Abstract
The aim of this paper is to empirically examine the influence of credibility on the likelihood to grant consortia of collaborating actors an innovation subsidy. Theorizing from the viewpoint of resource dependence theory and the sociology of expectations, we hypothesize that four types of credibility of influence: scientific credibility, market credibility, expectation track record, and generated social capital. We operate on two levels of analysis, the actor and the consortium. We quantitatively analyze the Dutch electric vehicle subsidy program as case. We develop a model that accurately forecasts which consortia are most likely to receive subsidies. We demonstrate that generated social capital and market credibility positively influence the likelihood of receiving innovation subsidies, while scientific credibility sources and expectation track record have a negative influence. Based on these findings we provide policy recommendations and avenues for further research.
Similar content being viewed by others
Notes
In this paper we use the word ‘actor’ as it is used in innovation system literature. By actor we mean an institutional party in the innovation system that plays a role in developing new innovations, we do not refer to individuals. Subsidized consortia consist of multiple actors, collaborating to develop innovations.
Rao et al. (2008) define these sources in terms of legitimacy. To avoid confusion with our dependent variable we refer to these sources as credibility. In our opinion the term credibility also better fits the relationship studied here. Further, Rao et al. also distinguish historical and locational legitimacy. However, since the EVT field is new, historical legitimacy is of less importance. It is partially captured by actor age, but older actors are usually from outside the EVT field. Locational legitimacy was considered as possible concept, but the geographic distances in the Netherlands are relatively small and there are no obvious large EVT clusters yet. Therefore it was not included in the model.
Unfortunately the data does not provide rankings on the criteria.
A value of 1 was added to all observations in order to be able to calculate the natural logarithm for cases with value 0. After the transformation these values were 0 again, since ln(1) = 0.
Other proxies were also tested, such as the sum, the maximum or the natural logarithms of the number of articles published. However, the average number gave the best model results for all types of credibility.
We tested extensively for interaction effects between type and credibility at the actor level, but this yielded no significant results.
Appendices can be found online, these contain technology specific details, but are of no further concern for this study.
References
Adler, P. S., & Kwon, S.-W. (2002). Social capital: Prospects for a new concept. The Academy of Management Review, 27, 17–40.
Aguillo, I. F., Bar-Ilan, J., Levene, M., & Ortega, J. L. (2010). Comparing university rankings. Scientometrics, 85, 243–256.
Ahuja, G. (2000). Collaboration networks, structural holes, and innovation: A longitudinal study. Administrative Science Quarterly, 45, 425–455.
Alkemade, F., & Suurs, R. A. A. (2012). Patterns of expectations for emerging sustainable technologies. Technological Forecasting and Social Change, 79, 448–456.
Anderson, R. C., Narin, F., & McAllister, P. (1978). Publication ratings versus peer ratings of universities. Journal of the American Society for Information Science, 29, 91–103.
Bakker, S. (2010). The car industry and the blow-out of the hydrogen hype. Energy Policy, 38, 6540–6544.
Bakker, S., & Trip, J. J. (2013). Policy options to support the adoption of electric vehicles in the urban environment. Transportation Research Part D: Transport and Environment, 25, 18–23.
Bakker, S., Van Lente, H., & Meeus, M. T. H. (2011). Arenas of expectations for hydrogen technologies. Technological Forecasting and Social Change, 78, 152–162.
Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17, 99–120.
Baruch, Y., & Hall, D. T. (2004). The academic career: A model for future careers in other sectors? Journal of Vocational Behavior, 64, 241–262.
Bates, D. M., & Sarkar, D. (2006). The lme4 library. http://lib.stat.cmu.edu/R/CRAN.
Berkhout, F. (2006). Normative expectations in systems innovation. Technology Analysis and Strategic Management, 18, 299–311.
Bornmann, L., Leydesdorff, L., & Van den Besselaar, P. (2010). A meta-evaluation of scientific research proposals: Different ways of comparing rejected to awarded applications. Journal of Informetrics, 4, 211–220.
Borup, M., Brown, N., Konrad, K., & Van Lente, H. (2006). The sociology of expectations in science and technology. Technology Analysis and Strategic Management, 18, 285–298.
