A transformation in the functions of university, industry, and government, the “triple helix,” is taking place as each institution can increasingly assume the role of the other. The Triple Helix thesis states that the knowledge infrastructure can be explained in terms of these changing relationships. Arrangements and networks among the three institutional spheres provide input and sustainance to science-based innovation processes. In this new configuration, academia can play a role as a source of firm-formation, technological, and regional development, in addition to its traditional role as a provider of trained persons and basic knowledge.
The expansion of the role of knowledge in society and of the university in the economy can be analyzed in terms of a Triple Helix of university-industry-government relations. An expanding network system of interactive spirals is generated as university, industry, and government engage to promote economic development and academic research. The entrepreneurial university that can be created from this constellation encompasses and transcends previous academic missions of education and research. The mission of economic development is increasingly added to that of the reproduction of the knowledge base and the systematic production of scientific novelty (Etzkowitz, 2001).
Although only a small fraction of university innovations, relative to R&D budgets, is actually utilized by industry, a transmission belt of firm-formation has been created, often with government assistance, through incubator facilities and entrepreneurship centers (e.g., Klofsten et al., 1999). The Triple Helix of university, industry, and government is exemplified in new organizational mechanisms that promote innovation and new business formation (Lissenburgh and Harding, 2000). This structure differs dramatically, in its functions and roles, from the innovation model that existed prior to the emergence of knowledge-based economic and social development.
For example, although a university may establish an incubator on the basis of its endogenous capacities, incubation is most productively organized as a cooperative venture between one or more universities, a local government authority, and a consortium of financial institutions interested in enhancing the local innovation environment. The growing role of the university in the new economy goes well beyond providing industry and the state apparatuses with trained personnel and engaging in research that provides a knowledge base for industry to draw upon (Mansfield, 1991). On the scale that has emerged during the past two or three decades, academic knowledge production is increasingly a structural factor in science-based innovation processes.
The organizational mechanisms are sometimes extensions of technology licensing offices that act as intermediaries between the universities and existing firms. These new arrangements may be tied directly to the research and teaching activities of the university and extend these in the direction of industrial innovation. The transmission and transfer is highly selective, but it operates effectively. In Beijing, for example, the number of employees of the science parks is nowadays larger than that of the aggregate of the universities (Leydesdorff & Guoping, 2001). The university thus becomes an agency of economic and social development, building upon its previous missions of teaching and research.
A transformation in the functions of university, industry, and government is taking place as each institution can assume the role of the other. Under certain circumstances, the university can take the role of industry, helping to form new firms in incubator facilities. Government can take the role of industry, helping to support these new developments through funding programs and changes in the regulatory environment. Industry can take the role of the university in developing training and research, often at the same high level as universities.
The network relationships within the Triple Helix are changing the participating institutions into relatively autonomous yet interdependent spheres. The initial conditions are different in various countries. In the United States, university, industry, and government are becoming less isolated from each other. In many Latin American countries industries and universities, formerly under strict governmental control, are gaining relative autonomy from the state. In Europe the unification process paradoxically leads to enhancement of the regional and transnational levels of governance simultaneously, with different effects in the various member states (Leydesdorff, 2000).
The first, second, and third worlds that formerly had distinctly different institutional arrangements are now moving in a common direction that seeks a balance between competition and cooperation (Gibbons et al., 1994). A redefinition of the public/private divide is unavoidable in a knowledge-based economy because academic knowledge is a public good, whereas entrepreneurship requires conditions for private appropriation. In contrast to neo-liberal expectations, the direction is thus not toward laissez-faire. There is an important but not dominant role for government and an enhanced role for the university in the Triple Helix. What drives this change in the role of these institutional spheres and their networks of relations is the need to sustain a high level of innovation.
The globalization of the configuration of university-industry-government relations can be considered as a result of various developments that have coincided:
Over time, these developments have led to shifts in the political-economic relationships among university, industry, and government, moving them closer in some societies and distancing them in others. For example, in the U.S. university, industry, and government are supposed to work more or less independently of each other. Nevertheless, government plays an increasingly more important role not only in providing a regulatory environment, but also in encouraging innovation. Academia, traditionally supposed to be apart from industry, is increasingly involved with industry, not only through consulting and contract research but in forming companies from academic research (Etzkowitz et al., 2001).
