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The Dynamics of Innovation: From National Systems and “Mode 2” to a Triple Helix of University–Industry–Government Relations



The Triple Helix of university–industry–government relations is compared with alternative models for explaining the current research system in its social contexts. Communications and negotiations between institutional partners generate an overlay that increasingly reorganizes the underlying arrangements. The institutional layer can be considered as the retention mechanism of a developing system. For example, the national organization of the system of innovation has historically been important in determining competition. Reorganizations across industrial sectors and nation states, however, are induced by new technologies (biotechnology, ICT). The consequent transformations can be analyzed in terms of (neo-)evolutionary mechanisms. University research may function increasingly as a locus in the “laboratory” of such knowledge-intensive network transitions.
Etzkowitz, H.,* & Leydesdorff, L.** (2000). The Dynamics of Innovation: From
National Systems and 'Mode 2' to a Triple Helix of University-Industry-Government
Relations. Research Policy, 29(2), 109-123.
(rotating first authorship)
* Science Policy Institute, Social Science Division, State University of New York at Purchase, 735
Anderson Hill Road, Purchase, NY 10577-1400, USA. E-mail:
** Department of Science and Technology Dynamics, Nieuwe Achtergracht 166, 1018 WV
Amsterdam, The Netherlands. E-mail:
The Triple Helix of University-Industry-Government Relations is compared with alternative models
for explaining the current research system in its social contexts. Communications and negotiations
between institutional partners generate an overlay that increasingly reorganizes the underlying
arrangements. The institutional layer can be considered as the retention mechanism of a developing
system. For example, the national organization of the system of innovation has historically been
important in determining competition. Reorganizations across industrial sectors and nation states,
however, are induced by new technologies (biotechnology, ICT). The consequent transformations
can be analyzed in terms of (neo-)evolutionary mechanisms. University research may function
increasingly as a locus in the "laboratory" of such knowledge-intensive network transitions.
1. Introduction: From the Endless Frontier to an Endless Transition
The "Triple Helix" thesis states that the university can play an enhanced role in innovation in
increasingly knowledge-based societies. The underlying model is analytically different from the
National Systems of Innovation (NSI) approach (Lundvall 1988 and 1992; Nelson 1993), which
considers the firm as having the leading role in innovation, and from the "Triangle" model of Sábato
(1975), in which the state is privileged (cf. Sábato and Mackenzie 1982). We focus on the network
overlay of communications and expectations that reshape the institutional arrangements among
universities, industries, and governmental agencies.
As the role of the military has decreased and academia has risen in the institutional structures of
contemporary societies, the network of relationships among academia, industry, and government have
also been transformed, displacing the Cold-War "Power Elite" trilateral mode of Wright Mills (1958)
with an overlay of reflexive communcations that increasingly reshape the infrastructure (Etzkowitz
and Leydesdorff 1997). Not surprisingly, the effects of these transformations are the subject of an
international debate over the appropriate role of the university in technology and knowledge transfer.
For example, the Swedish Research 2000 Report recommended the withdrawal of the universities
from the envisaged "third mission" of direct contributions to industry (see Benner and Sandström,
this issue). Instead, the university should return to research and teaching tasks, as traditionally
conceptualized. However, it can be expected that proponents of the third mission from the new
universities and regional colleges, which have based their research programmes on its premises, will
continue to make their case. Science and technology have become important to regional
developments (e.g., Braczyk et al. 1998). Both R&D and higher education can be analyzed also in
terms of markets (Dasgupta and David, 1994).
The issues in the Swedish debate are echoed in the critique of academic technology transfer in the
U.S.A. by several economists (e.g., Rosenberg and Nelson, 1994). The argument is that academic
technology transfer mechanisms may create unnecessary transaction costs by encapsulating
knowledge in patents that might otherwise flow freely to industry. But would the knowledge be
efficiently transferred to industry without the series of mechanisms for identifying and enhancing the
applicability of research findings? How are development processes to be carried further, through
special grants for this purpose or in new firms formed on campus and in university incubator
The institutional innovations aim to promote closer relations between faculties and firms. "The
Endless Frontier" of basic research funded as an end in itself, with only long-term practical results
expected, is being replaced by an "Endless Transition" model in which basic research is linked to
utilization through a series of intermediate processes (Callon 1998), often stimulated by government.
The linear model either expressed in terms of "market pull" or "technology push" was insufficient to
induce transfer of knowledge and technology. Publication and patenting assume different systems of
reference both from each other and with reference to the transformation of knowledge and technology
into marketable products. The rules and regulations had to be reshaped and an interface strategy
invented in order to integrate "market pull" and "technology push" through new organizational
mechanisms (e.g., OECD 1980; Rothwell & Zegveld 1981).
In the U.S.A., these programs include the Small Business Innovation Research program (SBIR) and
the Small Bussiness Technology Transfer Program (STTR) of the Department of Defense, the
Industry/University Cooperative Research Centers (IUCRC) and Engineering Research Centers
(ERC) of the National Science Foundation, etc. (Etzkowitz et al., 2000). In Sweden, the Knowledge
Competency Foundation, the Technology Bridge Foundation were established as public venture
capital source, utilizing the Wage Earners Fund, originally intended to buy stock in established firms
on behalf of the public. The beginnings of a Swedish movement to involve academia more closely in
this direction has occasioned a debate similar to the one that took place in the U.S. in the early 1980s.
At that time, Harvard University sought to establish a firm jointly with one of its professors, based
on his research results.
Can academia encompass a third mission of economic development in addition to research and
teaching? How can each of these various tasks contribute to the mission of the university? The late
nineteenth century witnessed an academic revolution in which research was introduced into the
university mission and made more or less compatible with teaching, at least at the graduate level.
Many universities in the U.S.A. and worldwide are still undergoing this transformation of purpose.
The increased salience of knowledge and research to economic development has opened up a third
mission: the role of the university in economic development. A "Second Academic Revolution"
seems under way since W.W. II, but more visibly since the end of the Cold War (Etzkowitz,
In the U.S.A. in the 1970s, in various Western European countries during the 1980s, and in Sweden at
present, this transition has led to a reevaluation of the mission and role of the university in society.
