The structure and dynamics of technological systems: a conceptual model

Technology In Society. Vol. 17, No. 4, pp. 365-384, 1995 Copyright 0 1995 Elsevier Science Ltd Printed in Great Britain. All rights reserved 0160-791X/95 $9.5&0.00

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The Structure Technological

Dynamics of Systems: a Conceptual Model and

Erkko Autio and Ari-Pekka Hameri

This paperfocuses on the construction of an integrated model of technological systems. The model has two purposes: (I) to clarifv the scope and relations of various concepts in this peld, especially the concepts of technology, of national systems of innovation, and of technological systems; and (2) to provide a framework in which to study the conceptualization and construction of evolving technological systems. These purposes require the creation of new concepts and the redefinitions of some familiar ones. The model is structured at four levels of aggregation: (1) the individual; (2) the organizational; (3) the sectoral; and (4) the national. From within this structure we can ident@ parallaxes, or changes in the appearance of each of the four levels as the level from which observation takes place is changed. The structure of the model and the concept of parallax serve to clarify some important complexities of the dynamics of the system within which technological change takes place.

ABSTRACT

Erkko Autio received his degrees of Master of Science and Licentiate in Tecbnology at the Helsinki University of Technology, in industrial management. He was a Visiting Fellow at the Science Policy Research Unit (SPRU) of the University of Sussex in 1994. Autio has published articles in Technovation, International Journal of Technology Management, Small Business Economics, and in Entrepreneurship and Regional Development among other Journaki. Currently, Erkko Autio is Director of the Center for Technology Management of Helsinki University of Technology. Ari-Pekka Hameri received his degree of Doctor of Technology at the Helsinki University of Technology, in production management. He has visited the Maastricht Economic Research Institute on Innovation and Technology (MERID at the University of Limburg and at the University of Sussex as a visiting researcher. Currently, be is associated with CERN and the technology transfer issues related to major international basic research centers.

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Introduction In this paper we attempt to construct an integrated model of technological systems. The model has two purposes: first, to clarify the scope and relations of the various concepts in this field, especially the concepts of technology, of national systems of innovation, and of technological systems; and second, to provide a framework in which to study the conceptualization and construction of evolutionary simulations of technological systems. The two purposes require the creation of some new concepts and the redefinitions of some familiar ones. The starting point in our model construction is at the level of the individual, from which we proceed to the organizational level, then to the sectoral level, and finally to the national level of technological development. We pay special attention to the interrelationships among these four levels, or layers of aggregation in technological systems. Within these interrelationships we are especially interested in what we call “technological parallaxes,” or changes in perceptions of the object (in this case, the layer of aggregation), that result from changes in the vantage-point, or layer, from which perception takes place. The concept of technological parallax then serves to illuminate some of the complexities concerning the interconnections and relationships among the four layers of technological systems, and makes it possible for us to suggest that it is the most basic layer, that of the individual, that is the ultimate source of uncertainty and change. Various concepts and models for the treatment of industrial and technological change have been proposed and discussed. For example, it is generally accepted that the concept of “an industry,” defined as a combination of related markets and production systems, is of limited use as a basis for studying industrial and technological change. Some industries may be connected to each other through a range of user-supplier relationships. Others may be sharing the same knowledge base.’ Industries can also be viewed as clusters, and their survival may be dependent on the success of the cluster of industries as a whole and not on the various firms constituting the industry itself. The concepts of technology, on the one hand, and industry, on the other, are often (but not always) closely interrelated. Many basic technologies are industry-specific. This means that the knowledge base necessary for their creation, and their scope of use, are closely tied to a specific need or to a particular market. The evolution of such “industry-specific” basic technologies is often driven largely by the underlying need which the basic technology addresses. Other “paradigmatic” basic technologies may span several industries. The evolution of “paradigmatic” basic technologies is usually not determined by the evolution of any specific industry; rather, the evolution of the basic technologies themselves often constitutes a major driving force for change in the industries in which they are used. The concepts of technology and of industry are similar enough to make it necessary to develop different concepts to analyze technological change.

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By focusing on the construction and dynamics of technological systems, we

can discuss and develop more useful concepts and models related to technology, technological systems, and technological change. It is especially helpful, we have found, to pay particular attention to the organization of technological systems, and to interrelated levels of aggregation that can be distinguished in technological systems. It is necessary to begin, therefore, by defining in considerable detail some of the terms that are necessary to this discussion.

Concepts of Technology and Technological Systems Technology The nature of technology and technological knowledge determines the nature of technological systems. Most often, technology is defmed in a very narrow way, with the definition restricted to some element of technology, or to a specific point of view. Technology may be defined by its physical manifestation, i.e. hardware, or in a generic way, i.e. manufacturing.* Some authors define technology as technical knowledge instrumental in the production of goods and services. 3 Behavioralists include the distinctly human element, arguing that since human talent, skill and effort are also required for the solution of technological problems, they should be included in the definition of technology. These human capabilities are sometimes referred to as the tacit component or tacit form of technology,* or as “sticky data.“5 According to this view, technology can assume various manifestations, ranging from physical artifacts to epistemic utilities and tacit development capabilities residing in the heads of R&D engineers. From the point of view of technology dissemination, different manifestations of technology have different qualities. Some, such as physical products, manuals, patent information, and scientific articles, are easily and rapidly disseminated. Other manifestations, such as development capability, are more difficult to disseminate rapidly. As demonstrated by the history of the Strauss dynasty in 19th Century Vienna, some highly tacit skills can only be passed from one generation to the next, and even then, change plays a major role. Technology is conventionally defined as the application of science to industrial or commercial purposes. For our discussion of technological systems, however, a broader definition is required. In this article we propose to use the term to include both knowledge and hardware, as well as the capabilities required for creating and utilizing technology. We add the following to the definition of technology: Technology comprises the ability to recognize technical problems and to develop new concepts and tangible solutions for them. The definition also includes the concepts and tangibles themselves, as well as their efficient use.