Other societies, such as the Former Soviet Union, started from a model of the state controlling and incorporating industry and academia. Some European and many Latin American countries have also maintained aspects of this model. The state was expected to coordinate industry and academia toward a common development goal. For example, in Argentina, Sábato’s triangle concept provided government with a rationale to coordinate university and industry in order to develop new technologies and industries (Sábato and Botana, 1968; Sábato, 1997).
Whether countries started from the model of the state incorporating industry and academia or a configuration in which they coexisted relatively separately, the different helices have recently been moving in a common direction to stimulate both competition and collaboration. There is movement toward a model where the three institutional spheres will overlap, with each sometimes taking the role of the others.
The necessity to restructure institutional relations is caused by the knowledge-intensity of the economic development. Institutional relations are restructured with reference to their innovative capacities. In the U.S., laws assisting start-up and the commericialization of academic research, such as the Bayh-Dole Act (1980), have been enacted even in the face of opposition from large firms. Organized knowledge production and control systems (e.g., technologies, disciplines) provide a medium of social coordination that adds to economic exchange and political decision-making with potentially synergetic interaction effects.
While the sciences traditionally produce sediment in the form of scientific literature, innovation can be defined only as an operation at an interface. For example, new ideas from the laboratories have to be brought together with market perspectives before innovation can take place. Firms based on academic research usually take off only after a researcher joins with a business partner. Traditionally, these different perspectives were brought together in the “technostructure” of knowledge-based corporations (Galbraith, 1967).
Innovation is different from invention in terms of the requirement that ideas be put to use through reflection in an interactive practice. Knowledge production can be considered as a necessary, but not a sufficient step to innovation. It creates a potential which can be actualized by bringing together users, producers, entrepreneurs, and policy-makers in a “transaction space” where problems and possibilities can be argued and traded-off (Nowotny et al., 2001).
The construction of a transaction space does not have to lead to consensus. On the contrary: one expects differences of perspective, leading to creative interactions in which the participants can transcend the idées reçues of their respective organizations. When individuals take the network perspective, which can be broader than the sum of the participating groups, a new interaction dynamic may also be generated. When new ideas for projects and programs are exchanged among people of different backgrounds and interests, they may be challenged to act freely and creatively. Such mutual adjustments of expectations then begin to change the “selection environments” (Nelson & Winter, 1982) of the entrepreneurs and institutional agencies involved by making the options more knowledge-intensive.
For example, in Rio de Janeiro several years ago an informal discussion group of regional business, political, and academic leaders, concerned with knowledge-based innovation, developed a concept for a new branch campus of the State University at Friburgo. Their plan went far beyond the initial intentions of the State University to develop an undergraduate campus in an outlying town. Instead, a Ph.D. program relating computer science to industrial processes was made the initial basis of the campus, along with an incubator facility housing consulting firms to relate this insertion of high-level academic research into local mid-tech industry. Without the discussions in a transaction space representing the different spheres, the likely result would have been a traditional academic program to supply engineers for existing industry rather than an innovative project to renew old and create new industrial development.
The network system of reference opens a window on a universe of discourse that generates a set of coordinates transcending the points of reference of discourses that previously took place within separate institutional spheres. These trans-institutional discourses soon generate a vocabulary of their own. For example, the European Union has developed a lingo of “RTD projects” with “objective 1” and “workpackage 2” within “Framework Five.” The discourses generate visions and metaphors that can be utilized to shape new economic, political, and social initiatives.
Ireland is a prime example of the creative use of European programs to renew a country’s political economy and social structure, even attracting emigrants to return home. Dublin’s Trinity College, for example, has become a force for indigenous innovation by developing a network of science parks and incubators tied to academic research. These initiatives complement an earlier strategy based upon attracting foreign direct investment, building upon an infrastructure enhanced with EU funds (Jones-Evans and Klofsten, 1998).
