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Loet Leydesdorff on the Triple Helix: How Synergies in University-Industry-Government Relations can Shape Innovation Systems

This is the sixth and last in a series of Talks dedicated to the technopolitics of International Relations, linked to the forthcoming double volume 'The Global Politics of Science and Technology' edited by Maximilian Mayer, Mariana Carpes, and Ruth Knoblich

The relationship between technological innovation
processes and the nation state remains a challenge for the discipline of
International Relations. Non-linear and multi-directional
characteristics of knowledge production, and the diffusive nature of knowledge
itself, limit the general ability of governments to influence and steer
innovation processes. Loet Leydesdorff advances the framework of the "Triple
Helix" that disaggregates national innovation systems into
evolving university-industry-government eco-systems. In this Talk, amongst others, he shows that these eco-systems can be
expected to generate niches with synergy at all scales, and emphasizes that, though
politics are always involved, synergies develop unintentionally.

Print version of this Talk (pdf)

What is the most relevant aspect
of the dynamics of innovation for the discipline of International Relations?

The
main challenge is to endogenize the notions of technological progress and
technological development into theorizing about political economies and nation states.
The endogenization of technological innovation and technological development
was first placed on the research agenda of economics by evolutionary economists
like Nelson and Winter in the late 1970s and early 1980s. In this context, the
question was how to endogenize the dynamics of knowledge, organized knowledge,
science and technology into economic theorizing. However, one can equally well
formulate the problem of how to reflect on the global (sub)dynamics of
organized knowledge production in political theory and International Relations.

From
a longer-term perspective, one can consider that the nation states – the
national or political economies in Europe – were shaped in the 19th
century, somewhat later for Germany (after 1871), but for most countries it was
during the first half of the 19th century. This was after the French
and American Revolutions and in relation to industrialization. These nation
states were able to develop an institutional framework for organizing the
market as a wealth-generating mechanism, while the institutional framework
permitted them to retain wealth, to regulate market forces, and also to steer
them to a certain extent. However, the market is not only a local dynamics; it
is also a global phenomenon.

Nowadays,
another global dynamics is involved: science and technology add a dynamics different
from that of the market. The market is an equilibrium-seeking mechanism at each
moment of time. The evolutionary dynamics of science and technology nowadays
adds a non-equilibrium-seeking dynamics over time on top of that, and this puts
the nation state in a very different position. Combining an equilibrium-seeking
dynamics at each moment of time with a non-equilibrium seeking one over time
results in a complex adaptive dynamics, or an eco-dynamics, or however you want
to call it – these are different words for approximately the same thing.

For
the nation state, the question arises of how it relates to the global market
dynamics on the one side, and the global dynamics of knowledge and innovation on
the other. Thus, the nation state has to combine two tasks. I illustrated this
model of three subdynamics with a figure in my 2006 book entitled The Knowledge-Based Economy: Modeled,
measured, simulated (see image). The figure shows that first-order interactions
generate a knowledge-based economy as a next-order or global regime on top of
the localized trajectories of nation states and innovative firms. These complex
dynamics have first to be specified and then to be analyzed empirically.

For
example, the knowledge-based dynamics change the relation between government
and the economy; and they consequently change the position of the state in
relation to wealth-retaining mechanisms. How can the nation state be organized
in such a way as to retain wealth from knowledge locally, while knowledge (like
capital) tends to travel beyond boundaries? One can envisage the complex system
dynamics as a kind of cloud – a cloud
that touches the ground at certain places, as Harald Bathelt, for example,
formulated.

How
can national governments shape conditions for the cloud to touch and to remain
on the ground? The Triple Helix of University-Industry-Government Relations can
be considered as an eco-system of bi- and tri-lateral relations. The three
institutions and their interrelations can be expected to form a system carrying
the three functions of (i) novelty production, (ii) wealth generation, and
(iii) normative control. One tends to think of university-industry-government
relations first as neo-corporatist arrangements between these institutional
partners. However, I am interested in the ecosystem shaped through the tri- and
bilateral relationships.

This
ecosystem can be shaped at different levels. It can be a regional ecosystem or
a national ecosystem, for instance. One can ask whether there is a surplus of synergy
between the three (sub-)dynamics of university-industry-government relations
and where that synergy can generate wealth, knowledge, and control; in which
places, and along trajectories for which periods of time – that is, the same
synergy as meant by "a cloud touching the ground".

