Triple Helix models of innovation: are synergies generated at national or regional levels?

Professor Emeritus
University of Amsterdam
Amsterdam School of Communication Research (ASCoR),
1001 NG Amsterdam,
The Netherlands
*email address protected*

The Triple Helix of University-Industry-Government Relations was formulated as an institutional model by Etkzowitz and Leydesdorff (1995 and 2000); but one objective has been to indicate the possible synergies between knowledge production, wealth generation, and political control in systems of innovation. The Triple-Helix indicator measures this synergy as redundancy generated among the distributions of relations (Leydesdorff, Park, and Lengyel, 2014). Redundancy is a measure of the difference between the total number of options in an innovation system (N) and the number of already-realized options (Leydesdorff and Ivanova, 2014). The options available to an innovation system may be as decisive for its survival as the historically already-realized innovations (“best practices”).

In an evolving system, realizations can be considered as historical variation. This variation tends to be organized along trajectories; but options for innovation are determined by the technological regime. The regime results from interactions among selection mechanisms. As a consequence, the total number of possible options [Hmax = ln(N)] typically increases much more rapidly than
the number of (historical) realizations (Hobserved). The difference can be measured as redundancy R (= Hmax – Hobserved) in bits of
information. A system without sufficient redundancy can be considered as locked-in, whereas increased redundancy – reduction of uncertainty – can be expected to improve the “climate” for innovation.

In the (neo-)Schupeterian model, two selection mechanisms were specified and represented orthogonally as shifts along the production function (factor substitution) and shifts of the production function towards the origin (technological development). In the case of three selection mechanisms, the interactions can not only make the environment hyper-selective, but also generate new options for innovation. The interaction between technological environments and market environments, for example, can be stimulated or inhibited by institutional arrangements as a third selection environment (Freeman and Perez, 1988). Interactions among three (or more) selection environments allow for non-linear feedback and feed-forward loops (Ulanowicz, 2009). Whether the additionally generated options can reflexively be perceived by inventors and used by entrepreneurs remains a question. Model-based insights can provide strategic information; but remain based on assumptions.

When the loop between the three dimensions is virtuous, the system flourishes in terms of opportunities for innovation; in the other direction, new developments can get stuck because of augmented selection pressure. Empirically, however, a configuration is a mixture of forward and backward loops: both realizations and new options are generated, but to different extents, at different places, in different markets, etc. The TH indicator measures whether uncertainty increases or decreases at the systems level and to which extent. Because this is an entropy measure, the results can be decomposed. For example, one can ask how much the national level adds to the sum total of synergy (negative entropy) generated at the level of regions. Or in terms of sectors: which sectors add more to synergy in the knowledge base: medium or high-tech? For example, knowledge-intensive services are not bound to their geographical location, but need an airport or train station nearby. Thus, they tend to uncouple from the synergy created in a region. From a local perspective, embeddedness can be expected to counteract on the footloose- ness of high-tech firms and knowledge-intensive services.

Using firms as units of analysis in a series of studies, we decomposed a number of national systems of innovation: Germany (Leydesdorff and Fritsch, 2006), the Netherlands (Leydesdorff, Dolfsma and van der Panne, 2006), Sweden (Leydesdorff and Strand, 2014), Norway (Strand and Leydesdorff, 2014), Italy (Cucco and Leydesdorff, manuscript), Hungary (Lengyel and Leydesdorff, 2011), the Russian Federation (Leydesdorff, Perevodchikov and Uvarov, 2015), and China (Leydesdorff and Zhou, 2014). In the case of the Netherlands, Sweden, and China, the national level adds to the sum of the regions. In Sweden, the knowledge-based economy is heavily focused in three regions (Stockholm, Gothenburg, and Malmö/Lund); in China, four municipalities which are administered at the national level participate in the knowledge-based economy more than comparable regions. In Norway, foreign-driven investment along the west-coast seems to drive the transition from a political to a knowledge-based economy. Hungary’s western part is transformed by the integration into the European Union, whereas the eastern part has remained a state-led innovation system. The capital Budapest has a separate position. In Germany, the generation of synergy is mainly at the level of the States (Länder) and not at the national level. In Italy, the main division is between the northern and southern parts of the country, and less so among regions as primarily administrative units. In the Russian Federation, the national level tends to disorganize synergy development at lower levels. The knowledge-intensive services cannot circulate because of their integration in the state apparatuses.

