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Technology transfer with search intensity and project advertising

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Abstract

We present a model where technology transfer (TT) is embedded into a non cooperative model of utility and profit maximization and is the result of an endogenous matching process between technology transfer offices and innovative firms. We show that TT strictly depends on the costs of searching researchers and firm advertising for vacant projects. In this scenario, technology progress might be excessively low if technology transfer offices search for project matches too much intensively. The result occurs because both sides of the market ignore the externalities of their decisions. Complementarity or substitutability between the tightness in the TT market and the technology stock are both potential equilibrium outcomes.

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Notes

  1. The other countries selected are: France, Germany, Japan, and United Kingdom. See World Economic Forum (2015) for a detailed description of the scores and ranking reported for each country. Calcagnini et al. (2015) discuss about the role of TT as one of the deep determinants of the TFP growth of European countries.

  2. Theoretical models of TT often referred either to the triple helix approach or to the linear model of innovation. The former uses an evolutionary framework to study the relationship among universities, industry and government (Leydesdorff 2000). The latter postulates that innovation starts with basic research, followed by applied research and development, and it eventually ends with production and diffusion (Godin 2006). However, this traditional view recently changed in favor of a microfoundation approach of the relationship among the three main actors of innovation. Barney and Felin (2013) focus on the matching between individuals and organizations, while Cunningham et al. (2015) study the factors that induce researchers to become a principal investigator. They labelled the influencing factors as push (project dependencies and institutional pressures) and pull (control, career ambition and advancement) factors. Further, recent empirical microfounded TT models give a great attention to the analysis of the aggregation, capability development, joint production motivation and value creation (Barney and Felin 2013; Foss and Lindenberg 2013; Winter 2013).

  3. In more general settings, other TTO’s functions are to help researchers to license inventions or to start spin off companies.

  4. We label as TTP any possible way through which the knowledge created in universities might be transferred to firms. In general, the prevailing way is through licensing agreements; the other way involves the creation of spin-offs. See Macho-Stadler and Prez-Castrillo (2010) for a discussion.

  5. A complete “Mathematical Appendix” is available upon request from the authors.

  6. We assume that even there is heterogeneity among TTOs, researchers and firms, an average behavior is prevailing in the non cooperative Nash equilibrium. See Sect. 2 of the “Mathematical Appendix”.

  7. In a principal-agent setting, Jensen and Thursby (2001) assume that TTO’s objective is to maximize a weighted average of the expected utilities of the university (administration) and researcher (inventor).

  8. Following the increasing recognition of a ’third mission’, most TTOs receive public financial resources from their parent university. TTOs can also draw upon a mix of funds resulting from their activities, namely a share of the capital gains on firms’ equity participation, a share of net royalties on licensed technology, and an overhead on collaborative research agreements (OECD 2011).

  9. The matching function exhibits constant returns-to-scale, as showed by Pissarides (1986) and Blanchard and Diamond (1989).

  10. Bozeman (2000) recognizes the active role of governments and universities in technology development and transfer. Governments act as producers of research, supplying applied research and technology to industry, or as brokers who design policies for industrial technology development and innovation. Thus, public policies are crucial to fostering U–I cooperation. Recently, Muscio et al. (2016) identify three classes of institutionally-defined rules that can motivate faculty members to establish a spinoff company. These are: general rules and procedures; rules regulating monetary incentives; rules related to the entrepreneurial risk.

  11. See Sect. 3 of the “Mathematical Appendix”.

  12. See Sects. 4 and 5 of the “Mathematical Appendix”.

  13. As for other model parameters, we assume an average bargaining power parameter \(\beta \) common to all TTOs. Of course, characteristics of TTOs vary significantly both within and across institutional and national contexts. Thus, different bargaining power might characterize the distribution of surplus between firms and TTOs in each matching.

  14. In Italy \(\beta \) is approximatively equal to 10%, i.e.: the 10% of the surplus generated by a TT project is gained by the university.

  15. See Kochenkova et al. (2015) for a systematic review of academic studies on public policy measures in support of technology transfer.

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Authors and Affiliations

  1. Department of Economics, Society and Politics, Università di Urbino Carlo Bo, Via Saffi 42, 61029, Urbino, Italy

    Giorgio Calcagnini, Germana Giombini & Giuseppe Travaglini

  2. Mo.Fi.R, Ancona, Italy

    Giorgio Calcagnini & Germana Giombini

  3. Department of Economics, Università degli Studi di Roma Tre, Via Silvio D’Amico 77, 00145, Rome, Italy

    Paolo Liberati

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  1. Giorgio Calcagnini
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  2. Germana Giombini
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  4. Giuseppe Travaglini
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Correspondence to Germana Giombini.

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Calcagnini, G., Giombini, G., Liberati, P. et al. Technology transfer with search intensity and project advertising. J Technol Transf 44, 1529–1546 (2019). https://doi.org/10.1007/s10961-018-9667-3

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