Bozeman, B., Fay, D., & Slade, C. (2013). Research collaboration in universities and academic entrepreneurship: The-state-of-the-art. The Journal of Technology Transfer, 38, 1–67.
Brown, N., & Michael, M. (2003). A sociology of expectations: Retrospecting prospects and prospecting retrospects. Technology Analysis and Strategic Management, 15, 3–18.
Burt, R. S. (1999). The social capital of opinion leaders. Annals of the American Academy of Political and Social Science, 566, 37–54.
Butts, C. T. (2008). Social network analysis with sna. Journal of Statistical Software, 24, 1–51.
Butts, C. T. (2012). Tools for social network analysis. R package version 2. http://stat.ethz.ch/CRAN/web/packages/sna.
Carayol, N. (2003). Objectives, agreements and matching in science-industry collaborations: Reassembling the pieces of the puzzle. Research Policy, 32, 887–908.
Carlsson, B., & Jacobsson, S. (1997). Diversity creation and technological systems: A technology policy perspective. In C. Edquist (Ed.), Systems of innovation: Technologies, institutions and organizations. London: Pinter Publishers.
Carlsson, B., & Stankiewicz, R. (1991). On the nature, function and composition of technological systems. Journal of Evolutionary Economics, 1, 93–118.
Chaminade, C., & Edquist, C. (2010). Rationales for public policy intervention in the innovation process: A systems of innovation approach. In R. Smits, S. Kuhlmann, & P. Shapira (Eds.), The theory and practice of innovation policy: An international research handbook. Cheltenham: Edward Elgar Publishers.
Chandy, R. K., & Tellis, G. J. (2000). The incumbent’s curse? Incumbency, size, and radical product innovation. The Journal of Marketing, 64, 1–17.
Cohen, W. M., & Levinthal, D. A. (1990). Absorptive capacity: A new perspective on learning and innovation. Administrative Science Quarterly, 35, 128–152.
Coleman, J. S. (1988). Social capital in the creation of human capital. American Journal of Sociology, 94, S95–S120.
Commission, European. (2003). COMMISSION RECOMMENDATION of 6 May 2003 concerning the definition of micro, small and medium-sized enterprises. Official Journal of the European Union, 124, 36–41.
Danneels, E. (2002). The dynamics of product innovation and firm competences. Strategic Management Journal, 23, 1095–1121.
Davidsson, P., & Honig, B. (2003). The role of social and human capital among nascent entrepreneurs. Journal of Business Venturing, 18, 301–331.
Deeds, D. L., Mang, P. Y., & Frandsen, M. L. (2004). The influence of firms’ and industries’ legitimacy on the flow of capital into high-technology ventures. Strategic Organization, 2, 9–34.
DiMaggio, P. J., & Powell, W. W. (1983). The Iron cage revisited: Institutional isomorphism and collective rationality in organizational fields. American Sociological Review, 48, 147–160.
Dutch Court of Audit. (2011). Innovatiebeleid (Innovation Policy). The Hague: Algemene Rekenkamer. Retrieved from http://www.rekenkamer.nl/Publicaties/Onderzoeksrapporten/Introducties/2011/09/Innovatiebeleid.
Edquist, C. (1997). Systems of innovation approaches—Their emergence and characteristics. In C. Edquist (Ed.), Systems of innovation. London: Pinter.
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, 109–123.
Ewing, G. O., & Sarigöllü, E. (1998). Car fuel-type choice under travel demand management and economic incentives. Transportation Research Part D: Transport and Environment, 3, 429–444.
Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27, 861–874.
Fombrun, C. (1996). Reputation: Realizing value from the corporate image. Boston: Harvard Business School Press.
Frenken, K., Hekkert, M., & Godfroij, P. (2004). R&D portfolios in environmentally friendly automotive propulsion: Variety, competition and policy implications. Technological Forecasting and Social Change, 71, 485–507.
Gibbons, M., Limoges, C., Nowotny, H., Schawartzman, S., Scott, P., & Trow, M. (1994). The new production of knowledge: The dynamics of science and research in contemporary societies. London: Sage.
Greene, W. H. (1997). Econometic analysis (3rd ed.). Upper Saddle River, NJ: Prentice-Hall.