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Our use of this broad concept of technology includes four components: (1) coded information, such as documents and manuals; (2) physical hardware and other artifacts; (3) individual skills, and (4) organizational competencies. The concepts and tangible solutions may take the form of systems, processes, methods, products, and services, or any combination of these. This fourcomponent approach to defining technology provides a basis for the theoretical and conceptual treatment of technological systems. A discussion of technological systems requires a definition of their conceptual boundaries. How should one define what disciplines or areas of technology constitute a particular technology or the basis of a technology area? This question is not simple and straightforward. Sometimes the boundaries of one technological area follow the boundaries of an industry or cluster of industries. For example, one might talk about factory-automation technology, or microprocessor technology, or, more inclusively, information technology. In other cases, the boundaries of one technology area are best defined as the boundaries of an academic discipline, such as neural processing technology. Carlsson and Stankiewicz discuss this problem.‘,’ They point out that increasing complexity and scientific content, particularly of science-derived basic technologies, make it more difficult to define the boundaries of a particular technology. The reasons for this include both an increased level of articulation and transferability of technological knowledge and increased fusibility of technologies.8*9 The increased fusibility and interdependence of technologies means that the physical manifestation of technology in any particular area of application frequently takes the form of a system that is composed of different elementary, or basic, technologies. This is why Carlsson and Stankiewicz emphasize the importance of economic competencies in the definition of the boundaries of technology areas: the presence of similar economic competencies can be used to define a technological system. This approach takes the definition of a technology area quite close to the definition of an industry. The existence of similar economic competencies alone may not always be a sufficient criterion when defining the boundaries of a technology area. Economic competencies can be developed only after a large enough market or customer base has been developed. Therefore, it is important to make a distinction between the early stages of the development of technology area and its more mature stages. In the early stages, the boundaries of a technology area can best be drawn along the boundaries of the academic discipline from which it is emerging. As the often chaotic early stages have been passed, and the technology has matured, the boundaries of technology areas can be drawn along the corresponding industry boundaries. At more mature stages, industrial development blocks” or value-creating systems” can be used as the basis for definition. As the above discussion suggests, technologies are hierarchical, and the aims and purposes of the observer often dictate the definition to be used. In the early stages, technology-related criteria and competencies often dominate, whereas in more mature stages, economic criteria and economic competencies become increasingly dominant.

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National Systems of Innovation The concept of national systems of innovation (NSIs) was first introduced by Freeman and Lundavll,’ ‘- ’ * and similar concepts have been introduced somewhat earlier. Other scholars, e.g. Nelson and Porter15-l7 have also used concepts that resemble those of Freeman and Lundvall. The approaches of these authors are similar, though differences in focus and emphasis exist. Interestingly, Lundvall gives no strict definition for NSIs, even though he focuses on them in his book. He discusses only various views and characteristics of NSIs. Freeman, on the other hand, defines NSIs as the constellation or network of agents participating in the creation, use, and diffusion of new technologies in the national context. These agents can be both public and private and can include coordinating, supporting, and productive institutions. Freeman explains how the system comprises the various interactions among the agents, the most important of these being the one between users and producers of technology. International technology flows and exchanges are also represented as playing an important role in an NSI. It is important to note that NSIs do not focus purely on technological innovation. Systemic and social innovations such as organizational changes are also viewed as important. Organizational changes can be derived from the production and utilization components of technology. This variety of types of innovation makes it difficult to define an NSI. It is especially difficult to determine to what extent the social and cultural facets of a society should be included in the definition. The process of technological innovation, for example, often cannot be separated from the cultural and socioeconomic context in which it takes place. The socioeconomic, cultural, and technological facets of a society are interdependent: a change in one is both affected by, and likely to cause changes in, the others. For example, Japanese institutional innovations, such as the just-in-time manufacturing system, Kanban, are very much influenced by the Japanese culture. Most discussions of NSIs have been based on case studies and have not attempted to provide a general framework or to identify the basic structure of NSIs. Only lately have efforts been made to develop more general models of NSIs.” One of the few illustrations is the popular diamond model by Porter. From the point of view of our discussion, NSIs can be viewed as metasysterns, of which technological systems are one part. An NSI is a system geared to promoting the emergence, diffusion, and utilization of new technologies in the national context, combined with innovation and commercial exploitation. One NSI can comprise any number of technological systems. In addition to technological systems, NSI’s also incorporate parts that are not included in technological systems, such as finance, labor, and markets. Technological Systems We take as our starting point the definition of technological systems pro posed by Carlsson and Stankiewicz. They define a technological system as: “a

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network of agents interacting in a specific technology area under a particular institutional infrastructure or set of infrastructures and involved in the generation, diffusion, and utilization of technology.“19 Carlsson and Stankiewicz point out that their definition is based on knowledge and competence flows rather than flows of ordinary goods and services. The network in which the knowledge and competence flows take place often is, but does not have to be, limited by national geographical boundaries. Carlsson and Stankiewicz prefer to view technological systems as regional rather than national networks, the limits of which may extend beyond national borders. As mentioned earlier, technological systems can be regarded, to some extent, as subsystems of NSIs. One NSI may comprise several technological systems. On the other hand, technological systems need not necessarily be restricted by national borders, and one technological system can extend beyond several NSIs. In Europe, for example, transnational technological systems are commonplace. The transnational character of technological systems means that they can follow development patterns which may be quite different from those of the NSIs in any given country. In a transnational technological system, the locus of development may shift from one country to another, as different countries are likely to follow different development patterns in different areas of technology. Yet, technological systems cannot be totally detached from national environments. Few truly global technological systems are likely to emerge in the foreseeable future, due to the local character of some components of almost any technology, such as individual skills and organizational competencies. Trading communities do not yet form fully integrated economies, and differences in culture, traditions, and individual experience would in varying degrees persist in any case. It might appear that the concepts of NSI and technological systems are quite close, but it is important to clarify the distinction. The essential characteristic of an NSI is its institutions: an NSI provides the institutional framework in which technological systems can emerge and develop at the national level. Technological systems, for their part, are less independent of institutions. The concept and behavior of technological systems are dominated by the concept and behavior of technological knowledge. This characteristic makes technological systems more dynamic than NSIs. Technological systems can be viewed as pools of technological knowledge, with the various agents participating in the process of creation, diffusion, replication, and use of this knowledge. Because of the concentration of knowledge, the dynamics of technological systems are not dictated by the institutional framework as much as that of NSIs. In the case of technological systems, factors affecting the transferability of technological knowledge become more important. This means that technological systems can encompass widely different development patterns, as compared with institutionalized NSIs. Some of the characteristics of technological systems include path-dependency, emergence and lock-in to technological trajectories, cross-fertilization

