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Do co-publications with industry lead to higher levels of university technology commercialization activity?

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Abstract

Using the university–industry co-publications (UICP) propensity indicators developed by Tijssen (CWTS Working Paper Series, CWTS-WP-2012-009, 2009), this paper examines the impact of university–industry R&D collaboration on university technology commercialization output for leading US and Canadian universities. Our analysis suggests that UICPs do have a significant positive influence on universities’ technology commercialization outputs, after controlling for the quantity and quality of their research and for their commercialization resources. The results are robust for all three common measures of university technology commercialization: patenting (both in terms of simple patent counts and citation-weighted counts), spin-off formation, and technology licensing. To supplement the aggregate regression findings, five case studies are provided that offer further insights on the causal mechanisms involved. Implications of these findings and possible future research directions are discussed.

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Notes

  1. In this and later studies (e.g. Tijssen et al. 2009), Tijssen uses the term ‘intensity’ in reference to the share of co-authored publications relative to total publications. We have replaced this term with ‘propensity’ as it more accurately reflects the meaning of the ratio.

  2. Due to data availability restrictions, the citation-weighted patents were calculated based on patents issued between 2006 and 2008, with citations up to 2009.

  3. The 2005 SCI sub-index also includes publications listed in the Arts & Humanities Citations Index (AHCI). However, we do not believe this change of definition materially changes our results. The correlation between the sub-index for 2005 is highly correlated with the data for 2006 (r = 0.998, p = 0.000), 2004 (r = 0.995, p = 0.000) and 2003 (r = 0.993, p = 0.000)].

  4. See Appendix 2 for more details.

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Authors and Affiliations

  1. NUS Entrepreneurship Centre, National University of Singapore, 21 Heng Mui Keng Terrace, Level 5, Singapore, 119613, Singapore

    Poh Kam Wong & Annette Singh

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  1. Poh Kam Wong
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  2. Annette Singh
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Correspondence to Poh Kam Wong.

Appendices

Appendix: 1 Construction of a citation-weighted patent count

Following Trajtenberg (1990), a linear weight was used, with the citation-weighted patent count (WPC) in year t being.

\( {\text{WPC}}_{\text{t}} = \sum\limits_{i = 1}^{{n_{\text{t}} }} {(1 + C_{\text{i}} )} \), where n t = number of patents issued to the university in year t for the years 2006 to 2008, and C i is the number of citations received by each patent i up to the year 2009.

This is a somewhat crude approximation of the true citation-weighted patents count, for two reasons. Firstly, truncation bias means that citations to more recently issued patents are under-represented. Secondly, citations received by patents typically peak 4–5 years after the patent is issued (Mowery and Ziedonis 2002). Since our patents are those issued between 2006 and 2008, and data availability restricts our citation data to 2009, we have captured only a small fraction of the citations that will eventually be made to the patents in our database.

Appendix 2: Results of white test for heteroscedasticity

As can be seen in Appendix Table 7, the results for the White test reveal the presence of heteroscedasticity when using the level values of the dependent variables (DVs) in the regressions for simple patent counts (LM = 48.7, p = 0.00), citation-weighted patent counts (LM = 35.7, p = 0.02) and spin-offs (LM = 50.0, p = 0.00). After applying a square root transformation to the DVs, there is no evidence for heteroscedasticity for the regression for simple patent counts (LM = 30.3, p = 0.06) and citation-weighted patent counts (LM = 17.0, p = 0.65). Although the heteroscedasticity in the regression for spin-offs persists (LM = 42.07, p = 0.03), we did not attempt to further correct this, as the results remain unbiased. For consistency the square root transformation was also applied to the DV for the regression for licenses.

Table 7 Results for White test for heteroscedasticity: models with level DVs versus models with transformed DVs
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Wong, P.K., Singh, A. Do co-publications with industry lead to higher levels of university technology commercialization activity?. Scientometrics 97, 245–265 (2013). https://doi.org/10.1007/s11192-013-1029-1

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