Similar controversies have taken place in Latin America, Asia, and elsewhere in Europe. The "Triple
Helix" series of conferences (Amsterdam, 1996; Purchase, New York, 1998; and Rio de Janeiro,
2000) have provided a venue for the discussion of theoretical and empirical issues by academics and
policy analysts (Leydesdorff and Etzkowitz, 1996 and 1998). Different possible resolutions of the
relations among the institutional spheres of university, industry, and government can help to generate
alternative strategies for economic growth and social transformation.
2. Triple Helix Configurations
The evolution of innovation systems, and the current conflict over which path should be taken in
university-industry relations, are reflected in the varying institutional arrangements of
university-industry-government relations. First, one can distinguish a specific historical situation
which one may wish to label "Triple Helix I." In this configuration the nation state encompasses
academia and industry and directs the relations between them (Figure 1). The strong version of this
model could be found in the former Soviet Union and in Eastern European countries under "existing
socialism." Weaker versions were formulated in the policies of many Latin American countries and
to some extent in European countries such as Norway.
A second policy model (Figure 2) consists of separate institutional spheres with strong borders
dividing them and highly circumscribed relations among the spheres, exemplified in Sweden by the
noted Research 2000 Report and in the U.S. in opposition to the various reports of the
Government-University-Industry Research Roundtable (GUIRR) of the National Research Council
(MacLane 1996; cf. GUIRR 1998). Finally, Triple Helix III is generating a knowledge infrastructure
in terms of overlapping institutional spheres, with each taking the role of the other and with hybrid
organizations emerging at the interfaces (Figure 3).
The differences between the latter two versions of the Triple Helix arrangements currently generate
normative interest. Triple Helix I is largely viewed as a failed developmental model. With too little
room for "bottom up" initiatives, innovation was discouraged rather than encouraged. Triple Helix II
entails a laissez-faire policy, nowadays also advocated as shock therapy to reduce the role of the state
in Triple Helix I.
In one form or another, most countries and regions are presently trying to attain some form of Triple
Helix III. The common objective is to realize an innovative environment consisting of university
spin-off firms, tri-lateral initiatives for knowledge-based economic development, and strategic
alliances among firms (large and small, operating in different areas, and with different levels of
technology), government laboratories, and academic research groups. These arrangements are often
encouraged, but not controlled, by government, whether through new "rules of the game," direct or
indirect financial assistance, or through the Bayh-Dole Act in the U.S.A. or new actors such as the
above mentioned foundations to promote innovation in Sweden.
3. The Triple Helix of Innovation
The Triple Helix as an analytical model adds to the description of the variety of institutional
arrangements and policy models an explanation of their dynamics. What are the units of operation
that interact when a system of innovation is formed? How can such a system be specified?
In our opinion, typifications in terms of "national systems of innovation" (Lundvall 1988; Nelson
1993); "research systems in transition" (Cozzens et al., 1990; Ziman 1994), "Mode 2" (Gibbons et al.,
1994) or "the post modern research system" (Rip and Van der Meulen 1996) are indicative of flux,
reorganization, and the enhanced role of knowledge in the economy and society. In order to explain
these observable reorganizations in university-industry-government relations, one needs to transform
the sociological theories of institutional retention, recombinatorial innovation, and reflexive controls.
Each theory can be expected to appreciate a different subdynamic (Leydesdorff 1997).
In contrast to a double helix (or a coevolution between two dynamics), a Triple Helix is not expected
to be stable. The biological metaphor cannot work because of the difference between cultural and
biological evolutions. Biological evolution theory assumes variation as a driver and selection to be
naturally given. Cultural evolution, however, is driven by individuals and groups who make
conscious decisions as well as the appearance of unintended consequences. A Triple Helix in which
each strand may relate to the other two can be expected to develop an emerging overlay of
communications, networks, and organizations among the helices (Figure Four).
The sources of innovation in a Triple Helix configuration are no longer synchronized a priori. They
do not fit together in a pregiven order, but they generate puzzles for participants, analysts, and policy-
makers to solve. This network of relations generates a reflexive subdynamics of intentions,
strategies, and projects that adds surplus value by reorganizing and harmonizing continuously the
underlying infrastructure in order to achieve at least an approximation of the goals. The issue of how
much we are in control or non-control of these dynamics specifies a research program on innovation.
Innovation systems, and the relationships among them, are apparent at the organizational, local,
regional, national, and multi-national levels. The interacting subdynamics, that is, specific operations
like markets and technological innovations, are continuously reconstructed like commerce on the
Internet, yet differently at different levels. The subdynamics and the levels are also reflexively
reconstructed through discussions and negotiation in the Triple Helix. What is considered as
"industry", what as "market" cannot be taken for granted and should not be reified. Each "system" is
defined and can be redefined as the research project is designed.
For example, "national systems of innovation" can be more or less systemic. The extent of
systemness remains an empirical question (Leydesdorff and Oomes 1999). The dynamic "system(s)
of innovation" may consist of increasingly complex collaborations across national borders and among
researchers and users of research from various institutional spheres (Godin and Gingras, this issue).
There may be different dynamics among regions. The systems of reference have to be specified
analytically, that is, as hypotheses. The Triple Helix hypothesis is that systems can be expected to
remain in transition. The observations provide an opportunity to update the analytical expectations.
4. An Endless Transition
The infrastructure of knowledge-intensive economies implies an Endless Transition. Marx's great
vision that "all that is solid, melts into air" (Berman 1982) underestimated the importance of
seemingly volatile communications and interactions in recoding the (complex) network system.
Particularly, when knowledge is increasingly utilized as a resource for the production and distribution
system, reconstruction may come to prevail as a mode of "creative destruction" (Schumpeter 1939
and 1966; Luhmann 1984).
Can the reconstructing forces be specified? One mode of specification is provided by evolutionary
economics in which the three functional mechanisms are: technological innovation provides the
variation, markets are the prevailing selectors, and the institutional structures provide the system with
retention and reflexive control (Nelson 1994). In advanced and pluriform societies, the mechanisms
of institutional control are again differentiated into public and private domains. Thus, a complex
system is developed that is continuously integrated and differentiated, both locally and globally.
Innovation can be defined at different levels and from different perspectives within this complex
dynamics. For example, evolutionary economists have argued that one should consider firms as the
units of analysis, since they carry the innovations and they have to compete in markets (Nelson and
Winter 1982; cf. Andersen 1994). From a policy perspective, one may wish to define "national
systems of innovation" as a relevant frame of reference for government interventions. Others have
argued in favour of networks as more abstract units of analysis: the semi-autonomous dynamics of the
networks may exhibit lock-ins, segmentation, etc. (e.g., David and Foray 1994). Furthermore, the
evolving networks may change in terms of relevant boundaries while developing (Maturana 1978).
