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Enhancing University–Industry collaboration: the role of intermediary organizations

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Abstract

We evaluate the role of intermediary organizations in fostering University–Industry (U–I) joint R&D by examining the characteristics of firms that interact with universities via these organizations vis-à-vis firms that interact directly with the university’s departments. We find that firms interacting via intermediary organizations are smaller, with less knowledge capabilities and geographically closer to the university, than counterparts. Thereby, our findings provide support to the view that intermediaries contribute to a broader diffusion of knowledge by enhancing U–I links with small firms. Cultural and organizational barriers are more significant among firms interacting directly with the university, whereas cognitive and cost barriers are more relevant among firms interacting via intermediaries. Geographic proximity has a preponderant role in U–I links highlighting the importance of mid-tier universities to regional growth in less technologically advanced regions.

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Notes

  1. University of Minho was founded in 1973 and is a research-based higher schooling institution. It has more than 19,000 FTE students, out of which 33% are master and PhD students and 13% are international students. According to the Times Higher Education ranking, University of Minho is ranked 151-200th in the Young University Rankings 2019 and 83rd in the University Impact Ranking 2019. In the Shangai ranking it has an Institutional Ranking of 401–500.

  2. SCIE is a firm-level database, provided by the Portuguese National Statistics Institute, containing about 350,000 firms and 278 economic and financial variables. ‘Quadros de Pessoal’ is a matched employer–employee dataset gathered by the Portuguese Ministry of Employment, which is based on a questionnaire that every firm is legally obliged to complete. Each year, around three million workers and more than 200,000 firms are covered. The data are available since 1986 and it include data regarding human capital education, occupation and firm’s location. In our analysis we use data for the period 2009–2015, amounting to 2,166,574 observations.

  3. In this specific analysis, due to limitations in our database, it is not possible to include in the model characteristics associated with choice $$j$$.

  4. We only show factors with loadings greater than 0.5 and with an eigenvalue greater than 1.0.

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Funding

Funding was provided by Fundação para a Ciência e a Tecnologia (Grant No. UID/ECO/03182/2019).

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Authors and Affiliations

  1. NIPE and School of Economics and Management, University of Minho, Campus de Gualtar, 4710 - 057, Braga, Portugal

    Fernando Alexandre, Hélder Costa, Ana Paula Faria & Miguel Portela

  2. CIPES and IZA, Bonn, Germany

    Miguel Portela

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  1. Fernando Alexandre
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  2. Hélder Costa
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  3. Ana Paula Faria
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  4. Miguel Portela
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Correspondence to Ana Paula Faria.

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Appendices

Appendix 1

See Table 7.

Table 7 Empirical variables of the probit and multinomial probit models
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Appendix 2

See Table 8.

Table 8 Summary statistics of the principal components variables, N = 41
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Alexandre, F., Costa, H., Faria, A.P. et al. Enhancing University–Industry collaboration: the role of intermediary organizations. J Technol Transf 47, 1584–1611 (2022). https://doi.org/10.1007/s10961-021-09889-8

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