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between technological systems, and changes in the manifestations of technology in different evolutionary phases. The evolution of technological systems is driven by technology flows within the system. These flows are caused by the processes of technology transfer and diffusion. Of these, technology transfer is caused by intentional, goal-oriented action, and thus is the primary mechanism which gives life to technological systems. We have pointed out elsewhere that the process of technology transfer is component, phase, and interface dependent2’ Here, component dependency refers to the four-component model of technology, as we defined it above. Phase dependency, for its part, refers to the process of technological innovation, in which different phases can be distinguished even though the process itself is often neither linear nor sequential. As applied to technology transfer, interface dependency refers to the interfaces among the various agents participating in the technology transfer process, and not to the earlier interfaces between different phases of the outdated linear model of technological innovation. The three contingencies shaping the dynamics of the process of technology transfer are not entirely dissimilar. Certain mechanisms are likely to be dominating at specific interfaces and in particular phases. Certain technology transfer mechanisms are likely to be dominated by distinct components of technology. Foor example, in the “early,” or “raw” phases of the technological innovation process, intangible components of technology are likely to dominate, since epistemic outcomes are likely to precede practical, more tangible outcomes. In other words, because knowledge is required for the creation of artifacts, the early phases of technological innovation process are likely to be dominated by knowledge-creation-intensive technology transfer mechanisms, and not by artifact-replication-intensive mechanisms. For example, as Freeman andd Perez 21 have pointed out, the lock-in of technological systems to certain technological trajectories is often preceded by a search phase. There the generic technology may take a large variety of different physical manifestations. After the lock-in has occurred, the configuration of the system changes to support the achievement of replication efficiency, through standardization and cumulative learning. Moreover, the interfaces among research institutions, and between research institutions and industrial companies, are likely to be dominant in the early phases of the development of a technological system. As a technological system matures, the balance of the dominating phases, interfaces, and components can be expected to change accordingly. After trajectory lock-in, the replication-intensive technology transfer mechanisms often dominate knowledge-creation-intensive mechanisms. At the same time, the manifestation of technology itself changes, often taking the tangible form of a physical artifact. The change from knowledge creation to artifact replication often means that the locus of innovation gradually shifts from research laboratories to industrial networks, the configuration of which is dictated by the microeconomics of the network.

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The phase, interface, and component dependencies of technology transfer mechanisms, as well as the interdependencies among these three characteristics, are illustrated in Table 1. In Table 1, we illustrate how the dominating phase, interface, component, and technology transfer and diffusion mechanisms can be expected to change over different stages of development of a technological system. In our illustration, the development stages are divided into early emergence, late emergence, trajectory lock-in, and stability and growth stages. The phases of the innovation process are divided into fundamental research, basic research, applied research, R&D, and manufacturing. In the manufacturing phase, replication-intensive mechanisms and capabilities tend to dominate over development-intensive capabilities. The interface

TABLE 1. Interdependencies Among Phase, Interfkce, and Component Dependendes of Technology Transfer Mechanisms in Diffbrent Development Stages of Technological Systems Stage of development of a technological system

Phase of the innovation process

Interfaces

Early emergence

Fundamental research, basic research

Among basic research laboratories

Late emergence

Basic research, applied research

Trajectory lockin

Applied research, R&D

Stability, growth

R&D, manufacturing

Components

Dominating transfer and diffusion mechanisms

Development Research cooperation, capability, coded knowledge information dissemination Among basic Development Research and development research capability, coded co-operation, laboratories, industrial R&D information, transfer of physical departments, people manifestations and new, teclmologybased companies Physical R&D consortia, Between industrial R&D manifestations, technology development acquisition departments, capability and new, technologybased companies Replication Internal process Interfaces within industrial capability, development, manufacturing physical value creating investments manifestation system

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refers to interfaces among various agents participating in the innovation process. The components of technology are divided into development capability, coded information, physical manifestations, and replication capability. As Table 1 shows, the center of activity of technological systems normally shifts from basic research laboratories toward applied research laboratories; new, technology-based firms (NTBFs); R&D departments of large industrial companies; and customers. In terms of industrial organization, the early phases of a technological system are often, but not necessarily always, dominated by a relatively large number of NTBFs. NTBFs are often needed to break out from the traditional ways of doing things. Sometimes the emergence of new technological systems directly threatens the continuance of existing systems. In such situations, NTBFs are often needed to test and prove the alternative technology, and thus initiate the change in the status quo. If the capabilities required for mastering the new, alternative solutions are far enough from the capability pool of the existing system, NTBFs may have a chance to become dominant in the new technological system. Quite often, though, the relatively small NTBFs leading the emergence of a new technological system end up being acquired by the relatively large, dominant actors of the existing system, and help transform the capability base of the dominant actors of the existing system. 22 This is why technology acquisition often is the dominant technology transfer and diffusion mechanism during and after the trajectory lock-in stage. In the stability and growth stage, the industrial manifestation of a technological system may take different forms, such as the varying configurations of innovation networks.23

Structuring

Technological Systems

Having discussed various concepts related to technology, we can now pro teed to developing a model for structuring technological systems. We will identify and discuss four layers within technological systems. These layers are: individual, organizational, industrial/sectoral, and national levels of aggregation. Each of the layers depicts different modes of behavior, partly affected by the characteristics of the other layers. After having discussed individual layers, we will discuss the interconnections among different layers, including the shifting perspectives which we have termed technological parallaxes.

