovation systems although there was some problem with identifying national borders (Nelson & Rosenberg, 1993, pp. 20, 506). Other difficulties included comparing and contrasting countries with varying economic and political circumstances, and distinguishing cross-border operations such as with transnational firms. At the conclusion, there was evidence to suggest that the technological capabilities of a nation’s firms do have an impact on its ability to remain competitive globally. However not all research tasks can produce results at this macro-level, nor is it a requirement.  In 1998, Choung authored an article titled, ‘Patterns of innovation in Korea and Taiwan’, examining thirty-four technical fields among which were telecommunications; semiconductors; electrical devices and systems; and calculators computers and other office systems. While Choung identifies Korea and Taiwan as the geographic settings for his study, the focus is on the thirty-four technical fields not on the actual countries.

Micro-level projects have their benefits also. They are likely to uncover more detailed results and recommendations that are easier to implement within a firm or industry. The issue with a national comparative study is, who are the results aimed at and what types of organizations are willing to react to the findings? Consequently the term systems of innovation without the word ‘national’ has become more acceptable as a framework, giving the researcher the flexibility he or she needs to work at any level; national, regional, sectoral or industry-specific. Evidence of the SI framework is particularly apparent throughout Nelson and Rosenberg’s (1993, p. 15) introduction: “Technological advance proceeds through the interaction of many actors. Above we have considered some of the key interactions involved, between component and system producers, upstream and downstream firms, universities and industry, and government agencies and universities and industries. The important interactions, the networks, are not the same in all industries or technologies.”

 Contrary to both studies conducted by Sharp and, Nelson and Rosenberg, this investigation will focus on a single industry, i.e. auto-ID. Nelson and Rosenberg (1993, p. 13) themselves deem this to be positive, acknowledging that “...there are important interindustry differences in the nature of technical change, the sources, and how the involved actors are connected to each other...” Carlsson and Stankiewicz (1991, p. 111) are also in agreement with a technology and industry specific focus. As mentioned, they have termed this idea “technology system” (TS) where upon a “...greater emphasis is placed on the way specific clusters of firms, technologies, and industries are related in the generation and diffusion of new technologies and on the knowledge flows that take place among them” (Breschi & Malerba, 1997, p. 130).


Empirical Studies at the Sectoral Level

In Henry (1991), some very interesting papers were published in Forecasting Technological Innovation; proceedings of the Eurocourse lectures delivered in Italy the previous year. Many of the case studies presented used a sector-level approach and investigated high technologies such as advanced materials (Malaman), machinery and automation (Parker), electronics and information technology (Drangeid), telecommunications (Bigi & Cariello), transport (Marchetti) and biotechnology (Roels). The study on telecommunications is the one with which this forthcoming auto-ID study will be most closely aligned stylistically. Rosenberg (1994) also uses a sectoral approach in his case studies of technological change in energy, chemical processing, telecommunications, and forest products. Banbury’s 1997 study however, Surviving Technological Innovation in the Pacemaker Industry, 1959-1990, is that study that can be considered a precursor to this book. Banbury’s work shows the validity of a micro-level investigation on an individual industry and the excellent results which can be achieved.

In so far as the method is concerned for this research project, case studies will be more exploratory in the style of Sharp than Nelson and Rosenberg. The results of the study are not meant to be quantifiable (e.g. how many patents, how many firms) but rather qualitative (e.g. what are the organizational and institutional dynamics). In terms of the structure of the literature review, this now concludes the space dedicated to the review of theories, theoretical frameworks and methods adopted by other researchers doing similar innovation and diffusion studies.



The fundamental purpose of the review on innovation literature was to establish a plausible conceptual framework within which to study auto-ID innovation. This study will use key terms and concepts defined by researchers in evolutionary theory, to describe trends that are occurring in the auto-ID industry. In approach, it will incorporate the interdisciplinary SI framework to identify the factors affecting innovation and diffusion in the auto-ID industry. As shown from the critical review, the framework of SI is very flexible. It allows the researcher to include and exclude data dependent on the scope of the problem, macro or micro in level. By collecting the data using a case study methodology, this study will also be adopting the example of many other researchers (e.g. Sharp, Nelson and Rosenberg) in the field of innovation. Case studies are compatible with the kind of research that requires exploration and are common in books on automatic identification. Indicative of the widespread applicability of the SI framework is the support it has gained from researchers all over Europe especially. Researchers in business, economics, sociology, industrial management and information technology have contributed to its development (see Edquist, 1997). While some may regard this to be a disadvantage, interdisciplinary research has boomed in the 1990s and has even been adopted by researchers in Asia and North America.



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