The new concepts have consequences because funding is at stake, programs are created, “structural funds” are delivered to “less favoured regions,” etc. The scientists, policy makers, and industrialists may have to manoeuvre carefully in order to respect the “subsidiarity” between the different levels: in European parlance and policy, “subsidiarity” means that the supernational entity is only mandated to do what the national level has not been able to accomplish. Thus, the emphasis on regional policy has emerged in a Europe where regions hitherto sometimes lacked political definition. The issue of “regional development” can then legitimate a new discourse.
For example, some of the energies caught up in political and cultural conflicts between regional identities in Spain have been redirected into economic and social advances, enhancing Basque and Catalan autonomy without necessarily challenging national boundaries (Mosa & Olazaran, forthcoming; Riba & Leydesdorff, 2001). Nevertheless, a “Europe of Regions” in a multi-national context inevitably reduces the role of the nation-state. Government itself becomes a variable, as it can be (re-)organized at different levels.
Previously, the various agencies worked in hierarchical systems with predefined roles or on markets which forced roles upon them. Now they are expected to assume multiple roles and functions, not only within their own institutions, but within these new networked and hybrid organizations. Such a double-layered system of variation and selection in terms of both institutions and functions can drive itself through very specific—”path-dependent” (Arthur et al., 1987; David, 1992)—transitions into a more complex dynamics insofar as the agencies involved are able to use the knowledge base to change their roles, interactions, and positions.
The network arrangements can be considered as an overlay that acts on a variety of institutions and organizations which may crosscut institutional and national boundaries. The agencies are both participants that sustain these networks and observers that position themselves in the relevant selection environments. In this constellation, a new profession of network coordinators and organizers has arisen to make the complex system work progressively. Their task is to translate between different domains and languages, and to get people who are used to work only in one domain to perform tasks in several. These innovation organizers with inter-organizational and interpersonal skills rise to higher levels in universities and companies, and increasingly form their own interface organizations and become knowledge brokers.
Gibbons et al. (1994) have suggested that the university is declining because academic knowledge production is being displaced, if not replaced by the knowledge brokers and consulting firms that “transgress” disciplinary and institutional boundaries (Nowotny et al., 2001). Such a firm, however, brings together a temporary group which solves a problem, but then they disappear and start over again. After a while the consulting firm, like the Institutes in the former Soviet Union, have the same people working on similar problems and they tend to apply the same solutions because the group is relatively static in its knowledge base and continuously under time pressure. The focus is on practical solutions and then on to the next job.
The comparative advantage of the university is that the knowledge-base is continuously developed because there is a flow through of students on the higher-education side. As one technology transfer officer said in an interview, “Each year I have 3,000 potential new inventors.” That number was the annual intake of the university (Etzkowitz, 1986). Even though the professors may want to keep their graduate students on as cheap labor, sooner or later, they will graduate and others will enter. In fact, much of the training model at the graduate level involves senior graduate students passing on their knowledge of how to run the research equipment to the junior graduate students and then finding new problems to work on. If there is ever a break or gap in funding, the professor’s research program can be destroyed. He or she may no longer know how to work the equipment, such is the extent of dependence on a flux of graduate students.
In between academic research groups and Institutes is the expanding realm of centers. These academic research units represent a compromise, incorporating elements of each of the organizational models (Lissenburgh & Harding, 2000). For example, to insure continuity of research focus and the ability to carry out a project expeditiously, the university has created new categories of researchers that are neither professors, nor students, nor technicians. By hiring permanent senior staff without faculty status, the universities are moving in some places toward the Institute model. They are forming research centers around permanent employees who are not tenure track faculty in order not only to insure continuity in research programs but to also arrange collaboration among research groups and the ability to undertake larger scale projects. Thus, there are also mixed organizational models, drawing upon academic, industrial, and governmental research formats, such as the University at Albany incubator facility combining academic research groups, government laboratories, and firms for the purpose of developing shared instrumentation resources.
Using the Triple Helix Model, the roles of government or the university are no longer fixed, because interaction between the different functions is needed in order to generate and sustain the specific configuration of an innovation system. Innovation is no longer a function of a single institutional sphere such as industry. Innovation in “a system of innovations” can itself thus be made the subject of a process of dissensus and consensus formation.