Individual

Layer

Technological decisions are based on bounded rationality. This implies that human action is more or less restricted by the context in which it takes place. The innovation process, although intrinsically uncertain, is based on human decision making, and is therefore somehow understandable through “extensive empirical research.“24 In a very strict sense, the innovation process can be viewed as a sequence of separate, but not independent, decision

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actions, each of which is temporally and contextually bounded within situational constraints. Stiglitz, for example, maintains that the accumulation of learning makes further technical innovations easier and prepares the ground for further technological progress.25 Sahalz6 states that innovations depend upon learning to learn, unlearn, and re-learn. Shapere discusses the same process, learning to learn, in science.27 Simon’s aim is to point out that human decision making, although possessing some unpredictable features, can be studied, and he warns us not to think of it as a “black-box.“** Technological action usually takes place in a technological trajectory,29 which is a part of the prevailing techno-economic paradigm. The distinction between trajectories and paradigms is vague. A trajectory could be defined as a special subparadigm of a given paradigm. Kuhn views paradigms as hierarchical structures: one paradigm may be part of other, perhaps global paradigms. 3o Both trajectories and paradigms describe the epistemological and practical attitudes of the technological system, only at different levels. A paradigm concerns entire basic technologies (information, medical, etc.), while trajectories describe certain specific technological characteristics of basic technologies (computational speed, efficiency of fuel consumption, etc.). To understand individual action, we must examine the concepts of knowledge and mental processes on which it is based. Knowledge - both scientific and technological - used in decision making, is accumulated through diffusion and in-house R&D that take place in the technological system. This knowledge also provides the basis for imitation and innovation. In the case of product innovation, the motivation for action is usually to increase profits. A close connection with customers is often necessary. Market events create opportunities that might overcome the agent’s aspiration level and in this way trigger the technological action. The mental characteristics of the individual determine which action is to be selected and how it is to be performed. The outcome of the action, if successful, results in a change in the technological system and profits will accrue. Whether the action is prominent or not, it changes the knowledge base of the system and affects its underlying mental concepts. This sort of individual action may lead to further innovation, but at the very least, it catalyzes some sort of diffusion process. The above discussion of the role of individual action as a source of change in technological systems follows the ideas of the behavioral approach to research on technological change. The basic view, however, is largely the same as is embedded in the concept of technical norms used in action theory models.“’ In short, a technical norm can be presented as follows: If you wish to achieve A and you believe that you are in B, do X! Solving a specific problem or improving an artifact (product or process) forms the objective, A, of the action; the contextual variables preceding the action form the understanding of the decision situation, B, the technological action itself, X, is the result of the inference between A and B. The goal of

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technological action is to establish some kind of change, X, in the prevailing technological system, which, if successful, leads to technological ditfusion and profits. The situational constraints will change after the action has taken place and also because of other technological actions and their effects.

Organizational

Layer

Most human efforts require the sharing of work. This implies that work to be performed in an organization must be structured. The effectiveness of an organization depends upon its capability to efficiently create, share, and exploit information among individuals, while striving to meet the external goals set for the organization. Usually the goal of an industrial organization is to survive and prosper by producing artifacts in an efficient manner. Striving for internal and external efficiency dictates that the creativity and innovativeness both in the production process and in the product itself affect the success of the company. In order to remain competitive, a firm must continuously adapt its structure according to changes in its technological and economic environment. Organizational flexibility has become a critical competitive factor. In his flexibility framework, Slack makes a clear distinction between resource flexibility and manufacturing system flexibility.32 Resource flexibility is related both to the technology itself (machines and linkages between machines), to labor (skillful workforce capable of working in different departments and the system’s ability to adjust the number of people employed), and also to the flexibility of suppliers (delivery times, reliability) and controllers (of orders, production loading and scheduling, and quality) functioning in the production system. Manufacturing flexibility is related to flexibility in the production of new and improved products, to the range or mix of the products, to the changes of the aggregate volume of the manufacturing system, and to matching changed or planned delivery dates. An ultimately flexible firm is independent of both past and future events, i.e. the reaction time for the changes or disturbances in the market situation or production system approaches zero. In general, an industrial organization can be seen as an actor subject to the same situational constraints as an individual. Thus, the concept of technical norms could also be applied to industrial firms. Whether viewed from the strategic or from the operational level, some rationale should be found in a firm’s action that could be expressed in the form of a technical norm. As it is with individual creativity and inventiveness, a firm has to maintain some loose organization and uncertainty at its core to be capable of producing something novel and unique. Too strict and rigid an organizational structure inhibits creativity. Through constant change, together with increasing interaction and communication among various parts of the organization, the in-house resources will be maximized and the organizational learning triggered. The accumulated and tacit knowledge embedded in the organization is actually embedded in human networks.

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The more complex the tasks the human network performs, the more specialized the subnetworks it has to have. Work is divided and subdivided into groups, each accomplishing its own part of the whole. If the network is broken and part of the knowledge is lost, the network must be restructured either by reorganizing existing knowledge or by acquiring new knowledge. The lost knowledge, when entering a new network, should find its place through intensive interaction with the existing knowledge of the new organization. In both cases, the adaptation to the new situation should be seen as a chance for creativity and change. Interaction and communication between individuals in an organization should be as broad as possible. To characterize technical change at the level of the firm, four issues become central. Integration of the production systems and the knowledge embedded in the firm is necessary, including inter-firm aspects. Technical change through innovation creates uncertainty, but is also necessary in order to compete and survive, making for difficult decisions in management policies. Flexibility is the other key factor, apart from technical change, affecting future success. Finally, assessment issues form the fourth cornerstone at the firm level. All four issues aim to promote customer service and satisfaction, which ultimately determines the success or failure of the firm.