In our opinion, these various perspectives open windows of appreciation on the dynamic and complex
processes of innovation, but from specific angles. The complex dynamics is composed of
subdynamics like market forces, political power, institutional control, social movements,
technological trajectories and regimes. The operations can be expected to be nested and interacting.
Integration, for example, within a corporation or within a nation state, cannot be taken for granted.
Technological innovation may also require the reshaping of an organization or a community
(Freeman and Perez 1988). But the system is not deterministic: in some phases intentional actions
may be more succesful in shaping the direction of technological change than in others (Hughes 1983).
The dynamics are non-linear while both the interaction terms and the recursive terms have to be
declared. First, there are ongoing transformations within each of the helices. These reconstructions
can be considered as a level of continuous innovations under pressure of changing environments.
When two helices are increasingly shaping each other mutually, co-evolution may lead to a
stabilization along a trajectory. If more than a single interface is stabilized, the formation of a
globalized regime can be expected. At each level, cycles are generated which guide the phasing of
the developments. The higher-order transformations (longer-term) are induced by the lower-order
ones, but the latter can seriously be disturbed by events at a next-order system's level (Schumpeter
1939; Kampmann et al. 1994).
Although this model is abstract, it enables us to specify the various windows of theoretical
appreciation in terms of their constitutive subdynamics (e.g., Leydesdorff & Van den Besselaar
1997). The different subdynamics can be expected to select upon each other asymmetrically, as in
processes of negotiation, by using their specific codes. For example, the markets and networks select
upon technological feasibilities, whereas the options for technological developments can also be
specified in terms of market forces. Governments can intervene by helping create a new market or
otherwise changing the rules of the game.
When the selections "lock-in" upon each other, next-order systems may become relevant. For
example, airplane development at the level of firms generates trajectories at the level of the industry
in coevolutions between selected technologies and markets (e.g., Nelson 1994, cf. McKelvey 1996).
Nowadays, the development of a new technological trajectory invokes the support of national
governments and even international levels (like the EU), using increasingly a Triple Helix regime
(Frenken and Leydesdorff, forthcoming).
We have organized this theme issue about the Triple Helix of University-Industry-Government
Relations in terms of three such interlocking dynamics: institutional transformations, evolutionary
mechanisms, and the new position of the university. This approach allows us to pursue the analysis
at the network level and then to compare among units of analysis. For example, both industries and
governments are entrained in institutional transformations, while the institutional transformations
themselves change under the pressure of information and communication technologies (ICT) or
government policies. Before explaining the organization of the theme issue in detail, however, we
wish to turn briefly to the analytical position of the Triple Helix model in relation to other non-linear
models of innovation, like "Mode 2" and "national systems of innovation."
5. Non-linear models of innovation
As noted, non-linear models of innovation extend upon linear models by taking interactive and
recursive terms into account. These non-linear terms can be expected to change the causal relations
between input and output. The production rules in the systems under study, for example, can be
expected to change with the further development of the input/output relations (e.g., because of
economies of scale). Thus, the unit of operation may be transformed, as is typical when a pilot plant
in the chemical industry is scaled up to a production facility.
By changing the unit of analysis or the unit of operation at the reflexive level, one obtains a different
perspective on the system under study. But the system itself is also evolving. In terms of
methodologies, this challenges our conceptual apparatus, since one has to be able to distinguish
whether the variable has changed or merely the value of the variable. The analysis contains a
snapshot, while the reality provides a moving picture. One needs metaphors to reduce the complexity
for the discursive understanding. Geometrical metaphors can be stabilized by higher-order
codifications as in the case of paradigms. The understanding in terms of fluxes (that is, how the
variables as well as the value may change over time), however, calls for the use of algorithmic
simulations. The observables can then be considered as special cases which inform the expectations
(Leydesdorff 1995).
Innovation, in particular, can be defined only in terms of an operation. Both the innovator(s) and the
innovated system(s) are expected to be changed by the innovation. Furthermore, one is able to be
both a participant and an observer, and one is also able to change perspectives. In the analysis,
however, the various roles are distinguished although they can sometimes be fused in "real life"
events. Langton (1989) proposed to distinguish between the "phenotypical" level of the observables
and the "genotypical" level of analytical theorizing. The "phenotypes" remain to be explained and
the various explanations compete in terms of their clarity and usefulness for updating the
expectations. Confusion, however, is difficult to avoid given the pressure to jump to normative
conclusions, while different perspectives are continuously competing, both normatively and
Let us first focus on the problem of the unit of analysis and the unit of operation. In addition to
extending the linear (input/output) models of neo-classical and business economics, evolutionary
economists also changed the unit of analysis. Whereas neo-classical economics focused on markets
as networks in terms of input/output relations among individual (rational) agents, evolutionary
economists have tended to focus on firms as the specific (and bounded) carriers of an innovation
process. Both the unit of analysis and the unit of operation were changed (Andersen 1994; cf.
Alchian 1950).
Lundvall (1988, at p. 357) noted that the interactive terms between demand and supply in
user-producer relations assume a system of reference in addition to the market. The classical dispute
in innovation theory had, in his opinion, referred to the role of demand and supply, that is, market
forces, in determining the rate and direction of the process of innovation (cf. Mowery and Rosenberg,
1979; Freeman, 1982, p. 211). If, however, the dynamics of innovation (e.g., product competition)
are expected to be different from the dynamics of the market (e.g., price competition), an alternative
system of reference for the selection should also be specified. For this purpose, Lundvall proposed
"to take the national system of production as a starting point when defining a system of innovation"
(p. 362).
Lundvall added that the national system of production should not be considered as a closed system:
"the specific degree and form of openness determines the dynamics of each national system of
production." In our opinion, as a first step, innovation systems should be considered as the dynamics
of change in systems of both production and distribution. From this perspective, national systems
compete in terms of the adaptability of their knowledge infrastructure. How are competences
distributed for solving "the production puzzle" which is generated by uneven technological
developments across sectors (Nelson & Winter 1975; Nelson 1982)? The infrastructure conditions
the processes of innovation which are possible within and among the sectors. In particular, the
distribution of relevant actors contains an heuristic potential which can be made reflexive by a
strategic analysis of specific strengths and weaknesses (Pavitt 1984).