The dynamics of interactions among discursive perspectives are complex because the participants are able to relate to different systems of reference, also in response to perceiving each other’s positions. The language of cause and effect, of independent and dependent variables, assumed a single and stable universe in which fixed relations were predominant. When the change in these relations becomes the focus of the analysis from a network perspective, the purposes of institutions and organizations that could previously be taken for granted become uncertain and can therefore be redefined.
For example, a university can be expected to produce scientific results, but is it also expected to produce new technologies, and if so, to what extent? A government is expected to provide rules and regulations, but can it also be expected to provide venture capital? Are we able to find an early indicator for these unexpected functions? How large is the contribution of university research to technological development?
Industrial economists have typically argued that if one looks at innovation one always sees entrepreneurship and industry, and that is true. But in the case of a system of innovation, one can also see a knowledge infrastructure derived from universities (Narin et al., 1997). The university assumes this role not only as a supplier of knowledge and human capital, but as another “industrial actor” creating intellectual property and co-shaping new firms. Furthermore, governments enter the scene as entrepreneurs directly and/or indirectly, to variable extents, not only supplying the resources to the other actors or regulating their relations with each other, but as an instigator of organizational innovations and structural adjustments that increasingly form the basis of innovation systems. The partners are both participants and observers; they act in the “double hermeneutics” that Giddens (1976) originally specified as typical of the social scientist (Leydesdorff, 2001).
Heretofore, innovation was viewed primarily as the application of technology: a tilt-train, an airbus, or a solar power station could then be indicated as a product. Increasingly, innovation at the organizational level is the social precondition for creating technological innovations, especially at the interfaces of the institutional spheres, that is, in the Triple Helix of university, industry, and government relations. (Re.)organization at the social level is always a reflexive activity that requires insights from the social sciences and the development of communicative competencies. The idea of single (ideal) solution has to be given up.
Since it is not obvious how much government has to intervene or how much a university has to reach out, these questions can now be made the subject of systematic reflection and theoretical debate. Comparative research, for example, may inform policy makers about how other (potentially competing) units have solved the “production growth puzzle” of integrating the various ingredients into a specific mix given local conditions (Nelson & Winter, 1975; Lundvall, 1988). Can lessons be learned from “best practices,” or are conditions fundamentally different across locations? If so, can these conditions also be used elsewhere, and at what cost to the original function?
Trade-offs have continuously to be elaborated, for example, on the basis of research reports informing the decision-making. Some lessons can be learned from best practices, but the information has to be provided with meaning by theoretical specification. Established practices require “rules of thumb”, that is, codified regulations that offer not fixed rules, but informative guidance from which one can deviate when arguments are provided. Evaluation and synthesis of diverse practices from a variety of institutional sources are needed to create new innovation models, that is, with sufficient specification.
Innovation requires agencies competent and able to assess the possibilities contained in a given situation and aware of the fact that the assessment can always be made more knowledge-intensive. Systematic research can add potentially counter-intuitive information to the routines which have guided behavior hitherto. Only on the basis of such reports and discussions will organizations be able to learn. Without discursive knowledge input, the prevailing metaphors can be expected to degenerate into metonomies, a closed world picture that no longer allows one to envision new possibilities if one cannot accept innovative representations.
Kaghan & Barnett (1997), for example, have described how laboratory management of digital research units may block innovation when it is based on traditional concepts of laboratory research. The notion of an incubator, for example, previously associated with a specific locale, can now be extended to the idea of “a virtual incubator” which can be entertained at a global level (Nowak & Grantham, 2000). These concepts can be redefined swiftly, and without loss of quality, because they are codified in a theoretical reflection.
For example, when an academic institution founds a science park or incubator facility, the university provides infrastructure, real estate, technical capacities, etc. to attract the research units of large firms and encourage faculty members and alumni to start-up small enterprises on the basis of advanced ideas from their research. A new university-industry dynamic may be set in motion. Once this system gains momentum, it may have an impact on its environment, and its functions both within the university and in its relations to the relevant environments can be expected to change. The city government hosting this university may perceive the foundation of the science park as a change in the position of the traditional university and assess this change from the perspective of its responsibility for regional development. For example, the expectations of traffic densitities may have to be adjusted and new facilities may be needed.