For
example, when studying Piedmont as a region in Northern Italy, it is
questionable whether the synergy in university-industry-government relations is
optimal at this regional level or should better be examined from a larger perspective
that includes Lombardy. On the one
hand, the administrative borders of nations and regions result from the
construction of political economies in the 19th century; but on the
other hand, the niches of synergy that can be expected in a knowledge-based
economy are bordered also; for example, in terms of metropolitan regions (e.g.,
Milan–Turin–Genoa).

Since
political dynamics are always involved, this has implications for International
Relations as a field of study. But the dynamic analysis is different from
comparative statics (that is, measurement at different moments of time). The
knowledge dynamics can travel and be "footloose" to use the words of Raymond
Vernon, although it leaves footprints behind. Grasping "wealth from knowledge"
(locally or regionally) requires taking a systems perspective. However, the
system is not "given"; the system remains under reconstruction and can thus be
articulated only as a theoretically informed hypothesis.

In
the social sciences, one can use the concept of a hypothesized system
heuristically. For example, when
analyzing the knowledge-based economy in Germany, one can ask whether more synergy
can be explained when looking at the level of the whole country (e.g., in terms
of the East-West or North-South divide) or at the level of Germany's Federal
States? What is the surplus of the nation or at the European level? How can one
provide political decision-making with the required variety to operate as a
control mechanism on the complex dynamics of these eco-systems?

A
complex system can be expected to generate niches with synergy at all scales,
but as unintended consequences. To what extent and for which time span can
these effects be anticipated and then perhaps be facilitated? At this point,
Luhmann's theory comes in because he has this notion of different codifications
of communication, which then, at a next-order level, begin to self-organize
when symbolically generalized.

Codes
are constructed bottom-up, but what is constructed bottom-up may thereafter
begin to control top-down. Thus, one should articulate reflexively the
selection mechanisms that are constructed from the bottom-up variation by
specifying the why as an hypothesis.
What are the selection mechanisms? Observable relations (such as
university-industry relations) are not neutral, but mean different things for
the economy and for the state; and this meaning of the observable relations can
be evaluated in terms of the codes of communication.

Against
Niklas Luhmann's model, I would argue that codes of communication can be
translated into one another since interhuman communications are not
operationally closed, as in the biological model of autopoiesis. One also needs a social-scientific perspective on the
fluidities ("overflows") and translations among functions, as emphasized, for
example, by French scholars such as Michel Callon and Bruno Latour. In
evolutionary economics, one distinguishes between market and non-market
selection environments, but not among selection environments that are
differently codified. Here, Luhmann's theory offers us a heuristic: The complex
system of communications tends to differentiate in terms of the symbolic
generalizations of codes of communication because this differentiation is
functional in allowing the system to process more complexity and thus to be
more innovative. The more orthogonal the codes, the more options for
translations among them. The synergy indicator measures these options as
redundancy. The selection environments, however, have to be specified historically
because these redundancies—other possibilities—are not given but rather constructed
over long periods of time.

How did you arrive where
you currently work on?

I
became interested in the relations between science, technology, and society as
an undergraduate (in biochemistry) which coincided with the time of the student
movement of the late 1960s. We began to study Jürgen Habermas in the framework
of the "critical university," and I decided to continue with a second degree in
philosophy. After the discussions between Luhmann and Habermas (1971), I
recognized the advantages of Luhmann's more empirically oriented systems approach
and I pursued my Ph.D. in the sociology of organization and labour.

In
the meantime, we got the opportunity to organize an interfaculty department for
Science and Technology Dynamics at the University of Amsterdam after a
competition for a large government grant. In the context of this department, I
became interested in methodology: how can one compare across case studies and
make inferences? Actually, my 1995 book The
Challenge of Scientometrics
had a kind of Triple-Helix model on the cover: How do cognitions, texts, and
authors exhibit different dynamics that influence one another?