The solution of the production puzzle typically brings government into the picture shifting the
dynamics from a double to a triple helix. The consequent processes of negotiation are both complex
and dynamic: one expects that the (institutional) actors will be reproduced and changed by the
interactions. Trilateral networks and hybrid organzations are created for resolving social and
economic crises. The actors from the different spheres negotiate and define new projects, such as the
invention of the venture capital firm in New England in the early post-war era (Etzkowitz,
forthcoming). Thus, a Triple Helix dynamics of University-Industry-Government Relations is
generated endogeneously.
Gibbons et al. (1994) argued that this "new mode of the production of scientific knowledge" has
become manifest. But: how are these dynamics in the network arrangements between industries,
governments, and academia a consequence of the user-producer interactions foregrounded by
Lundvall (1988)? Are national systems still a relevant unit of analysis? Since the new mode of
knowledge production ("Mode 2") is characterized as an outcome, it should, in our opinion, be
considered as an emerging system. The emerging system rests like a hyper-network on the networks
on which it builds (such as the disciplines, the industries, and the national governments), but the
knowledge-economy transforms "the ship while a storm is raging on the open sea" (Neurath et al.,
Science has always been organized through networks, and to pursue practical as well as theoretical
interests. Centuries before “Mersenne”, was transmogrified into an Internet site, he was an individual,
who by visits and letters, knitted the European scientific community together. The Academies of
Science played a similar role in local and national contexts from the 16th century.
The practical impetus to scientific discovery is long-standing. Robert K. Merton's (1938)
dissertation reported that between 40-60% of discoveries in the 17th century could be classified as
having their origins in trying to solve problems in navigation, mining, etc. Conversely, solution of
practical problems through scientific means has been an important factor in scientific development,
whether in German pharmaceutical science in the 17th century (Gustin 1975) or in the British
sponsored competition to provide a secure basis for navigation (Sobel, 1995).
The so-called "Mode 2" is not new; it is the original format of science before its academic
institutionalization in the nineteenth century. Another question to be answered is why "Mode 1" has
arisen after "Mode 2": the original organizational and institutional basis of science, consisting of
networks and invisible colleges (cf. Weingart, 1997; Godin, 1998).Where have these ideas, of the
scientist as the isolated individual and of science separated from the interests of society, come from?
"Mode 2" represents the material base of science, how it actually operates. "Mode 1" is a construct,
built upon that base in order to justify autonomy for science, especially in an earlier era when it was
still a fragile institution and needed all the help it could get.
In the U.S.A., during the late 19th century, large fortunes were given to found new universities, and
expand old ones. There were grave concerns among many academics that the industrialists making
these gifts would try to directly influence the universities, by claiming rights to hire and fire
professors as well as well as to decide what topics were acceptable for research and instruction
(Storr, 1953). To carve out an independent space for science, beyond the control of economic
interests, a physicist, Henry Rowland, propounded the doctrine that if anyone with external interests
tried to intervene, it would harm the conduct of science. As President of the American Association
for the Advancement of Science, he promoted the ideology of pure research in the late 19th century.
Of course, at the same time as liberal arts universities oriented toward pure research were being
founded, land grant universities, including MIT, pursued more practical research strategies. These
two contrasting academic modes existed in parallel for many years.
Decades hence, Robert K. Merton posited the normative structure of science in 1942 and
strengthened the ideology of “pure science.” His emphasis on universalism and skepticism was a
response to a particular historical situation, the need to defend science from corruption by the Nazi
doctrine of a racial basis for science and from Lysenko’s attack on genetics in the Soviet Union.
Merton’s formulation of a set of norms to protect the free space of science was accepted as the basis
for an empirical sociology of science for many years.
The third element in establishing the ideology of pure science was, of course, the Bush Report of
1945. The huge success of science in supplying practical results during World War II in one sense
supplied its own legitimation for science. But with the end of the war at hand and wanting to insure
that science was funded in peacetime, a rationale was needed in 1944 when Bush persuaded President
Roosevelt to write a letter commissioning the report (Bush 1980).
In the first draft of his report, Bush proposed to follow the then current British method of funding
science at universities. It would be distributed on a per capita basis according to the number of
students at each school. In the contemporary British system of a small number of universities, the
funds automatically went to an elite. However, if that model had been followed in the U.S., even in
the early post war era, the flow of funds would have taken a different course. The funding would not
only have flowed primarily to a bi-coastal academic elite but would have been much more broadly
distributed across the academic spectrum, especially to the large state universities in the Midwest.
In the time between the draft and the final report, the mechanism for distribution of government funds
to academic research was revised and “peer review” was introduced. Adapted from Foundation
practices in the 1920s and 30s, it could be expected that "the peers," the leading scientists who would
most surely be on those committees, would distribute the funds primarily to a scientific elite. The
status system of U.S. universities that had been in place from the 1920s was reinforced.
This model of “best science” is no longer acceptable to many as the sole basis for distribution of
public research funds. Congresspersons who represent regions with universities that are not
significant recipients of research funds have disregarded peer review and distributed research funds
by direct appropriation, much as roads and bridges are often sited through “log rolling” and “pork
barrel” processes. Nevertheless, these politically directed funds support also serious scientific
research and instrumentation projects. Even when received by schools with little or no previous
research experience, these “one time funds” are typically used to rapidly build up competencies in
order to compete within the peer review system.
Indeed, when a leading school, Columbia University, needed to renew the infrastructure of its
chemistry department, it contracted with the same lobbying firm in Washington DC as less well-
known schools. Through public relations advice, Columbia relabeled its chemistry department "The
National Center for Excellence in Chemistry." A special federal appropriation was made and the
research facilities were renovated and expanded. To hold its faculty, the university could not afford to
wait for the slower route of peer review, and likely smaller amounts of funding.
Increasing competition for research funds among new and old actors has caused an incipient
breakdown of “peer review,” a system that could best adjudicate within a moderate level of
competition. As competition for research funds continues to expand, how should the strain be
adjusted? Some propose shrinking the research system; others suggest linking science to new sources
of legitimation such as regional development.
6. The Future Legitimation of Science
It is nowadays apparent that the development of science provides much of the basis for future
industrial development. These connections, however, have been present from the creation of science
as an organized activity in the 17th century. Marx pointed them out again in the mid-19th century in
connection with the development of chemical industry in Germany. At the time, he developed a thesis
of the growth of science-based industry on the basis of a single empirical example: Perkins researches
on dyestuffs in the UK leading to the development of an industry in Germany.