Problem areas can then be put on the agenda which contain a mixture of industrial, governmental, and academic interests different from those before. In this new configuration, industry is no longer considered as a separate institutional sphere from the university to which the knowledge has to be “transferred”. Industry itself is now increasingly present within academia, potentially co-constitutive of the knowledge production process. Note that in an asymmetrical way, the university through these institutional innovations is also co-constitutive of its industrial environment.
What does it mean for an economy to become knowledge-based? In our opinion, it means that knowledge and information increasingly provide a structural mechanism, that is, a medium of social coordination in addition to economic exchange relations and political and/or managerial control. Knowledge is no longer a supplemental input into an otherwise industrial and market-oriented economy; the codification of information into knowledge provides a basis for production systems that change over time with the further development of their knowledge infrastructure (Cowan & Foray, 1997).
Industries no longer produce only physical artifacts and systems, but also knowledge artifacts such as software, new business models, and intellectual capital. The intangibles (e.g., patents, licenses, engineering research centers, etc.) have become part of the wealth creation process. As early as the mid-19 th century, Karl Marx, abstracting from early examples of science-based industries such as dye-stuffs developed from academic research in chemistry, considered the driving force of knowledge in future economic development (Marx, 1858). He noted its importance as a future possibility, but rejected it as the historical force of change in the transition from capitalism that he foresaw through the organization of workers and the class struggle. Schumpeter (1943) analyzed the dynamics of capitalism as more complex, that is, involving technological developments. Technological innovation tends to upset the market relations among existing products and firms. The accumulation of innovations can reshape industrial relations periodically. New products and firms induce a mechanism of “creative destruction.”
Machlup (1952) noted the increasing size of occupational sectors based on knowledge-related activities. This effect resounded in traditional economics when economic growth could no longer be explained completely in terms of traditional terms like land, labor, and capital, since an increasingly important part was due to technological development. This became known as “the residue” (Abramowitz, 1956; OECD, 1964). Rosenberg (1976) opened the “black box” of technological development by analyzing historical instances of the role that science and technology have played in particular economic sectors, such as textiles, auto manufacturing, and aviation.
Bell (1973) showed that the emerging trend toward knowledge-based economics gained such force that service occupations were overtaking those in manufacturing, if not in absolute numbers at that time then at least in terms of growth rates. By the late 1980s his prediction had come true (Barras, 1990). Knorr-Cetina (1999) argues that not only has the proportion of knowlege-workers increased, but that the format of the knowledge workplace, that is, the laboratory, has become the dominant form of organized production and control.
The addition of knowledge and information as a coordination mechanism of society upsets and reshapes the political economy. Like the development of the world market, this transition takes place at the level of the global regime. Whereas the previous regime was largely shaped as a liberal constitution in the 19 th century, the public/private divide can no longer be kept clear when the system is under continuous reconstruction. The knowledge-based construction of new bridging functions is needed since the political system has to provide the incentives to structure the local innovation systems in order to retain the wealth potentially generated from expected innovations (Freeman & Perez, 1988). As these reflexive constructions among expectations begin to function, new waves of development can be expected.
An historical example of a “nexus” of reflexive legislation and regulation has been provided by the analysis of the patent system which emerged in its present form during the 19 th century, actually in relation to the knowledge-based development of the dye-stuff industry (Van den Belt & Rip, 1987). Patents make protected knowledge available as a public resource for others to create new knowledge which itself can be privatized and protected even as it is also publicized (Etzkowitz, In Press).
In addition to transaction costs, there can also be transaction benefits by having an intermediary system of translations. For example, a university-industry transfer office generates royalties from licenses of knowledge that might otherwise be given away freely in publications although without the added value of inventor involvement in further development. Additionally, the transfer office provides the university staff with a search mechanism to identify potential users of knowledge who may not already be in the informal network of university-industry relations or within the circle of scientific journal readers.
The transaction benefits may outweigh the costs or not, from the perspective of the users and/or the producers of knowledge. The costs and benefits can vary among parties. How does one solve the puzzle? Obviously, there is no single and global solution. Under what conditions does formalization of the relations stimulate innovation, for example, by opening new opportunities for informal relationships or by creating barriers to informal exchange among partners? Thus, precisely with reference to the question of how to organize intervention, a realm of empirical research questions can be specified.