For
example, when an author publishes a paper in a scholarly journal, this may add
to his reputation as an author, but the knowledge claimed in the text enters a
process of validation which can be much more global and anonymous. These
processes are mediated since they are based on communication. Thus, one can add
to the context of discovery (of authors) and the context of justification (of
knowledge contents) a context of mediation (in texts). The status of a journal,
for example, matters for the communication of the knowledge content in the article.
The contexts operate as selection environments upon one another.

In
evolutionary economics, one is used to distinguishing between market and
non-market selection environments, but not among more selection environments
that are differently codified. At this point, Luhmann's theory offers a new
perspective: The complex system of communications tends to differentiate in
terms of the symbolic generalization of codes of communication because this differentiation
among the codes of communication allows the system to process more complexity
and to be more innovative in terms of possible translations. The different
selection environments for communications, however, are not given but constructed
historically over long periods of time. The modern (standardized) format of the
citation, for example, was constructed at the end of the 19th
century, but it took until the 1950s before the idea of a citation index was
formulated (by Eugene Garfield). The use of citations in evaluative
bibliometrics is even more recent.

In
evolutionary economics, one distinguishes furthermore between (technological)
trajectories and regimes. Trajectories can result from "mutual shaping" between
two selection environments, for example, markets and technologies. Nations and
firms follow trajectories in a landscape. Regimes are global and require the
specification of three (or more) selection environments. When three (or more)
dynamics interact, symmetry can be broken and one can expect feed-forward and
feedback loops. Such a system can begin to flourish auto-catalytically when the
configuration is optimal.

From
such considerations, that is, a confluence of the neo-institutional program of
Henry Etzkowitz and my neo-evolutionary view, our Triple Helix model emerged in
1994: how do institutions and functions interrelate and change one another or,
in other words, provide options for innovation? Under what conditions
can university-industry-government relations lead to wealth generation and
organized knowledge production? The starting point was a workshop about Evolutionary Economics and Chaos Theory: New
directions for technology studies held in Amsterdam in 1993. Henry
suggested thereafter that we could collaborate further on university-industry
relations. I answered that I needed at least three (sub)dynamics from the
perspective of my research program, and then we agreed about "A Triple Helix of
University-Industry-Government Relations". Years later, however, we took our
two lines of research apart again, and in 2002 I began developing a
Triple-Helix indicator of synergy in a series of studies of national systems of
innovation.

What would you give as
advice to students who would like to get into the field of innovation and
global politics?

In
general, I would advise them to be both a specialist and broader than that.
Innovation involves crossing established borders. Learn at least two languages.
If your background is political science, then take a minor in science &
technology studies or in economics. One needs both the specialist profile and
the potential to reach out to other audiences by being aware of the need to
make translations between different frameworks. Learn to be reflexive about the
status of what one can say in one or the other framework.

For
example, I learned to avoid the formulation of grandiose statements such as
"modern economies are knowledge-based economies," and to say instead: "modern
economies can increasingly be considered as knowledge-based economies." The
latter formulation provides room for asking "to what extent," and thus one can
ask for further information, indicators, and results of the measurement.

In
the sociology of science, specialisms and paradigms are sometimes considered as
belief systems. It seems to me that by considering scholarly discourses as
systems of rationalized expectations one can make the distinction between
normative and cognitive learning. Normative learning (that is, in belief
systems) is slower than cognitive learning (in terms of theorized expectations)
because the cognitive mode provides us with more room for experimentation: One
can afford to make mistakes, since one's communication and knowledge claims
remain under discussion, and not one's status as a communicator. The cognitive
mode has advantages; it can be considered as the surplus that is further
developed during higher education. Normative learning is slower; it dominates
in the political sphere.

What
does the "Triple Helix" reveal about the fragmentation of "national innovation
systems"?

In
2003, colleagues from the Department of Economics and Management Studies at the
Erasmus University in Rotterdam offered me firm data from the Netherlands containing
these three dimensions: the economic, the geographical, and the technological dimensions
in data of more than a million Dutch firms. I presented the results at the
Schumpeter Society in Turin in 2004, and asked whether someone in the audience
had similar data for other countries. I expected Swedish or Israeli colleagues
to have this type of statistics, but someone from Germany stepped in, Michael
Fritsch, and so we did the analysis for Germany. These studies were first
published in Research Policy.
Thereafter, we did studies on Hungary, Norway, Sweden, and recently also China
and Russia.