The potential of science to contribute to economic development has become a source of regional and
international competition at the turn of the millenium. Until recently, the location of research was of
little concern. The relationship between the site where knowledge is produced and its eventual
utilization was not seen to be tightly linked, even as a first mover advantage. This view has changed
dramatically in recent years, as has the notion that high-tech conurbations, like Route 128 and Silicon
Valley, are unique instances that can not be replicated. The more recent emergence of Austin,
Texas, for example, is based in part on the expansion of research at the University of Texas, aided by
state as well as industry and federal funds.
Less research intensive regions are by now well aware that science, applied to local resources, is the
basis of much of their future potential for economic and social development. In the U.S.A., it is no
longer acceptable for research funds to primarily go to the east and west coasts with a few places in
between in the Midwest. The reason why funding is awarded on bases other than the peer review
system, is that all regions want a share of research funding
The classic legitimation for scientific research as a contribution to culture still holds and military and
health objectives also remain a strong stimulus to research funding. Nevertheless, the future
legitimation for scientific research, which will keep funding at a high level, is that it is increasingly
the source of new lines of economic development.
Newly created disciplines are often the basis for these heightened expectations. Such disciplines do
not arise only from the subdivision of new disciplines from old ones, as in the 19th century (Ben
David and Collins, 1966). New disciplines have arisen, more recently, through syntheses of practical
and theoretical interests. For example, computer science grew out of elements of older disciplines
such as electrical engineering, psychology, philosophy, and a machine. Materials science and other
fields such as nano-technology that are on every nation’s critical technology list were similarly
The university can be expected to remain the core institution of the knowledge sector as long as it
retains its original educational mission (Etzkowitz, Webster, Gebhardt, and Terra, this issue).
Teaching is the university’s comparative advantage, especially when linked to research and economic
development. Students are also potential inventors. They represent a dynamic flow-through of
“human capital” in academic research groups, as opposed to more static industrial laboratories and
research institutes. Although they are sometimes considered a necessary distraction, the turnover of
students insures the primacy of the university as a source of innovation.
The university may be compared to other recently proposed contenders for knowledge leadership,
such as the consulting firm. A consulting company draws together widely dispersed personnel for
individual projects and then disperses them again after a project, solving a client’s particular
problem, is completed. Such firms lack the organizational ability to pursue a cumulative research
program as a matter of course. The university’s unique comparative advantages is that it combines
continuity with change, organizational and research memory with new persons and new ideas,
through the passage of student generations. When there is a break in the generations, typically caused
by a loss of research funding, one academic research group disappears and can be replaced by
Of course, as firms organize increasingly higher level training programs (e.g., Applied Global
University at the Applied Materials Devices Corporation, a semi-conductor equipment manufacturer
in Silicon Valley) they might in the future also, individually or jointly, attempt to give out degrees.
Companies often draw upon personnel in their research units, as well as external consultants, to do
some of the teaching in their corporate universities. Nevertheless, with a few notable exceptions, such
as the RAND Corporation, they have not yet systematically drawn together research and training into
a single framework. However, as the need for life-long learning increases, a university tied to the
workplace becomes more salient.
7. Implications of the Triple Helix Model
The Triple Helix denotes not only the relationship of university, industry and government, but also
internal transformation within each of these spheres. The university has been transformed from a
teaching institution into one which combines teaching with research, a revolution that is still ongoing,
not only in the U.S.A., but in many other countries. There is a tension between the two activities but
nevertheless they co-exist in a more or less compatible relationship with each other because it has
been found to be both more productive and cost effective to combine the two functions.
The Triple Helix overlay provides a model at the level of social structure for the explanation of
"Mode 2" as an historically emerging structure for the production of scientific knowledge, and its
relation to "Mode 1." First, the arrangements between industry and government no longer need to be
conceptualized as exclusively between national governments and specific industrial sectors. Strategic
alliances cut across traditional sector divides; governments can act at national, regional, or
increasingly also at international levels. Corporations adopt "global" postures either within a formal
corporate structure or by alliance. Trade blocks like the EU, NAFTA, and Mercosul provide new
options for breaking "lock-ins," without the sacrifice of competitive advantages from previous
constellations. For example, the Airbus can be considered as an interactive opportunity for
recombination at the supra-national level (Frenken, this issue).
Second, the driving force of the interactions can be specified as the expectation of profits. "Profit"
may mean different things to the various actors involved. A leading edge consumer, for example,
provides firms and engineers with opportunities to perceive "reverse salients" in current product lines
and software. Thus, opportunities for improvements and puzzle-solving trajectories can be defined.
Note that analytically the drivers are no longer conceptualized as ex ante causes, but in terms of
expectations that can be evaluated only ex post. From the evolutionary perspective, selection (ex
post) is structure determined, while variation may be random (Arthur 1988; Leydesdorff and Van den
Besselaar 1998).
Third, the foundation of the model in terms of expectations leaves room for uncertainties and chance
processes. The institutional carriers are expected to be reproduced as far as they have been
functional hitherto, but the negotiations can be expected to lead to experiments which may thereafter
also be institutionalized. Thus, a stage model of innovation can be specified.
The stages of this model do not need to correspond with product life cycle theory. Barras (1990), for
example, noted that in ICT "a reverse product life" cycle seems to be dominant. Bruckner et al.
(1994) proposed niche-creation as the mechanism of potential lock-out in the case of competing
technologies. A successful innovation changes the landscape, that is, the opportunity structure for the
institutional actors involved. Structural changes in turn are expected to change the dynamics.
Fourth, the expansion of the higher-education and academic research sector has provided society with
a realm in which different representations can be entertained and recombined in a systematic manner.
Kaghan and Barett (1997) have used in this context the term "desktop innovation" as different from
the laboratory model (cf. Etzkowitz, 1999). Knowledge-intensive economies can no longer be based
on simple measures of profit maximization: utility functions have to be matched with opportunity
structures. Over time, opportunity structures are recursively driven by the contingencies of prevailing
and possible technologies. A laboratory of knowledge-intensive developments is socially available
and can be improved upon (Etzkowitz and Leydesdorff 1995). As this helix operates, the human
capital factor is further developed along the learning curves and as an antidote to the risk of
technological unemployment (Pasinetti, 1981).