Greve, A., & Salaff, J. W. (2003). Social networks and entrepreneurship. Entrepreneurship theory and practice, 28, 1–22.
Gulbrandsen, M., & Smeby, J.-C. (2005). Industry funding and university professors’ research performance. Research Policy, 34, 932–950.
Hannan, M. T., & Freeman, J. (1984). Structural inertia and organisational change. American Sociological Review, 49, 149–164.
Hannan, M. T., & Freeman, J. (1989). Organizational ecology. Cambridge, MA: Harvard University Press.
Harman, G. (2001). University-industry research partnerships in Australia: Extent, benefits and risks. Higher Education Research & Development, 20, 245–264.
Hekkert, M. P., Suurs, R. A. A., Negro, S. O., Kuhlmann, S., & Smits, R. E. H. M. (2007). Functions of innovation systems: A new approach for analysing technological change. Technological Forecasting and Social Change, 74, 413–432.
Henderson, R. M. (1993). Underinvestment and incompetence as responses to radical innovation: Evidence from the photolithographic alignment equipment industry. The Rand Journal of Economics, 24, 248–270.
Henderson, R. M., & Clark, K. B. (1990). Architectural innovation—the reconfiguration of existing product technologies and the failure of established firms. Administrative Science Quarterly, 35, 9–30.
Hessels, L. K., & van Lente, H. (2008). Re-thinking new knowledge production: A literature review and a research agenda. Research Policy, 37, 740–760.
Hillman, A. J., Withers, M. C., & Collins, B. J. (2009). Resource dependence theory: A review. Journal of Management, 35, 1404–1427.
Hovland, C. I., & Weiss, W. (1951). The influence of source credibility on communication effectiveness. Public Opinion Quarterly, 15, 635–650.
Howells, J. (2002). The response of old technology incumbents to technological competition—Does the sailing ship effect exist? Journal of Management Studies, 39, 887–906.
Huétink, F. J., der Vooren, A., & van Alkemade, F. (2010). Initial infrastructure development strategies for the transition to sustainable mobility. Technological Forecasting and Social Change, 77, 1270–1281.
Kleinknecht, A., & Verspagen, B. (1990). Demand and innovation: Schmookler re-examined. Research Policy, 19, 387–394.
Kogut, B., & Zander, U. (1992). Knowledge of the firm, combinative capabilities, and the replication of technology. Organization Science, 3, 383–397.
Latour, B., & Woolgar, S. (1979). Laboratory life: The construction of scientific facts. London: Sage.
Laursen, K., & Salter, A. (2006). Open for innovation: The role of openness in explaining innovation performance among UK manufacturing firms. Strategic Management Journal, 27, 131–150.
Lee, S., & Bozeman, B. (2005). The impact of research collaboration on scientific productivity. Social Studies of Science, 35, 673–702.
Lehmann, S., Jackson, A. D., & Lautrup, B. E. (2006). Measures for measures. Nature, 444, 1003–1004.
Lewin, A. Y., Weigelt, C. B., & Emery, J. D. (2004). Adaption and selection in strategy and change: Perspectives on strategic change in organizations. In M. S. Poole & A. H. Van de Ven (Eds.), Handbook of organizational change and innovation. Oxford: Oxford University Press.
Li, T., & Calantone, R. J. (1998). The impact of market knowledge competence on new product advantage: Conceptualization and empirical examination. The Journal of Marketing, 62, 13–29.
Liberman, S., & Wolf, K. B. (1998). Bonding number in scientific disciplines. Social Networks, 20, 239–246.
Lieven, T., Mühlmeier, S., Henkel, S., & Waller, J. F. (2011). Who will buy electric cars? An empirical study in Germany. Transportation Research Part D: Transport and Environment, 16, 236–243.
Lin, Z., Yang, H., & Arya, B. (2009). Alliance partners and firm performance: Resource complementarity and status association. Strategic Management Journal, 30, 921–940.
McFadden, D. (1974). Conditional logit analysis of qualitative choice behavior. In P. Zarembka (Ed.), Frontiers in economics (pp. 105–142). New York: Academic Press.
Meeus, M. T. H., Oerlemans, L. A. G., & Hage, J. (2004). Industry-public knowledge infrastructure interaction: Intra- and Inter-organizational explanations of interactive learning. Industry and Innovation, 11, 327–352.