Sectoral or Industrial

Layer

Firms belonging to the same industry usually share the same markets (i.e. the basic functions of their products are equivalent) and use basically similar production methods and raw materials. They may differ in terms of personnel, productivity, work organization, management policies, and in terms of the environment (physical, geographic, social, political, and legal) in which they operate. Many factors that affect a firm’s operational effectiveness are determined outside the firm. For example, the bargaining process between trade unions and employer’s associations exercises an important influence on labor costs. Thus, external factors such as the industrial relations system together with technical change continuously mold the firm’s operational environment. There is also a clear interaction between the industrial relations system and technical change. On the one hand, technological advances change the work environment, possibly to the extent that it may become necessary to control the change through legislative measures. On the other hand, the legislative process may affect technological change by limiting, guiding, or even prohibiting its evolution. The connection between governmental action and industrial evolution is often evident. Matsuoj” explains that Japanese technology policy aims to develop new businesses, the domestic market, and to secure domestic investment opportunities by refining and changing the industrial infrastructure. By industrial infrastructure he means a much broader system than the industrial relations system. At the end of the 1980s the plans to improve

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the Japanese industrial infrastructure included an active R&D policy, development of information and communications systems, high-speed traffic networks, and life-related environmental facilities. The ultimate aim of these reforms was (and still is) to catalyze techno-industrial innovation. The idea is not only to concentrate on single industries, but also to increase the interaction among industries. Speeding up the diffusion of production techno-

logies and inventions among the industries is one key for developing national competitiveness. New technologies usually have a long history before they give birth to new industries. The government role is crucial in assuring and supporting the flow of knowledge from public research to productive sector. No single firm has the resources to take the risks of developing a new technology (industry), the objectives of which are uncertain, when it has no clear estimate of payback. Like biotechnology, the other “new” technologies and industries (information and communication technologies) are strongly science-based, with close links with basic and academic research, originally governmentally funded. Using the evolutionary approach and with the help of Coombs3* we can easily produce an aggregate step-wise description of the industrial structure and its evolution, from the market point of view: 1. Knowledge accumulation through academic research and R&D efforts, leading to seminal inventions 2. Early innovations by a few pioneering companies, leading to rapid growth of potential market entrants, through diffusion and imitation processes 3. Increase in the number of producers 4. Competition for market share, entrants and exists canceling out with zero net entry, i.e. selection of the fittest 5. Negative entry, or shake-out, i.e. selection from among even the fittest 6. Industry becomes mature, minor fluctuations in the number of entrants and exits.

National and Global Layer

Traditional indicators of a nation’s economic performance include GDP, balance of trade, rate of unemployment, and inflation. These figures are systemically related, but they do not fully explain why, for example, Japanese automobile plants turned out two or three times more cars per worker than U.S. or European plants in 1990. 35 Such considerations spurred intensive study of the competitive advantage of nations. Within the scope of this article, we will discuss some of the main characteristics of the different traditions of manufacturing at world level. There are several things to be considered by the governments in order to encourage industrial dynamism. These issues are addressed in national innovation policies, the formality of which may vary from country to country. Finland, for example, has a very hands-on and well-formulated blueprint

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of its national innovation include the following:

policy. 36 The issues discussed in such blueprints

l

l

l

System. At the industrial level, a skillful workforce enables initiative and quick response to technical change, and at the national level scientific research provides a basis for techno-industrial innovation. In practice, it is more important to concentrate on learning how to learn than to accumulate a large amount of scattered knowledge. Boyer concludes that many studies in OECD countries, Newly Industrialized Economies, and Third World nations show that a good general education is a prerequisite to any economic growth strategy.37 Freeman states that the contribution of Japanese universities both to world scientific research and to Japanese industrial innovation is often underestimated.‘8 The correlation between national economic performance and education level is evident. Also the Technology & Economy Program39 concludes that the human resources will play an important role in national competence, and that the emphasis is on qualitative aspects because of the decline in the quantity of human resources (e.g. by the end of this century, most OECD countries and other industrialized nations are expected to show a decrease in the absolute number of working-age population). Yet the educational system should not overconcentrate on technological issues. As Marchello points out, the basic definition of an educated person will remain relatively stable, i.e. we must continue to train young people to speak and write clearly, to manipulate mathematical and logical concepts, to be familiar with history, and to have an understanding of the world. Interaction and Cooperation Among Firms and Institutions. The relevance of interaction between different institutions has already been identified as the source of technical change and innovation. Perhaps the bestknown governmental institution to coordinate and promote a nation’s industrial competence is the Ministry of International Trade and Industry (MITI) in Japan. By concentrating on certain key industries (computers, consumer durable goods) MIT1 has orchestrated R&D attacks, using the knowledge of the main firms in order to achieve commercial success. MITI’s capability to join the experiences and visions of various firms is seen as one of the prime reasons for the Japanese prosperity.*l In the U.S., research support through the Department of Defense (DOD) has played a significant role in the emergence of new technologies and companies. The studies by Hagedoorn and Schakenraad42,43 clearly show the increasing importance of inter-firm partnerships and the use of cooperative strategies among large industrial corporations. Also the industry-university links have increased in frequency. Technological Incentives. Tax incentives have traditionally been used as a means for encouraging industrial firms to invest in productive physical capital. The ultimate goal in creating technological incentives is to catalyze innovation, not only for the benefit of industrial firms, but for the society as a whole, especially in areas such as environmental technologies.