The Triple Helix system under study is driven by the increased relevance of technological knowledge and academic knowledge to industrial production and social development. The study of the Triple Helix, however, is driven by policy questions which bring the relation between university and government to the fore (Wouters et al., 1999; Guston, 2000).
For example, Hungary developed an elaborate innovation system during the communist period, including a well-established academic system and industrial production at a sophisticated level, but lacking effective linkages between them. The change in conditions made the weaknesses in this integration very obvious during the latter half of the 1980s. Reflection on these conditions can help to inform the search for other solutions. However, the possible solutions are attractive for competing nations in similar situations as well (Radosevic, 1999).
The Triple Helix model provides a theoretical frame of reference—an heuristics—for the assessment of knowledge-based development options. These options do not emerge in vacuo; the analysis of their knowledge-base can be pursued in terms of university-industry-government configurations (Etzkowitz & Leydesdorff, 1997 and 2000).
This focus originated from research on university-industry relations in the U.S., realizing that the topic could not be fully understood elsewhere (e.g., in Mexico) without taking into account the role of government. At the time, models from non-linear dynamics and chaos theory became increasingly available in relation to evolutionary economics and the social study of technology. These models allow for the systematic study of interactions among more than two subdynamics, for example, by using simulations (Leydesdorff & Van den Besselaar, 1994). A Triple Helix system can be expected to exhibit all kinds of chaotic behaviour, such as unintended consequences, crises, niche formation, and self-organization.
The observable configurations inform us about the selections that may have taken place, but the provisional inferences can be expected to raise further research questions. For example, during the transition from socialism, some reformers in Eastern Europe were trying to remove government from having any role, not only in science and technology policy, but also in other areas of society. Even though foreign direct investment was encouraged, this seldom involved utilizing local R&D resources. Innovation systems were largely coming to a halt. More recently, political leaders are moving away from that position and bringing government back into the picture to take advantage of the R&D resources left behind from the previous era.
In sociological terms, the Triple Helix model can be considered as a “multi-structural/ multi-functional” framework in contrast to the structural-functionalist model in which a single function was expected to be carried by a single institution (Parsons, 1951). Merton (1957) added to this model that functions are historically contingent and can be carried by different institutions. How institutions and functions operate in relation to each other could then become the focus of a research program, for example, in the sociology of science (e.g., Zuckerman & Merton, 1971; Parsons & Platt, 1975). In reaction to developments in the philosophy of science (Kuhn, 1962), the sociology of scientific knowledge emphasized that functions and institutions can be considered both as constructed and as reconstructed in the light of socio-cognitive developments in scientific paradigms, fields, and specialties (Barnes & Dolby, 1970). The focus in the emerging interdiscipline of science, technology, and innovation studies thus shifted from structural to action parameters (Latour, 1987). In our model, we keep the focus on action and change, but we assume that the communicative actions generate codes of communication over time (in order to reduce the uncertainty). These codes of communication can feed back as selective structures on the generation processes, both recursively and interactively. Whether the codes are stabilized—as “evidential contexts” (Pinch, 1985) or “validation boundaries” (Fujigaki, 1998)—remains an empirical question. The stabilization of different selection mechanisms is historically contingent, both within the empirical sciences (Gilbert & Mulkay, 1984) and in relation to the relevant interfaces (e.g., Barnes & Edge, 1982; Knorr-Cetina, 1999). While the agencies at the nodes are active and recursively selective according to their own specific functions and institutional constraints, the network system of university-industry-government relations adds a layer of distributed, uncoordinated, and therefore uncertain interactions. The various representations interact and operate on each other in the transaction spaces between institutions and functions at the network level, but with different (sub)dynamics for the various partners involved. The locus of control can then vary, and the prevailing codifications can be expected to alternate over time. Differentiation among the codes can be maintained and/or can be expected to be blurred insofar as this is deemed functional by the various partners involved, and to different degrees given local contingencies. Thus, the exchange processes become complex and can be provided with different meanings from the various perspectives. As noted, these distributed network systems can also be considered as the “transaction spaces” that sometimes enable the participants to translate among the different meanings. Translations can again be organized and codified into informed and knowledge-based reconstructions and roles (Fujigaki & Leydesdorff, 2000). If this process is successfully achieved, the previously stabilized configurations can even be overwritten and/or become globalized, that is, made one layer more complex, flexible, and resilient.
