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University-industry research collaboration: a model to assess university capability

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Abstract

Scholars and policy makers recognize that collaboration between industry and the public research institutions is a necessity for innovation and national economic development. This work presents an econometric model which expresses the university capability for collaboration with industry as a function of size, location and research quality. The field of observation is made of the census of 2001–2003 scientific articles in the hard sciences, co-authored by universities and private enterprises located in Italy. The analysis shows that research quality of universities has an impact higher than geographic distance on the capability for collaborating with industry. The model proposed and the measures that descend from it are suited for use at various levels of administration, to assist in realizing the “third role” of universities: the contribution to socio-economic development through public to private technology transfer.

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Notes

  1. Gallino describes how flourishing high-tech sectors of the Italian industry, such as the chemical, computer, airplane, electronic industries either disappeared or irremediably shrunk in the past 40 years.

  2. The term “social proximity” is used in the sense given by Boschma (2005): “Social proximity is defined here in terms of embedded relations between agents at the micro-level. Relations between actors are socially embedded when they involve trust based on friendship, kinship and experience”.

  3. This model was selected because the dependent variable is a count variable.

  4. After the introduction of “academic privilege” in 2001, university researchers are probably filing a higher number of patents, however identification and collection of relevant data is extremely difficult.

  5. Enterprises may have simply funded the academic research underlying co-filed patents.

  6. The complete list is available at http://www.miur.it/atti/2000/alladm001004_01.htm.

  7. The number of university-enterprise pairs is less than the number of university-enterprise collaborations because, in the period considered, some pairs collaborated more than once.

  8. Each SDS can be considered as a community of university researchers with a common set of competencies even if each SDS includes multiple and probably non-quantifiable specializations that continuously evolve. The authors maintain that the approximation introduced by considering the SDS as a unique set of competencies shared by the participating researchers permits the understanding of certain aspects of university-industry collaboration.

  9. Expressed as the straight-line surface distance between university and the capital of each administrative region.

  10. To account for the variability in IF seen in the journals of the different WoS subject categories, the IF value of each journal is expressed as the percentile ranking of all the journals in the same discipline. For example, a value of 90 indicates that 90% of the journals falling in the same discipline have lower impact factor than the one under consideration. The authors are aware of the limitations of this use of the Impact Factor as proxy for the quality of a publication (Moed and Van Leeuwen 1996; Weingart 2005), but believe that the scope of the present study justifies its use in absence of data on the number of citations received by each article.

  11. For details see Abramo et al. 2009c.

  12. Such an analysis would also have significant costs in time and funds.

  13. Regression coefficients were interpreted as the difference between the log of expected counts, where formally, this can be written as \( \beta = \log \left( {\mu_{{x_{0} + 1}} } \right) - \log (\mu_{{x_{0} }} ) \), where β is the regression coefficient, μ is the expected count and the subscripts represent the predictor variable evaluated at x 0 and x 0 + 1 (implying a one unit change in the predictor variable x). The difference of two logs is equal to the log of their quotient, \( \log (\mu_{{x_{0} + 1}} ) - \log (\mu_{{x_{0} }} ) = \log (\mu_{{x_{0} + 1}} /\mu_{{x_{0} }} ) \), and therefore, we could have also interpreted the parameter estimate as the log of the ratio of expected counts. This explains the “ratio” in terms of incidence rate ratios (http://www.ats.ucla.edu/stat/stata/output/stata_nbreg_output.htm).

  14. The likelihood-ratio chi-square test indicates that the dispersion parameter alpha is equal to zero (H 0: α = 0; no-overdispersion). The rejection of the null hypothesis would suggest that the dependent variable is over-dispersed and is not sufficiently described by the simpler Poisson distribution.

  15. For example, the internal medicine disciplinary sector would certainly have different practices than the electronics or biochemistry SDS (these are the three SDSs showing highest frequency of collaboration with enterprises).

  16. A possible explanation is that private enterprises in pharmacology tend to draw heavily on networks of personal contacts in the choice of university partners for collaboration. Social proximity is indeed one of the primary determinants of choice in a research partnership but we have argued that the variables in the models embed some of the proximity considerations involved.

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Authors and Affiliations

  1. Laboratory for Studies of Research and Technology Transfer, School of Engineering, Department of Management, University of Rome “Tor Vergata”, Via del Politecnico 1, 00133, Rome, Italy

    Giovanni Abramo, Ciriaco Andrea D’Angelo & Flavia Di Costa

  2. National Research Council of Italy, Rome, Italy

    Giovanni Abramo

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  1. Giovanni Abramo
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  2. Ciriaco Andrea D’Angelo
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  3. Flavia Di Costa
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Correspondence to Giovanni Abramo.

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Abramo, G., D’Angelo, C.A. & Di Costa, F. University-industry research collaboration: a model to assess university capability. High Educ 62, 163–181 (2011). https://doi.org/10.1007/s10734-010-9372-0

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