Using other units of analysis (eg. scientific publications) and other attributes (eg. institutional addresses or medical subject headings, one can generalize the TH model and indicator to a Triple Helix of supply, demand, and technological capacities. Using address information, a geographical decomposition remains always possible, but one does not expect an innovation dynamic to be geographically constrained. Pockets of negative entropy production touch the ground at some places more than others (Bathelt, 2003), like clouds which are part of frontal weather systems and non-linear turbulences. Agents and policy makers compete in terms of their reflexive capacities to model the opportunities offered by sets of TH relations (Leydesdorff, 2010).


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Etzkowitz, H and Leydesdorff, L. (1995) The Triple Helix-University -Industry-Government Relations: a Laboratory for Knowledge -Based Economic Development. EASST Review 14(1), 14-19.
Etzkowitz, H and 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.
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Leydesdorff, L. (2010) Communicative Competencies and the Structuration of Expectations: the Creative Tension between Habermas’ Critical Theory and Luhmann’s Social Systems Theory. Complicity: an International Journal of Complexity and Education, 7(2), 66-76; available at http://
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Leydesdorff, L and Fritsch, M. (2006). Measuring the Knowledge Base of Regional Innovation Systems in Germany in terms of a Triple Helix Dynamics. Research Policy, 35(10), 1538-1553.

Leydesdorff, L. and Ivanova, I A. (2014) Mutual Redundancies in Inter-human Communication Systems: Steps Towards a Calculus of Processing Meaning. Journal of the Association for Information Science and Technology, 65(2), 386-399.
Leydesdorff, L, Park, H W and Lengyel, B. (2014) A Routine for Measuring Synergy in University-Industry-Government Relations: Mutual Information as a Triple-Helix and Quadruple-Helix Indicator. Scientometrics, 99(1), 27-35. doi: 10. 1007/s11192-013-1079-4.
Leydesdorff, L., Perevodchikov, E and Uvarov, A. (2015) Measuring triple-helix synergy in the Russian innovation systems at regional, provincial, and national levels. Journal of the Association for Information Science and Technology, 66(6), 1229- 1238.
Leydesdorff, L. and Strand, Ø. (2013) The Swedish System of Innovation: Regional Synergies in a Knowledge-Based Economy. Journal of the American Society for Information Science and Technology, 64(9), 1890-1902; doi: 1810.1002/ asi.22895.
Leydesdorff, L. and Zhou, P. (2014) Measuring the Knowledge- Based Economy of China in terms of Synergy among Technological, Organizational, and Geographic Attributes of Firms. Scientometrics, 98(3), 1703-1719. doi: 10.1007/s11192- 013-1179-1.
Strand, Ø and Leydesdorff, L. (2013) Where is Synergy in the Norwegian Innovation System Indicated? Triple Helix Relations among Technology, Organization, and Geography. Technological Forecasting and Social Change, 80(3), 471-484.
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Loet Leydesdorff (PhD Sociology, MA Philosophy, and MSc. Biochemistry) is Professor at the Amsterdam School of Communications Research (ASCoR) of the University of Amsterdam. He is an Associate Faculty at the Science and Technology Policy Research Unit (SPRU) of the University of Sussex; Visiting Professor of the Institute of Scientific and Technical Information of China (ISTIC) in Beijing; Guest Professor at Zhejiang University in Hangzhou; and Visiting Professor at the School of Management, Birkbeck, University of London. He has published extensively in systems theory, social network analysis, scientometrics, and the sociology of innovation (see at ). 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 (C AS YS’07, Liège). Since 2014, Thomson Reuters lists him as a highly-cited author (http:// highlycited. com).