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Why do academics engage with industry? The entrepreneurial university and individual motivations

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Abstract

The debate on the entrepreneurial university has raised questions about what motivates academic scientists to engage with industry. This paper provides evidence based on survey data for a large sample of UK investigators in the physical and engineering sciences. The results suggest that most academics engage with industry to further their research rather than to commercialize their knowledge. However, there are differences in terms of the channels of engagement. Patenting and spin-off company formation are motivated exclusively by commercialization whilst joint research, contract research and consulting are strongly informed by research-related motives. We conclude that policy should refrain from overly focusing on monetary incentives for industry engagement and consider a broader range of incentives for promoting interaction between academia and industry.

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Notes

  1. According to data from the UK 2001 Research Assessment Exercise (RAE).

  2. Response rates (number of valid returned questionnaires relative to population surveyed) by discipline: Chemical Engineering, 35.6%; Chemistry, 35.9%; Civil Engineering, 35.5%; Computer Science, 30.2%; Electrical & Electronic Engineering, 34.7%; General Engineering, 39.7%; Mathematics, 38.4%; Mechanical, Aeronautical & Manufacturing Engineering, 36.9%; Metallurgy & Materials, 34.2%; and Physics, 32.7%.

  3. See D’Este and Patel (2007) for a detailed description.

  4. However, for patents, respondents were requested to report the actual number of patent applications.

  5. Both variables log transformed.

  6. Data on department finances and staff numbers are from www.hesa.ac.uk. Variables for industry and public research funding, and number of staff, were computed at department level as averages for the academic years 1998–1999 and 2000–2001. Public research funding refers to funding for research from any of the UK research councils. Finance data are in £’000. All variables log transformed.

  7. The choice of these three categories (below 5, 5 and 5*) is based on the fact that the reference category accounts for a large proportion of departments: three categories produces a more even distribution of departments. Information on UK RAE 2001 is from: www.hero.ac.uk.

  8. The 5 variables related to barriers are dichotomous variables which take the value 1 if the respondent assessed the barriers as very, or extremely important. The 5 barriers are: absence of established procedures to collaborate with industry; nature of my research not aligned with industry interests or needs; potential conflicts with industry regarding royalty payments from patents or other IP rights; short term orientation of industry research; and rules and regulations imposed by university or government funding agency. The results of the first-stage logistic regressions are available on request.

  9. The two items where there were significant differences across engineering fields are: ‘feedback from industry’ and ‘access to equipment’.

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Acknowledgments

We thank Virginia Acha, Thomas Astebro, Charles Baden-Fuller, Kate Bishop, Isabel Bodas de Araújo Freitas, Maryann Feldman, Roberto Fontana, Patrick Llerena, Ammon Salter, Naohiro Shichijo, Valentina Tartari, Finn Valentin, Jaider Vega-Jurado, John Walsh, Kathryn Walsh for helpful comments. The usual disclaimer applies. Previous versions of the paper were presented at the Triple Helix Conference (16–18 May 2007, Singapore), the AIM workshop ‘Exploring & Mapping University-Industry Relationships’ (21 May 2007, London) and the DIME plenary session at the DRUID Summer Conference (17–19 June 2009, Copenhagen). The authors acknowledge support from the Innovation and Productivity Grand Challenge (IPGC), an initiative of the Advanced Institute of Management Research (AIM) funded by the UK’s Engineering and Physical Sciences Research Council (EP/C534239/1). Markus Perkmann acknowledges funding from the Economic and Social Research Council via an AIM Practices Fellowship (RES-331-27-0063).

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  1. INGENIO, CSIC-UPV, Spanish Council for Scientific Research, Polytechnic University of Valencia, Ciudad Politecnica de la Innovacion, Edif 8E 4, Camino de Vera s/n, 46022, Valencia, Spain

    Pablo D’Este

  2. Business School, Imperial College London, South Kensington Campus, London, SW7 2AZ, UK

    Markus Perkmann

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  1. Pablo D’Este
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Appendix

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See Tables 4, 5, 6, and 7.

Table 4 Descriptive statistics for dependent variables
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Table 5 Descriptive statistics and correlation matrix for explanatory and control variables
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Table 6 Factor analysis results: incentives for interacting with industry
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Table 7 Relationship between frequency of interaction and motivations
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D’Este, P., Perkmann, M. Why do academics engage with industry? The entrepreneurial university and individual motivations. J Technol Transf 36, 316–339 (2011). https://doi.org/10.1007/s10961-010-9153-z

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