Several
conclusions arise from these studies. Using entropy statistics, the data can be
decomposed along the three different dimensions. One can decompose national
systems geographically into regions, but one can also decompose them in terms
of the technologies involved (e.g., high-tech versus medium-tech). We were
mainly relying on national data. And of course, there are limitations to the
data collections. Actually, we now have international data, but this is
commercial data and therefore more difficult to use reliably than governmental
statistics.

For
the Netherlands, we obtained the picture that would more or less be expected:
Amsterdam, Rotterdam, and Eindhoven are the most knowledge-intensive and
knowledge-based regions. This is not surprising, although there was one surprise:
We know that in terms of knowledge bases, Amsterdam is connected to Utrecht and
then the geography goes a bit to the east in the direction of Wageningen. What
we did not know was that the niche also spreads to the north in the direction
of Zwolle. The highways to Amsterdam Airport (Schiphol) are probably the most
important.

In
the case of Germany, when we first analyzed the data at the level of the
"Laender" (Federal States), we could see the East-West divide still prevailing,
but when we repeated the analysis at the lower level of the "Regierungsbezirke"
we no longer found the East-West divide as dominant (using 2004 data). So, the
environment of Dresden for example was more synergetic in Triple-Helix terms
than that of Saarbruecken. And this was nice to see considering my idea that
the knowledge-based economy increasingly prevails since the fall of the Berlin
Wall and the demise of the Soviet Union. The discussion about two different
models for organizing the political economy—communism or liberal democracy—had
become obsolete after 1990.

After
studying Germany, I worked with Balázs Lengyel on Hungarian data. Originally,
we could not find any regularity in the Hungarian data, but then the idea arose
to analyze the Hungarian data as three different innovation systems: one around
Budapest, which is a metropolitan innovation system; one in the west of the
country, which has been incorporated into Western Europe; and one in the east
of the country, which has remained the old innovation system that is state-led
and dependent on subsidies. For the western part, one could say that Hungary
has been "europeanized" by Austria and Germany; it has become part of a
European system.

When
Hungary came into the position to create a national
innovation system, free from Russia and the Comecon, it was too late, as
Europeanization had already stepped in and national boundaries were no longer
as dominant. Accordingly, and this was a very nice result, assessing this
synergy indicator on Hungary as a nation, we did not find additional synergy at
the national (that is, above-regional) level. While we clearly found synergy at
the national level for the Netherlands and also found it in Germany, but at the
level of the Federal States, we could not find synergy at a national level for
Hungary. Hungary has probably developed too late to develop a nationally
controlled system of innovations.

A
similar phenomenon appeared when we studied Norway: my Norwegian colleague
(Øivind Strand) did most of our analysis there. To our surprise, the
knowledge-based economy was not generated where the universities are located (Oslo
and Trondheim), but on the West Coast, where the off-shore, marine and maritime
industries are most dominant. FDI (foreign direct investment) in the marine and
maritime industries leads to knowledge-based synergy in the regions on the West
Shore of Norway. Norway is still a national system, but the Norwegian
universities like Trondheim or Oslo are not so much involved in entrepreneurial
networks. These are traditional universities, which tend to keep their hands
off the economy.

Actually,
when we had discussions about these two cases, Norway and Hungary, which both
show that internationalization had become a major factor, either in the form of
Europeanization in the Hungarian case, or in the form of foreign-driven
investments (off-shore industry and oil companies) in the Norwegian case, I
became uncertain and asked myself whether we did not believe too much in our
indicators? Therefore, I proposed to Øivind to study Sweden, given the availability
of well-organized data of this national system.

We
expected to find synergy concentrated in the three regional systems of
Stockholm, Gothenburg, and Malmö/Lund. Indeed, 48.5 percent of the Swedish
synergy is created in these three regions. This is more than one would expect
on the basis of the literature. Some colleagues were upset, because they had
already started trying to work on new developments of the Triple Helix, for
example, in Linköping. But the Swedish economy is organized and centralized in
this geographical dimension. Perhaps that is why one talks so much about
"regionalization" in policy documents. Sweden is very much a national
innovation system, with additional synergy between the regions.

Can governments alter
historical trajectories of national, regional or local innovation systems?