Educational

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From the individual to the national level, technological progress has its impact on social values and ideologies, which in turn affect technological progress. This relationship between technological change and social change is not always entirely clear, yet it can be very powerfu1.44,45 Technological progress often destroys values and creates new ones (e.g. change in the respect of work from physical to mental), changes conceptions of the world (e.g. mass media reveal the hidden and make the secret public), and, in general, complicates the world in which we are living (e.g. the new artifacts require effort to examine, understand and evaluate). This general effect of technological change and progress calls for an increased attention to technological assessment in national policies. Technological Parallax It has become clear that the linear model of innovation is insufficient and sometimes even misleading. In departing from the linear model, researchers have introduced several astronomical terms in innovation theory. These include, for example, the concepts of trajectory and attractor. Following this tradition, we introduce the concept of technological paraZZax.It is used here to connect the four layers we identity in a technological system. The word parallax can be interpreted as the apparent change in the position of an object resulting from the change in the position from which it is viewed. This difference between the actual view and the apparent view exists both when the range of vision is great (e.g. viewing an adjacent star in comparison with distant stars), and when the range of vision is small (e.g. zooming in on an object which is close to the camera). In the context of technological systems, a technological parallax can be defined as the bias in observation, caused by the layer at which the economic actors participating in the technological innovation process are viewed. If the focus of observation is changed from one layer to another layer, this results in a change in the apparent position of the other layers. In other words, the layer at which the main emphasis is attached causes bias in the observation of other layers. The relations between different layers of a technological system can be interpreted as axles of technological parallax. The technological parallax axles link the above discussed layers, which constitute the technological system. The conceptual subsets attached to the different layers in Figure 1 are exemplary only. The main aim of the figure is to draw attention to the interconnections between the different layers and to emphasize the role of the individual layer as the ultimate source of technological uncertainty. The traditional division of economics between macro- and microeconomics is inadequate for technological studies and is extended here to include all relevant layers. The true nature of a technological system resembles the seamless web described earlier. This means that wherever in the web technical, economic, social, or political change takes place, the consequences affect all the other parts of the web. In the following the different parallax axles and their special characteristics are discussed.

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E. Autio and A. -P. Hameri TECHNO-ECONOMICS= {Nations, Industries,Technologies} = {Political ,Cuhure, Science} SOCIAL_INSTHUTIONS= {Education,Welfare,Judiciary) NOLOGY_POLICY= {Assessment, Incentives)

‘.I,%2iLzg ...**a . . . . . . . . .

Parallaxaxle

..

4::*...*...”

L-yRM

INDUSTRIES = {New, Old} Firms, Uncertamty) _ _.___,:____. uLu = {unions, Associanons, txonomtc_constramts, rumsI

= {Humanware,Hardware,Management} HUMANWARE= {Indntduals, Knowledge, Experience} HARDWARE = {Artifacts, Production_facilities} MANAGEMENT= (Otganizational_methods,Values, Finance.}

Parallaxaxle 1

. INDIVIDUAL= {Action, Uncertainty,Knowledge, Incentives} KNOWLEDGE= {Private, Public, Values} = {Change, Invention, Innovation} ENTREPRENEUR3 {Individuals}

Figure

1. Technological Different

l

l

Parallaxes and Parallax Layers of a Technological

Axles Connecting System

the

Purakzx axle 1. Compared with the other axles, the parallax axle connecting the individual layer and the firm layer seems to be straightforward and simple. Yet, this relation is complex and interesting when viewed from a technological and economic perspective. Employees are a central resource in nearly all firms. They use and create new resources for the firm. The unpredictable and at the same time logical nature of human beings operates within firms by reshaping the firm itself, the market structure, and sometimes, the whole industrial and techno-economic environment. The uncertainty characterizing technological and economic processes originates from this axle. Where and how new technological ideas and inventions arise, the various ways in which these seeds eventually grow and become innovations, and what the final outcome of these processes will be, make this axle worth studying. The understanding of the other parallax axles has to be based on a good understanding of this axle. But, as noted earlier, the whole parallax structure is intertwined and, thus, the constraints and features of the other levels should be taken into account when explaining interaction of people and firms. Puru2Zu.x axle 2. Here we confront the first parallactic bias of Figure 1, which gives the impression that firms are at a lower hierarchical level than industries and nations. However, we know that this is not always the case. The nationality and/or the industrial or technological sectors in which many global corporations operate are often very difficult to determine. This means that the parallax axle 2 can also be “turned” around, and the layers can change their places, depending on the situation. Often, the relation between firms and industries is a kind of “one-to-one” relationship, meaning that a firm may be operating in several industries. The same can also be said when comparing the firm and national layers. The industries and nation(s) in which the firm is operating form the environmental

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constraints, incentives, and opportunities for the employees of the company. The extensive research on global industries and multinational companies4”-48 reflects the importance of this axle in economic studies. Conand comparative advantage” and “global cepts such as “competitive platforms” illustrate the dynamics of the globalization process among industries and companies. Parallax axle 3. This axle overlaps with parallax axle 2. It is clear that nations can create industries together and that global industrial performance depends partly on national and partly on international policies. The numerous international coalitions are examples of efforts to direct and control industrial development at the international level. It is also evident that national undertakings play a crucial role when a nation’s contribution to global industries is measured and when new industries emerge. The interaction between the industrial and the national layer is complicated, and the parallactic hierarchy has to be determined case by case. The vast literature on governmental intervention as a catalyst to industrial development has dealt with this interaction from many (legislative, educational, economic, financial, geographical, etc.) perspectives. If the layers presented in Figure 1 are viewed from above, the result might look like the one presented in Figure 2. The four concentric circles represent the four layers of technological systems. At the core of the system is the individual layer, which interacts with the firm layer, the industrial layer, and the national layer. The concept of technological parallaxes becomes easy to visualize if each layer is imagined to form a convex lens, with different lenses piled on top of each other. A change in the position of any one of these lenses will result in a change in the view from above. If the lenses are correctly positioned, it is possible to see “through” the lens system down