From this perspective of interacting subdynamics spanning transaction spaces, the institutional layers function mainly as a retention mechanism for economic wealth, archival knowledge, and “best practices,” respectively (Van Lente & Rip, 1998). The subdynamics (wealth creation, knowledge generation, and public versus private control) are continuously developed both in parallel and interactively. The results compete for institutionalization, but institutionalization is itself also one of the competing subdynamics. Thus, one can consider institutionalization in terms of its functionality for improving communication (and then sometimes collaboration) among the partners with reference to innovation. In other stages, one can expect de-institutionalization and “creative destruction” (Schumpeter, 1943).
The resulting overlay of communications among the partners cannot be completed, since it remains disturbed by institutional interests, by market forces, and by unexpected innovations. All participants develop a partial perspective, and they are reflexively aware of doing so. The model is different from its reification into a neo-corporatist arrangement because of the implied emphasis on the dynamics of change and its appreciation of differences in opinion, position, and interests. One may even wish to use the Triple Helix model as a critique of situations in which the various dynamics are “locked-in” into coevolutions of insufficient complexity.
This Triple Helix model of competing and partially resonating (and dissonating) subdynamics contrasts with C. Wright Mills’ (1957) societal model in which the military formed the third element of an institutional triad with large industry and the executive branch of the U.S. federal government. Mills argued that an interlocking “power elite,” sharing a common educational and social background, ran the major institutions in the U.S. He further held that this coalition of institutional leaders transcended mere electoral politics and guided decision-making on important policy issues. The end of the Cold War vitiated Mills’ analysis, especially since the major instance of decision-making that he wished to analyze was the nation’s ability to initiate World War III (Mills, 1958).
As geo-political and military issues were gradually displaced during the 1970s and ’80s by issues of economic competitiveness, decision-making became more diffuse, involved other actors, and often devolved from the national to the regional level. While science and technology policy issues had been understood as a spin-off from Cold-War military and political exigencies—and therefore as taking place according to a linear model—henceforth a framework was required that would account for the increasing importance of the science base and the decreasing relevance of the military.
The “Triple Helix” thesis is that university-industry-government network relations are the key to knowledge-based economic development in a broad range of post laissez-faire capitalist and post-socialist societies. This Triple Helix comprises universities and other knowledge-producing institutions; industry, including high-tech start-ups as well as technostructures, megacorps, and multinationals; and government at various levels (local, regional, national, and transnational). These institutional units have to engage in exchange relations in order to participate in the innovation system by innovatively transforming themselves in accordance with changes in the codification structures.
For example, etatist models like the “import substitution” regime in Latin America during the 1980s tried to impose a technocratic vision on the dynamics of the civil society (Sábato, 1997). In the Triple Helix model, however, the three (sub.)dynamics are considered as degrees of freedom of the complex system. Only as degrees of freedom can they be made to contribute to the further development of society. As noted, the three functional subdynamics are: wealth creation, knowledge production, and the political expression and (incomplete) coordination of different interests.
The interaction terms among the subdynamics provide the potential for the progressive and creative deconstruction of existing relations into ones which suit us better (that is, as participant-observers) given ongoing changes in the relevant environments. The analytical declaration of an overlay system as a relevant level of interaction provides us (as observing analysts) with the tools for understanding innovation as the crucial operation of a knowledge-based economy. The missing links can then be specified theoretically, that is, as hypotheses to be tested. What can be innovated, in terms of what, and what are the consequences for other parts of the system?
The uncertain operation of innovation and the relatively unpredictable dynamics of knowledge-based innovation systems can themselves be considered as the drivers of these systems. The reflexive mode of R&D is volatile, but one is committed to investigating whether the envisaged options can also be realized. In order to be made fruitful, variation has to be codified both recursively, that is, in relation to a previous stage, and interactively, that is, in terms of the competition among alternatives. It is not sufficient to provide the means of an innovation, but one also has to convince agencies to take it further from where one can bring it from one’s own specific angle. As far as a next stage can be reached (for example, in terms of a new generation of a technology), new horizons can be expected to open up for further knowledge-based developments.