Let
me mention the empirical results for China in order to illustrate the
implications of empirical conclusions for policy options. We had no Chinese
data set, but we obtained access to the database Orbis of the Bureau van Dijk
(an international company, which is Wall Street oriented, assembling data about
companies) that contains industry indicators such as names, addresses,
NACE-codes, types of technology, the sizes of each enterprise, etc. However,
this data can be very incomplete. Using this incomplete data for China, we said
that we were just going to show how one could do the analysis if one had full data. We guess that the
National Bureau of Statistics of China has complete data.
I did the analysis with Ping Zhou, Professor at Zhejiang University.

We
analyzed China first at the provincial level, and as expected, the East Coast
emerged as much more knowledge intense than the rest of the country. After
that, we also looked at the next-lower level of the 339 prefectures of China.
From this analysis, four of them popped up as far more synergetic than the
others. These four municipalities were: Beijing, Shanghai, Tianjin, and
Chongqing.

These
four municipalities became clearly visible as an order of magnitude more
synergetic than other regions. The special characteristic about them is that –as
against the others – these four municipalities are administered by the central
government. Actually, it came out of my data and I did not understand it; but
my Chinese colleague said that this result was very nice and specified this
relationship.

The
Chinese case thus illustrates that government control can make a difference. It
shows – and that is not surprising, as China runs on a different model – that
the government is able to organize the four municipalities in such a way as to
increase synergy. Of course, I do not know what is happening on the ground. We
know that the Chinese system is more complex than these three dimensions
suggest. I guess the government agencies may wish to consider the option of
extending the success of this development model, to Guangdong for example or to
other parts of China. Isn't it worrisome that all the other and less controlled
districts have not been as successful in generating synergy?

Referring
more generally to innovation policies, I would advise as a heuristics that
political discourse is able to signal a problem, but policy questions do not
enable us to analyze the issues. Regional development, for example, is an issue
in Sweden because the system is very centralized, more than in Norway, for
example. But there is nothing in our data that supports the claim that the
Swedish government is successful in decentralizing the knowledge-based economy
beyond the three metropolitan regions. We may be able to reach conclusions like
these serving as policy advice. One develops policies on the basis of intuitive
assumptions which a researcher is sometimes able to test.

As
noted, one can expect a complex system continuously to produce unintended
consequences, and thus it needs monitoring. The dynamics of the system are
different from the sum of the sub-dynamics because of the interaction effects
and feedback loops. Metaphors such as a Triple Helix, Mode-2, or the Risk
Society can be stimulating for the discourse, but these metaphors tend to develop
their own dynamics of proliferating discourses.

The
Triple Helix, for example, can first be considered as a call for collaboration
in networks of institutions. However, in an ecosystem of bi-lateral and
tri-lateral relations, one has a trade-off between local integration
(collaboration) and global differentiation (competition). The markets and the
sciences develop at the global level, above the level of specific relations. A
principal agent such as government may be locked into a suboptimum. Institutional
reform that frees the other two dynamics (markets and sciences) requires
translation of political legitimation into other codes of communication. Translations
among codes of communication provide the innovation engine.

Is there a connection
between infrastructures and the success of innovation processes?

One
of the conclusions, which pervades throughout all advanced economies, is that
knowledge intensive services (KIS) are
not synergetic locally because they can be disconnected – uncoupled – from the
location. For example, if one offers a knowledge-intensive service in Munich
and receives a phone call from Hamburg, the next step is to take a plane to
Hamburg, or to catch a train inside Germany perhaps. Thus, it does not matter
whether one is located in Munich or Hamburg as knowledge-intensive services
uncouple from the local economy. The main point is proximity to an airport or
train station.

This
is also the case for high-tech knowledge-based manufacturing. But it is
different for medium-tech manufacturing, because in this case the dynamics are
more embedded in the other parts of the economy. If one looks at Russia, the
knowledge-intensive services operate differently from the Western European
model, where the phenomenon of uncoupling takes place. In Russia, KIS
contribute to coupling, as knowledge-intensive services are related to state
apparatuses.