Fifth, the model also explains why the tensions need not to be resolved. A resolution would hinder
the dynamics of a system which lives from the perturbations and interactions among its subsystems.
Thus, the subsystems are expected to be reproduced. When one opens the black-box one finds
"Mode 1" within "Mode 2," and "Mode 2" within "Mode 1." The system is neither integrated nor
completely differentiated, but it performs on the edges of fractional differentiations and local
integrations. Using this model, one can begin to understand why the global regime exhibits itself in
progressive instances, while the local instances inform us about global developments in terms of the
exceptions which are replicated and built upon.
Case materials enable us to specify the negative selection mechanisms reflexively. Selection
mechanisms, however, remain constructs. Over time, the inference can be corroborated. At this end,
the function of reflexive inferencing based on available and new theories moves the system forward
by drawing attention to possibilities for change.
Sixth, the crucial question of the exchange media economic expectations (in terms of profit and
growth), theoretical expectations, assessment of what can be realized given institutional and
geographic constraints have to be related and converted into one another. The helices
communicate recursively over time in terms of each one's own code. Reflexively, they can also take
the role of each other, to a certain extent. While the discourses are able to interact at the interfaces,
the frequency of the external interaction is (at least initially) lower than the frequency within each
helix. Over time and with the availability of ICT, this relation is changing.
The balance between spatial and virtual relations is contingent upon the availability of the exchange
media and their codifications. Codified media provide the system with opportunities to change the
meaning of a communication (given another context) while maintaining its substance (Cowan and
Foray 1997). Despite the "virtuality" of the overlay, this system is not "on the fly": it is grounded in a
culture which it has to reproduce (Giddens 1984). The retention mechanism is no longer given, but
"on the move": it is reconstructed as the system is reconstructed, that is, as one of its subdynamics.
As the technological culture provides options for recombination, the boundaries of communities can
be reconstituted. The price may be felt as a loss of traditional identities or alienation, or as a concern
with the sustainability of the reconstruction, but the reverse of "creative destruction" is the option of
increasing development. The new mode of knowledge production generates an Endless Transition
that continuously redefines the borders of the Endless Frontier.
8. The organization of the theme issue
As noted above, this issue is organized in three main parts, addressing (1) institutional
transformation, (2) evolutionary mechanisms, and (3) the second academic revolution. Each part
contains five contributions.
In Part One ("Institutional Transformations"), Michael Nowak and Charles Grantham open the
discussion with a paper about the impact of the Internet on incubation as an institutional mechanism
for technological innovation. The increased complexity of the process induces reflexivity about the
choices to be made, and human capital becomes increasingly crucial for carrying the transformations.
The failure of the "opening to the market" as an answer to the state-dominated economies in the
former Soviet Union, because of the neglect of the knowledge-intensive dimension, is discussed by
testing three models against each other in Judith Sedaitis' paper entitled "Technology Transfer in
Transitional Economies: Comparing Market, State, and Organizational Frameworks." The author
concludes that processes of transfer in these cases can be understood at the intermediate network
Norma Morris, in "Vial Bodies: Conflicting Interests in the Move to New Institutional Relationships
in Biological Medicines Research and Regulation," discusses normative issues that arise when the
borders are no longer defined institutionally and governmentally. The case of the EU places the role
of safety regulation at national and transnational levels on the agenda. In a paper entitled "The
Evolution of Rules for Access to Megascience Research Environments Viewed from Canadian
Experience," Cooper Langford and Martha Whitney Langford document what it means for the
organization of Canadian science that government and industry relations are deeply involved in this
enterprise. Are the Kudos-norms of Merton (1942) increasingly being replaced by a new set of
norms (Ziman 1994)? If so, what are the expected effects on reward systems and funding? In a
contribution to the latter question, Shin-Ichi Kobayashi argues that a third form of funding can be
distinguished nowadays (in addition to peer recognition and institutional allocation). The author
develops the new format using the metaphor of the audition system for the performing arts.
Thus, not only the institutions themselves are tranformed, but also their mechanisms of
transformation. These evolutionary mechanisms are central to the second part of the theme issue.
The contribution from the Aveiro team (Eduardo Anselmo de Castro, Carlos José Rodrigues, Carlos
Esteves, and Artur da Rosa Pires) returns to the impact of ICT on changing the stage. How can
institutional arrangements be shaped to match the options which telematics provide? How can a
retention mechanism be organized as a niche or a habitat for knowledge-intensive developments?
While the Portuguese team focuses on the regional level, Susanne Giesecke takes the analysis to the
level of comparing national governments in her contribution entitled "The Contrasting Roles of
Government in the Development of the Biotechnology Industries in the U.S. and Germany." She
notes the counter-effective policies of German governments which have operated on the basis of
assumptions about previous developments. Policies have to be updated in terms of bottom-up
processes and thus come to be understood in terms of reflexive feedbacks (instead of control).
Rosalba Casas, Rebeca de Gortari, and Ma. Josefa Santos from Mexico combine the issues of
regional developments and differences between the technologies involved by cross-tabling them for
the case of Mexico. These authors focus on what they call "the building of knowledge spaces." How
is the interrelationship between knowledge-intensity, industrial activity, and institutional control
shaped in terms of inter-human and inter-institutional relations? What is the function of shared
culture, values, and trust? Is the region a habitat for the technology, or the technology a precondition
for restructuring the region?
In a contribution entitled "The Triple Helix: An Evolutionary Model of Innovations," Loet
Leydesdorff uses simulations to show how a "lock-in" can be enhanced using a co-evolution like the
one between regions and technologies. A third source of random variation, however, may intervene,
reversing the order in a later stage and leading to more complex arrangements of market segmentation
(that is, different suboptima). A mechanism for "lock-out" can also be specified.
Koen Frenken takes the complexity approach one step further by confronting it with empirical data in
the case of the aircraft industry. Using Kauffman's (1993) model of "rugged fitness landscapes" he
shows the working of a Triple Helix in different phases of this industry (cf. Frenken and Leydesdorff,
forthcoming). The model can be extended to account for the additional degree of freedom in
international collaborations to develop new aircraft. The failure of Fokker Aircraft, for example, can
be explained using these concepts: one cannot bet on two horses at the same time, since the markets
are fiercely competitive, technological infrastructures are expensive, and learning curves are steep.