Figure 2. The Four Layers of Technological

Systems

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E. Autio and A. -f? Hameri

to the individual layer. In these situations, the individual, or the entrepreneur, can be interpreted as playing the role of the primary motor and determining the development course of the whole system. From the point of view of evolution of technological systems, the interfaces between different layers play a crucial role. The interfaces between the individual and the firm layer and between the firm and the industrial layer are particularly important. These interfaces have been shaded dark in Figure 2. Technological knowledge and other information is disseminated through filters of communication between the different layers. Of these, particularly interesting is the one between the firm and the industrial level, denoted in Figure 2 as the R&D and marketing interface. This label does not refer to the interface between the R&D and marketing functions of the firm but refers to the interface through which the firm communicates with the four Cs, customers, contractors, collaborators, and competitors. In Figure 2, we have chosen to use the words “customer” and “contractor” for the more widely used words “user” and “supplier.” Firms communicate and cooperate with their customers, contractors, collaborators, and competitors, learning from each other, exchanging technological knowledge, and sometimes jointly developing it, in a constant struggle to survive and prosper. This interface is often the most dynamic and intensive interface, when technology flows within technological systems are analyzed.

Conclusion In this paper we have attempted to construct an integrated model of technological systems. The model has two purposes: first, it aims to clarify the scope and relations of the various concepts in this field, specifically technology, national systems of innovation, and technological systems; and second, the model aims to provide a framework in which to study the conceptualization and construction of evolutionary simulations of technological systems. The two purposes require the creation of new concepts and the redefinitions of familiar ones. The starting point in the model construction is at the level of the individual, from which we proceeded through the organizational and sectoral levels to the national level of technological development. Special attention was paid to the interrelationships among various layers of aggregation in technological systems, referred to here as technological parallaxes. On the basis of our layer model of technological systems, and our examination of four layers of aggregation within these systems, we have argued that the layer of the individual is the basic source of uncertainty and change. We developed and used the concept of technological parallax to depict the interconnections and relationships among different layers of technological systems. We then regrouped observations about technological systems, many of them familiar, in terms of the structure of the model, in order to clarify and organize our understanding of the interrelationships of the various elements that constitute a dynamic and comprehensive technological system. Recently, other attempts have been made to analyze industries according

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to the nature of their knowledge base. Balconi49, in particular, presents an interesting classification of such bases, considering the degrees of depth and codifiability of technologies. The depth of technological knowledge, as defined by Balconi, involves the complexity of technologies. The notion of industries as knowledge bases, as well as the classification system proposed by Balconi, are close to the discussion presented in this article. New tools can be developed for analyzing industries for strategic planning purposes, using the characteristics of industry knowledge bases, the characteristics of technological systems, and the nature of their interrelationships, including parallax. On the basis of more research and analysis in these or similar terms it may become possible to develop new concepts useful in technology planning, especially at the organizational or national level. Notes 1. M. Balconi, “The Notion of Industry and Knowledge Bases: The Evidence of Steel and Mini-mills,” Industrial and Corporate Change, Vol. 2 (1993) pp. 471-507. 2. U. Zander, “Exploiting a Technological Edge: Voluntay and Involuntary Dissemination of Tecbnology,” Doctoral Dissertation (Stockholm: Stockholm School of Economics, Institute of lnternational Business, 1991). 3. J. Niosi, P. Saviotti, B. Bellon and M. Crow, “National Systems of Innovation: In Search of a Workable Concept,” Technology in Society, Vol. 15 (1993) pp. 207-227. 4. M. Polanyi, The Tacit Dtmension (New York: Doubleday Anchor, 1967). 5. E. von Hippel, “The Impact of ‘Sticky Data’ on Innovation and Problem Solving,” Sloan School of Management Working Paper # 3147-9@BPS (Cambridge, MA, 1990). 6. E. Autio, “Spin-off Companies as Agents of Technology Transfer,” Technical Research Center of Finland V’IT, Research Publications 151. (Espoo, Finland, 1993) p. 18. 7. B. Carlsson and R. Stankiewicz, “On the Nature, Function, and Composition of Technological Systems,” Joumal of Evolutiona y Economics, Vol. 1 (1991) pp. 93-l 18. 8. 0. Granstrand, E. Bohlin, C. Oskarsson and N. Sjoberg, “External Technology Acquisition in Large Multi-technology Corporations,” R&D Management, Vol. 22 (1992) pp. 111-133. 9. F. Kodama, “Technology Fusion and the New R&D,” Harvard Business Review (July-August 1992) pp. 70-78. 10. E. Dahmtn, “Development Blocks in Industrial Economics,” in B. Carlsson (ed.) fndustriul l)ynamits (Boston: Kluwer Academic Publishers, 1989). 11. R. Normann and R. Ramirez, “From Value Chain to Value Constellation: Designing Interactive Strategy,” Harvard Business Review (July-August 1993). pp. 65-77. 12. B.-A. Lundvall, Product Innovation and User-Producer Interaction (Aalborg University. 1985). 13. B.-A. Lundvall, “Innovation as an Interactive Process: From User-Producer Interaction to National Systems of Innovation,” in Dosi et al., Technical Change and Economic Tbeoy (London: Pinter Publishers, 1988). 14. C. Freeman, Technology Policy and Economic Performance. Lessons from Japan (London: Pinter Publishers, 1987). 15. R. Nelson, “Institutions Supporting Technical Change in the United States,” in Dosi et af., Technical Change and Economic Theoy (London: Pinter Publishers, 1988). pp. 312-329. 16. R. Nelson (ed.), National Innovation Systems (New York: Oxford University Press, 1993). 17. M. E. Porter (ed.), The Competitive Advantage of Nations (New York: MacMillan, 1990). 18. J. Niosi, P. Saviotti, B. Bellon and M. Crow, “National Systems of Innovation: In Search of a Workable Concept,” Technology in Sociely, Vol. 15 (1993) pp. 207-227. 19. B. Carlsson and R. Stankiewicz, “On the Nature, Function and Composition of Technological Systems,” Journal of Evofuttonay Economics, Vol. 1 (1991) pp. 93-l 18. 20. E. Autio and T. Laamanen, “Measurement and Evaluation of Technology Transfer: Review of Technology Transfer Mmechanisms and Indicators,” Internattonai Journal of Technology Management, Vol. 10, nos. 7/8 (1995) pp. 643-664. 21. C. Freeman and C. Perez, “Structural Crises of Adjustment, Business Cycles and Investment