In
the Russian case, the knowledge-based economy is heavily concentrated in Moscow
and St. Petersburg. So, if one aims –as the Russian government proclaims – to create
not "wealth from knowledge" but "knowledge from wealth" – that is, oil revenues
–it might be wise to uncouple the knowledge-intensive services from the state
apparatuses. Of course, this is not easy to do in the Russian model because
traditionally, the center (Moscow) has never done this. Uncoupling
knowledge-intensive services, however, might give them a degree of freedom to
move around, from Tomsk to Minsk or vice
versa, steered by economic forces more than they currently are (via institutions
in Moscow).

Final question. What does path-dependency mean in the context of
innovation dynamics?

In
The Challenge of Scientometrics. The development, measurement, and
self-organization of scientific communications (1995), I used Shannon-type information theory to study
scientometric problems, as this methodology combines both static and dynamic
analyses. Connected to this theory I developed a measurement method for
path-dependency and critical transitions.

In
the case of a radio transmission, for example, you have a sender and a
receiver, and in between you may have an auxiliary station. For instance, the
sender is in New York and the receiver is in Bonn and the auxiliary station is
in Iceland. The signal emerges in New York and travels to Bonn, but it may be possible
to improve the reception by assuming the signal is from Iceland instead of
listening to New York. When Iceland provides a better signal, it is possible to
forget the history of the signal before it arrived in Island. It no longer
matters whether Iceland obtained the signal originally from New York or Boston.
One takes the signal from Iceland and the pre-history of the signal does not
matter anymore for a receiver.

Such
a configuration provides a path-dependency (on Iceland) in
information-theoretical terms, measurable in terms of bits of information. In a
certain sense you get negative bits of information, since the shortest path in
the normal triangle would be from New York to Bonn, and in this case the
shortest path is from New York via Iceland to Bonn. I called this at the time a
critical transition. In a scientific text for instance, a new terminology can
come up and if it overwrites the old terminology to the extent that one does
not have to listen to the old terminology anymore, one has a critical
transition that frees one from the path-dependencies at a previous moment of
time.

Thus,
my example is about radical and knowledge-based changes. As long as one has to
listen to the past, one does not make a critical transition. The knowledge-based
approach is always about creative destruction and about moving ahead,
incorporating possible new options in the future. The hypothesized future
states become more important than the past. The challenge, in my opinion, is to
make the notion of options operational and to bring these ideas into
measurement. The Triple-Helix indicator measures the number of possible options
as additional redundancy. This measurement has the additional advantage that one
becomes sensitive to uncertainty in the prediction.

Loet Leydesdorff is Professor Emeritus at the Amsterdam School of Communications Research (ASCoR) of the University of Amsterdam. He is Honorary Professor of the Science and Technology Policy Research Unit (SPRU) of the University of Sussex, Visiting Professor at the School of Management, Birkbeck, University of London, Visiting Professor of the Institute of Scientific and Technical Information of China (ISTIC) in Beijing, and Guest Professor at Zhejiang University in Hangzhou. He has published extensively in systems theory, social network analysis, scientometrics, and the sociology of innovation (see at http://www.leydesdorff.net/list.htm). With Henry Etzkowitz, he initiated a series of workshops, conferences, and special issues about the Triple Helix of University-Industry-Government Relations. He received the Derek de Solla Price Award for Scientometrics and Informetrics in 2003 and held "The City of Lausanne" Honor Chair at the School of Economics, Université de Lausanne, in 2005. In 2007, he was Vice-President of the 8th International Conference on Computing Anticipatory Systems (CASYS'07, Liège). In 2014, he was listed as a highly-cited author by Thomson Reuters.

Literature and Related
links:

Science & Technology Dynamics,
University of Amsterdam / Amsterdam School of Communications
Research (ASCoR)

Leydesdorff,
L. (2006). The Knowledge-Based Economy: Modeled, Measured, Simulated. Universal Publishers, Boca Raton, FL.

Leydesdorff, L. (2001). A Sociological Theory of Communication: The Self-Organization of the
Knowledge-Based Society. Universal Publishers, Boca Raton, FL.

Leydesdorff,
L. (1995). The Challenge of Scientometrics . The development, measurement, and
self-organization of scientific communications. Leiden, DSWO Press, Leiden University.

http://www.leydesdorff.net/

Print version of this Talk (pdf)

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