In the third part of the issue, we turn to the Second Academic Revolution. In their contribution
entitled "The Place of Universities in the System of Knowledge Production," Benoît Godin and Yves
Gingras argue against the thesis that the university would have lost its salient position in the
university-industry-government relations of "Mode 2." Using scientometric data, they show that
collaboration with academic teams is central to the operations of the networks which transform this
knowledge infrastructure. Although based on Canadian data, the argument is made that this holds
true also for other OECD countries.
From another world region, Judith Sutz reports about university-industry-government relations in
Latin America. These young democracies, on the one hand, wish to free themselves from the
limitation of the so-called "import substitution" regime by opening up to the market. On the other
hand, the connections are then established through the world system, and regional infrastructures tend
to remain underdeveloped. The issue will be central to the Third Triple Helix Conference to be held
in Rio de Janeiro, 26-29 April 2000. How can social, economic, and scientific developments be
networked at the regional level? What does niche management mean in an open system's
In a contribution entitled "Institutionalizing the Triple Helix: Research Funding and Norms in the
Academic System," Mats Benner and Ulf Sandström take a neo-institutional approach to the
transformation of the university system in Europe. How does the system react (resist and embody)
institutional transformation and neo-evolutionary pressures? In a further article, Eric Campbell and
his colleagues raise the question of how this affects research practices in terms of "Data Withholding
in Academic Medicine." Can characteristics of faculty denied access to research results and
biomaterials be distinguished?
In a final article, Henry Etzkowitz, Andrew Webster, Christiane Gebhardt, and Branca Terra
substantiate their claim that the transformation of the university system is a worldwide phenomenon.
In addition to research and higher eduction, the university nowadays has a third role in regional and
economic development because of the changing nature of both knowledge production and economic
production. While a "hands off" may have been functional to previous configurations, the exigencies
of today demand a more intensive interrelationship. As noted, a Triple Helix arrangement that tends
to reorganize the knowledge infrastructure in terms of possible overlays, can be expected to be
generated endogenously.
We acknowledge support from the U.S. National Science Foundation, the European Commission DG
XII, the Fundação Coppetec in Brazil, the CNRS in France, the Netherlands Graduate School for
Science, Technology and Modern Culture WTMC, the State University of New York SUNY, and our
respective departments. We thank Alexander Etzkowitz for assistance with graphics.
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Figure 1
An Etatistic Model of University-Industry-
Government Relations
Figure 2:
A "laissez-faire" Model of University-
Industry-Government Relations
Figure 3
The Triple Helix Model of University-Industry-
Government relations
networks and
Figure 4
The overlay of communications and
expectations at the network level guides
the reconstruction of institutional
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... All three players of the Triple Helix model fulfil their own roles and at the same time take on the role of the other actors in developing a triple interface between universities, industry and the state. A balanced arrangement of the three main players in the Triple Helix model offers the most favourable environment for innovation Etzkowitz, Leydesdorff, 2000). ...
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Regarding statistical and sociographic analyses, demographic crises of rural areas in the forms of depopulation, extreme ageing and outflux towards urban centrum areas – generated by the emergence of urban agglomeration and the structural change of the economic framework – can be considered a global phenomenon for more than 3 decades, seriously eroding the competitiveness and sustainability of peripheral regions. In the case of Hungary, rural society is also facing the same demographic and developmental challenge since the beginning of the 90s, thus, the Hungarian countryside and borderland areas became the most endangered areas in terms of demographic and economic sustainability. For this reason, national governments must enact adequately implemented policy measures against this demographic process so that declining rural areas may protect their remaining potentials and provide new initiatives for raising rural competitiveness by economic and social means. In this engagement, well-designed development models – such as the tetrahedron, rhombus, pyramid or helix models – can serve as an optimal basis for policy planning and implementation. However, even if these models are proven to be both scientifically and practically confirmed, in general, there is a lack of special “borderland” aspects in the identified basic models in terms of developing marginalized rural areas especially in areas in the proximity of borders. Therefore, the analysed models cannot be applied conveniently in these areas with special burdens due to their borderland status. For this aim, our analysis is making an attempt to establish an upgraded model structure incorporating both the most relevant aspects of the analysed basic models and the most important segments of the borderland’s social and economic status. Thus, we intend to provide a meaningful added value for supporting rural development efforts in Hungarian and European border territories as well.
... Our generative model deviates from previous work that simply scores a paper or patent's novelty by comparing its components to those of an average or random one 8,9 . It also deviates from work that focuses on the institutional structures that influence discovery in science [10][11][12][13][14][15] model research outputs leads to better assessments of research novelty and its reception as surprise by researchers within scientific and technical communities. Because of this complex interplay, we use unexpected, novel, surprising, and their derivations interchangeably in this paper. ...
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We investigate the degree to which impact in science and technology is associated with surprising breakthroughs, and how those breakthroughs arise. Identifying breakthroughs across science and technology requires models that distinguish surprising from expected advances at scale. Drawing on tens of millions of research papers and patents across the life sciences, physical sciences and patented inventions, and using a hypergraph model that predicts realized combinations of research contents (article keywords) and contexts (cited journals), here we show that surprise in terms of unexpected combinations of contents and contexts predicts outsized impact (within the top 10% of citations). These surprising advances emerge across, rather than within researchers or teams—most commonly when scientists from one field publish problem-solving results to an audience from a distant field. Our approach characterizes the frontier of science and technology as a complex hypergraph drawn from high-dimensional embeddings of research contents and contexts, and offers a measure of path-breaking surprise in science and technology.
... The move from linear to interactive models is in line with the importance that networks of collaboration have in the innovation process, as suggested by the literature [8][9][10][11][12][13][14]49]. In fact, this trend can be seen in actual forms of innovation such as crowdsourcing and free innovation, which have been gaining attention across the scientific community [50][51][52], and fits in the extended innovation network innovation model according to Table 2. Other models, such as the triple helix model [53] (Figure 3a), which eventually evolved to the quintuple helix (Figure 3b), have drawn particular interest in recent years because they integrate three different high-level actors (universities, industries, and government), with the aim that by a synergy effect, their combined value and performance will be greater than the individual sum of each one. The model advocates that the proximity between key actors in an innovation system increases collaboration between the different actors and is important for knowledge transfer, this being a major factor for successful innovation [54]. ...