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22. 23. 24. 25.

26. 27. 28. 29.

30. 31. 32. 33. 34.

35. 36. 37.

38. 39. 40. 41. 42.

43. 44. 45. 46. 47. 48. 49.

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Behavior,” in G. Dosi et al. (eds), Technical Change and Economic Tbeoy (London: Pinter Pub lishers, 1988), pp. 44-61. R. Lovio, “Evolution of Firm Communities in New Industries,” Acta Academiae Oeconomicae Helsingiensis, Series A, Vol. 92 (1993). R. Camagni (ed.), Innovation Networks: Spatial Perspectives (London: Belhaven Press, 1991). H. A. Simon, “On the Behavioral and Rational Foundations of Economic Dynamics,” in R. D. Day and G. Eliasson (eds), The Dynamics ofMarket Economies (Amsterdam: Elsevier, 1986) pp. 2 l-4 1. J. E. Stiglitz, “Learning to Learn, Localized Learning and Technological Process,” in P. Dasgupta and P. Starene (eds), Economic Policy and Technological Pe@ormance (Cambridge: Cambridge University Press, 1987), pp. 125-153. D. Sahal, “Metaprogress Functions,” OMEGA - The Journal of Management Science, Vol. 12 (1984) pp. 77-92. D. Shapere, “The Character of Scientific Change,” in T. Nickles (ed.), Scient+c Discovey, Logic and Rationality (Dordrecht: Reidel, 1980) pp. 61-l 16. H. A. Simon, “On the Behavioral and Rational Foundations of Economic Dynamics,” in R. D. Day and G. Eliasson, The L?ynamlcs of Market Economies (Amsterdam: Elsevier, 1986) pp. 21-41. G. Dosi, “Technological Paradigms and Technological Trajectories: A Suggested Interpretation of the Determinants and Directions of Technical Change,” Research Policy, Vol. 11 (1982) pp. 147-162. ‘I’.S. Kuhn, The Structure of Scienttfic Revolutions, 2nd edn (1st edn, 1962) (Chicago: The University of Chicago Press, 1970) (1st ed 1962). G. H. von Wright, Norm andAction -A Logicalhquiy (London: Routledge & Kegan Paul, 1963). N. Slack, “Flexibility as Managers See It,” in Warner et al., New Technology and Manufacturing Management (London, 1990) pp. 33-48. T. Matsuo, “Japanese R&D Policy for Techno-industrial Innovation, ” in U. Hilpert (ed.), State Policies and Techno-industrial Innovation (1991) pp. 235-259. U. Hilpert, “Economic Adjustment by Technoindustrial Innovation and the Role of the State Solar Technology and Biotechnology in France and West Germany,” in U. Hilpert (ed.), State Policies and Tecbno-industrial Innovation (1991), pp. 85-108. P. E. Drucker, “The Emerging Theory of Manufacturing,” Harvard Business Review (May-June 1990) pp. 94-102. Science and Technology Policy Council of Finland. Towards an Innovative Society:A Development Strategy for Finland (Helsinki, 1993). R. Boyet, “New Directions in Management Practices and Work Organization,” OECD Conference on Technological Change as a Social Process - Society, Enterprises and the Individual (Helsinki, December 11-13, 1989). C. Freeman, ‘Japan, a New National System of Innovation?, n in G. Dosi et al., Technical Change and Economic Tbeoy (London: Pinter Publishers, 1988) pp. 330-348. OECD, Technology in a Changing World (Paris: TEP - The Technology & Economy Program, 1991). J. M. Marchello, “Education for a Technological Age,” Futures (October 1987) pp. 555-565. L. S. Hiraoka, “Japan’s Increasing Investments Abroad,” Futures (October 1985), pp. 495-508. J, Hagedoorn and J, Schakenraad, “Inter-firm Partnership and Co-operative Strategies in Core Technologies,” in C. Freeman and L. Soete (eds), New Explorations in the Economics of Technical Change (19901, pp. 3-37. J. Hagedoorn, “Strategic Technology Partnering During the 1980’s: Trends, Networks, and Corporate Patterns in Non-core Technologies,” Research Policy, Vol. 24 (1995) pp. 207-231. R. Mitcham and R. Mackey (eds), PbilosopbJj and Technology: Readings in the Philosophical Problems of Technology (New York: The Free Press, 1983). W. E. Bijker, T. P. Hughes and T. J, Pinch, The Social Construction of Technological Systems New Directions in the Sociology and Histoy of Technology (London: The MIT Press, 1987). M. E. Porter, Competition In Global Industries (Harvard, MA: Harvard Business School Press, 1986). M. E. Porter, The Competitive Advantage of Nations (New York: MacMillan, 1990). R. E. Caves, Multinational Enterprise and Economic Analysis (Cambridge: Cambridge University Press, 1988). M. Balconi, “The Notion of Industry and Knowledge Bases: The Evidence of Steel and Mini-mills,” Industrial and Corporate Change, Vol. 2 (1993), pp. 471-507.