Merton, R. K. (1968). The Matthew effect in science. Science, 159, 56–63.
Minister of Economic Affairs. (2009). Regeling van de Minister van Economische Zaken van 30 oktober 2009, nr. WJZ/9166533, tot wijziging van de Subsidieregeling sterktes in innovatie. Staatscourant 1–8.
Ministry of Economic Affairs. (2009). Mobiliteitsbeleid (Mobility Policy).
Morrow, K., Karner, D., & Francfort, J. (2008). Plug-in hybrid electric vehicle charging infrastructure review. US Department of Energy-Vehicle Technologies Program.
Mowery, D., & Rosenberg, N. (1979). The influence of market demand upon innovation: A critical review of some recent empirical studies. Research Policy, 8, 102–153.
Nelson, R. R., & Winter, S. G. (1982). An evolutionary theory of economic change. Cambridge, MA: The Belknap of Harvard University Press.
Nieminen, M., Kaukonen, E. (2001). Universities and R&D networking an a knowledge-based economy. Sitra Reports.
Nooteboom, B., & Stam, E. (2008). Micro-foundations of innovation policy. Amsterdam: WRR, Amsterdam University Press.
Penrose, E. T. (1959). The theory of the growth of the firm. White Plains, NY: M. E. Sharpe.
Pfeffer, J., & Salancik, G. (2003). The external control of organizations: A resource dependence perspective. Palo Alto: Stanford Business Books.
Pornpitakpan, C. (2004). The persuasiveness of source credibility: A critical review of five decades’ evidence. Journal of Applied Social Psychology, 34, 243–281.
Powell, W. W., Koput, K. W., & Smith-Doerr, L. (1996). Interorganizational collaboration and the locus of innovation: Networks of learning in biotechnology. Administrative Science Quarterly, 41, 116–145.
R Development Core Team. (2013). R: A language and environment for statistical computing. www.R-project.org.
Rao, R. S., Chandy, R. K., & Prabhu, J. C. (2008). The fruits of legitimacy: Why some new ventures gain more from innovation than others. Journal of Marketing, 72, 58–75.
Renault, C. S. (2006). Academic capitalism and university incentives for faculty entrepreneurship. The Journal of Technology Transfer, 31, 227–239.
Rieh, S. Y., & Danielson, D. R. (2007). Credibility: A multidisciplinary framework. In B. Cronin (Ed.), Annual review of information science and technology (pp. 307–364). Medford, NJ: Information Today.
Rip, A. (1994). The republic of science in the 1990s. Higher Education, 28, 3–23.
Rip, A. (2006). Folk theories of nanotechnologists. Science as Culture, 15, 349–365.
Santoro, M. D., & Chakrabarti, A. K. (2002). Firm size and technology centrality in industry–university interactions. Research Policy, 31, 1163–1180.
Schilling, M. A., & Phelps, C. C. (2007). Interfirm collaboration networks: The impact of large-scale network structure on firm innovation. Management Science, 53, 1113–1126.
Siegel, D. S., Waldman, D. A., Atwater, L. E., & Link, A. N. (2003). Commercial knowledge transfers from universities to firms: Improving the effectiveness of university–industry collaboration. The Journal of High Technology Management Research, 14, 111–133.
Sorenson, O., & Fleming, L. (2004). Science and the diffusion of knowledge. Research Policy, 33, 1615–1634.
Sternthal, B., Ruby, D., & Leavitt, C. (1978). The persuasive effect of source credibility: Tests of cognitive response. Journal of Consumer Research, 4, 252–260.
Toutkoushian, R. K., Porter, S. R., Danielson, C., & Hollis, P. R. (2003). Using publications counts to measure an institution’s research productivity. Research in Higher Education, 44, 121–148.
Van Lente, H. (1993). Promising technology. The dynamics of expectations in technological developments. University of Twente, Enschede.
Van Lente, H., & Bakker, S. (2010). Competing expectations: The case of hydrogen storage technologies. Technology Analysis and Strategic Management, 22, 693–709.
Van Lente, H., & Rip, A. (1998). The rise of membrane technology from Rhetorics to social reality. Social Studies of Science, 28, 221–254.