For example, a potential development resource in the developing world is that there are universities that contain “islands of innovation,” groups within them that are working at the highest international levels. Traditionally, many of them have been tied to the research of their sponsors in developed countries. However, more recently there has been a shift in direction and some of these groups are focusing on issues in their home countries as well. An indigenous innovation strategy is increasingly possible because in some of these fields, such as software, technical capabilities are readily available. Large-scale expensive equipment is not a pre-requisite or barrier to entry. What is important is knowledge and the utilization of this knowledge.
One problem that may increasingly trouble the developed nations is that human resources can be considered as a comparative advantage of Eastern Europe and the Third World (Müller & Etzkowitz, 2000). It is now possible to raise these human resources to the highest level in these countries and be internationally competitive in niche areas of science, technology, and innovation, and even beyond. India’s position in the market of software development is the best known example, but Brazil’s export of software has also grown recently to over $500 million.
Under the emerging Triple Helix regime of knowledge-based economic developments one can expect an endless transition of innovation, rather than a journey to an assumed ideal model of socialism or capitalism. What all the world now has in common, whatever one’s particular starting-point—whether from a developing country or an advanced country—is the fact that at this point in time, all countries are continuously and competitively developing. In the case of knowledge-based developments one can no longer assume fixed endpoints to development.
Although the initial conditions of different nations are very unequal, one may expect successful niche creations and catastrophic crises. Knowledge-based development can be self-reinforcing (Arrow, 1962; Arthur, 1994). The organizing principle is different. Paradoxically, in some countries this new situation requires a reduced role for national government in order for other institutional spheres to play a greater role in society while in others, where government has been less active, an enhanced role for government may be required.
As the university takes on a new role as a knowledge-industry, both in its internal development and in stimulating innovation in the larger society, it can engage in translating research into practices, and problems in society into new research agendas. Accordingly, the university as an isolated “ivory tower” tends to collapse (Jacobs & Hellström, 2000). The attempt to position a campus as the Harvard of the Midwest is displaced by the more knowledge-based image of a Silicon Valley as the embodiment of differentiated, knowledge-intensive, and complex institutional arrangements. Stanford and MIT have become the role models for universities attempting to become progenitors of regional knowledge-based economies.
The further development of the relations between academia and governance, that is, government at various levels, transforms the public sphere into a more complex system. The university can act as a vanguard in the public sphere because it has a clearly defined function in the social system on which it can build recursively and knowledge-intensively. When academia fails to explain the complexity of the transformation processes to larger audiences, the public discussion, however, may degenerate.
NGOs, for example, need continuous support and feedback from academic expertise in order to be able to balance prevailing perspectives in political discussions about alternative options. The power balance requires capacities for information processing, storage, and control at the public end. Public access to the academic knowledge base and legislation about (in some instances limitation, and in other cases extension of) intellectual property rights are therefore crucial ingredients of the ongoing transformation processes in the public domain. For example, in Denmark, the traditional professorial ownership of intellectual property rights emanating from research was recently re-assigned by law to the university in order to enhance the likelihood of utilization.
The innovation systems of the 21 st century can be expected to involve both technological and organizational levels, and the possible change of relations between these two levels. Thus, society itself is continuously being reconstructed by the operation of social relations that are increasingly knowledge-intensive. The participation in a knowledge-based economy is limited to the degree that the citizens understand the mechanisms of control of a knowledge-based system.
A new mode of innovation is emerging, transforming and redesigning national and institutional boundaries. Boundary crossing and hybridization among institutional spheres provides an inspiration to innovation, at the levels of organizations, technologies, and knowledge. This system needs both functional differentiation and structural integration. The different subdynamics are interwoven. The Triple Helix of university-industry-and government involves internal transformations in the institutional spheres as well as expanded relations among different levels, such as start-up and established firms, regional and multi-national governance, local colleges and research universities. Understanding the dynamics of these relationships can be considered as the very purpose of innovation studies (Wouters, 1999; Cutcliffe, 2000).
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