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It is often argued that efficient collaboration is the key to success. However, research shows that if collaboration is not properly managed, collaborative risks may emerge, threatening business success. Furthermore, research shows that there is a lack of models to support the management of collaborative initiatives in organizations. To address this lack, presented in this work is a model to manage collaborative risks in organizations that work under the open innovation and the hybrid stage-gate development frameworks (two of the most popular collaborative frameworks in product and process development). The model presented in this work is a novel approach to manage collaborative risks in the open innovation and the hybrid stage-gate frameworks, and was developed based on network graph-theory to be used to identify informal collaborative interactions that may lead to the emergence of three major collaborative risks: (1) partner choice risks, (2) task assignment risks, and (3) behavioral risks. The results of the application of the proposed model in a real organizational collaborative context illustrated in the case study show that such collaborative risks can be identified in a timely manner, enabling an organization to efficiently and preventively act to minimize or eliminate the undesired effects of the mentioned collaborative risks.
... The creation of an industrial cluster offers a region several benefits and opportunities, as it brings together the main economic actors, namely the industry, government, and educational sector, and provides the appropriate infrastructure for all of them to work cooperatively [64][65][66]. The region's economic resources (private and public) are used in a more structured and rationalized way, thus achieving a more constructive return on investment [64,[67][68][69]. ...
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The phenomenon of firms grouping together has been extensively researched and is commonly known as industrial clusters. There are various ways to categorize these clusters, and in this paper, we adopt Markusen's classification, which identifies four distinct types of industrial districts: the Marshallian/Italianate type, the hub-and-spoke type, the satellite industrial platforms, and the state-anchored clusters. Adding to Markusen's typology, we will also try to delineate these two clusters' "European Aspects". We will examine if they have developed any "inter-European" synergy/ies with other entities (clusters, companies, E.U. institutions, etc.) of the E.U. ecosystem. The creation of such synergies includes the creation of technology innovation and interpersonal networks to serve as conduits for the diffusion of knowledge and exchange of information, the development of innovation initiatives between the entities of the technological ecosystem of the E.U. defense industry, and the creation of tangible "knowledge links". The aim of this study is to investigate which of the four types of industrial clusters described by Markusen the French Aerospace Valley cluster of the Midi-Pyrénées and Aquitaine regions and the Andalucia Aerospace cluster belong to.
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Productivity growth is important for long-term economic growth and development, and technology adoption is one of its key drivers. This study empirically assesses whether political regimes are a significant determinant of technology diffusion. Specifically, we examine the effects of political regimes on diffusion of technology using data based on a sample of 104 technologies from 137 countries for the period 1901 to 2000. We consider detailed categories of technologies and investigate the differences in the impacts of political regimes on each technology. Our estimation results show that democracy does not have a significant impact on the overall diffusion of technology but it is positively associated with the diffusion of health- and agriculture-related technologies. Furthermore, the diffusion of infrastructure, general, and other sector-specific technologies is not influenced by political regimes. Considering different types of democracies and dictatorships, we find that parliamentary democracy has a positive impact on health- and agriculture-related technology diffusion. On the contrary, all types of dictatorships, namely civilian, military, and royal, have negative impacts on diffusion of technology.
Im letzten Jahrzehnt haben sich die Anstrengungen verstärkt, in der Region Hamburg einen tragfähigen Standort für moderne Biotechnologien zu etablieren. Die wachsende Intensität der Hamburger Biotechnologieförderung kann als Reflex auf den drastischen Einbruch neu gegründeter Biotechnologiefirmen gesehen werden, der sich in den Jahren zuvor vollzog. Zahlreiche Unternehmen verschwanden ungeachtet guter Konzepte und guter Technologien nach kurzer Zeit wieder von der Bildfläche. Vor diesem Hintergrund wurde am Forschungsschwerpunkt Biotechnologie, Gesellschaft und Umwelt (BIOGUM) der Universität Hamburg im Wintersemester 2004/2005 die Vortragsreihe "Strategien biotechnischer Innovation" veranstaltet, deren Ziel es sein sollte, aus unterschiedlichen Perspektiven die Möglichkeiten, Probleme und Grenzen der Innovationssteuerung zu untersuchen. Die Texte des vorliegenden Bandes gehen auf Vorträge zurück, die von den Beitragenden im Rahmen dieser Vortragsreihe gehalten wurden.
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The landscape of science is changing radically. In particular, there is increasing heterogeneity of actors, research sites, knowledge and networks. Science policy-makers have to respond to these changes, but heterogeneity makes it more difficult to impose own goals on the research system. Yet, if the dynamics in and of the system are understood, other policy approaches become possible. Here two important systemic aspects of research systems — ‘steering’ (the extent to which the system is sensitive to attempts of a principal, generally the state, to implement own objectives) and ‘aggregation’ (the organisation of processes of agenda-building within the system) are introduced. It is argued that, because of the changes in science, a post-modern research system is both possible and desirable in which aggregation is favoured over steering.
On a mountainside in sunny Tuscany, in October 1989, 96 people from 23 countries on five continents gathered to learn and teach about the problems of managing contemporary science. The diversity of economic and political systems represented in the group was matched by our occupations, which stretched from science policy practitioners, through research scientists and engineers, through academic observers of science and science policy. It was this diversity, along with the opportunities for infonnal discussion provided by long meals and remote location, that made the conference a special learning experience. Except at lecture time, it was impossible to distinguish the "students" at this event from the "teachers," and even the most senior members of the teaching staff went away with a sense that they had learned more from this group than from many a standard conference on science policy they had attended. The flavor of the conference experience cannot be captured adequately in a proceedings volume, and so we have not tried to create a historical record in this book. Instead, we have attempted to illustrate the core problems the panicipants at the conference shared, discussed, and debated, using both lectures delivered by the fonnal teaching staff and summaries of panel discussions, which extended to other panicipants and therefore increased the range of experiences reponed.
At the beginning of 1929 Moritz Schlick received a very tempting call to Bonn. After some vacillation he decided to remain in Vienna. On this occasion, for the first time it became clear to him and us that there is such a thing as the ‘Vienna Circle’ of the scientific conception of the world, which goes on developing this mode of thought in a collaborative effort. This circle has no rigid organization; it consists of people of an equal and basic scientific attitude; each individual endeavours to fit in, each puts common ties in the foreground, none wishes to disturb the links through idiosyncrasies. In many cases one can deputise for another, the work of one can be carried on by another.