Van Merkerk, R. O., & Robinson, D. K. R. (2006). Characterizing the emergence of a technological field: Expectations, agendas and networks in Lab-on-a-chip technologies. Technology Analysis and Strategic Management, 18, 411–428.
Van Rijnsoever, F. J., Farla, J., & Dijst, M. J. (2009). Consumer car preferences and information search channels. Transportation Research Part D: Transport and Environment, 14, 334–342.
Van Rijnsoever, F. J., Hagen, P., & Willems, M. (2013). Preferences for alternative fuel vehicles by Dutch local governments. Transportation Research Part D: Transport and Environment, 20, 15–20.
Van Rijnsoever, F. J., Hessels, L. K., & Vandeberg, R. L. J. (2008). A resource-based view on the interactions of university researchers. Research Policy, 37, 1255–1266.
Van Rijnsoever, F. J., Meeus, M. T. H., & Donders, R. T. (2012). The effects of economic status and recent experience on innovative behavior under environmental variability: An experimental approach. Research Policy, 41, 833–847.
Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications. Cambridge: Cambridge University Press.
Zambaldi, F., Aranha, F., Lopes, H., & Politi, R. (2011). Credit granting to small firms: A Brazilian case. Journal of Business Research, 64, 309–315.
Zhang, J. (2010). The problems of using social networks in entrepreneurial resource acquisition. International Small Business Journal, 28, 338–361.
Ziegler, A. (2012). Individual characteristics and stated preferences for alternative energy sources and propulsion technologies in vehicles: A discrete choice analysis for Germany. Transportation Research Part A: Policy and Practice, 46, 1372–1385.
Author information
Authors and Affiliations
Corresponding author
Appendix 1: Literal translation of the criteria for granting subsidies and explanation [see: Staatscourant (Nr. 16803): 1–8]
Appendix 1: Literal translation of the criteria for granting subsidies and explanation [see: Staatscourant (Nr. 16803): 1–8]
1.1 Added definitions
HTAS-EVT-project: An innovation project consisting of experimental development or a combination of experimental development and industrial research that contributes to and fits within the strategic main goals of the HTAS-program as mentioned in Appendix 6.1Footnote 7 and the theme, the specific goals and focus areas as mentioned in Appendix 6.3.
HTAS-EVT-collaboration: a non-legal personality owning a collaboration consisting of two or more, participating members, not in a single group, of which at least one is a SME-entrepreneur and another party is either an entrepreneur or a research organization, executing a HTAS-EVT-project.
1.2 Article 6.25
-
1.
Criteria to grant subsidies
-
(a)
Technological novelty or a substantial novel application of an existing technology
-
(b)
Quality of the collaboration at least evident from the complementarity of the participants, the extent to which SMEs are involved and the novelty of the collaboration.
-
(c)
Sustainable economic perspectives of the project results, extensiveness of the possibilities for application of the project results.
-
(d)
The theme of the program and its specific goals and focus areas.
-
(a)
-
2.
When ranking the proposals all criteria are of equal weight.
1.3 Explanation of 6.25
The minister grants subsidies in according to the ranking of the subsidy proposals. HTAS EVT-projects are judged on four equally important criteria. The novelty of the technology or its applications are central to Part A. This also emphasizes the [62] SMEs in the consortium, possibilities for returns on investments and turnover, distinguishable market trends and the position of competitors in the market play a role. One can also consider the follow-up activities that are required to gain a sustainable perspective. If Criterion C is more often fulfilled this will have a positive impact on employment. The criterion in Part D, relates to the added themes and its specific goals and focus areas described in Appendix 6.3. This is further elaborated upon in Appendix 6.3 under the headings background and theme, HTAS-electric vehicle technology, specific goals, and focus areas.
Rights and permissions
About this article
Cite this article
van Rijnsoever, F.J., Welle, L. & Bakker, S. Credibility and legitimacy in policy-driven innovation networks: resource dependencies and expectations in Dutch electric vehicle subsidies. J Technol Transf 39, 635–661 (2014). https://doi.org/10.1007/s10961-013-9326-7
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10961-013-9326-7
Keywords
- Electric vehicle technology
- Expectations
- Resource dependence theory
- Credibility
- Legitimacy
- Innovation policy