Organizational structures of Knowledge Transfer Offices: an analysis of the world’s top-ranked universities

Abstract

Universities are central actors in the production and delivery of new knowledge, and they play a unique role in National and Regional Innovation Systems. Almost all research universities have established Knowledge Transfer Offices (KTOs) to pursue their so-called ‘third mission’. This paper analyses the organizational structure of KTOs by discussing how universities organize their knowledge transfer activities, and by considering what factors may impact on the choice of specific organizational structures. We examine the KTO structures of the top 200 ranked universities in the world and highlight the presence of three knowledge transfer organizational models (internal, external, and mix) and six configurations of these models.

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Notes

  1. 1.

    The Bayh–Dole Act or Patent and Trademark Law Amendments Act (Pub. L. 96–517, December 12, 1980) is a United States law dealing with intellectual property arising from federal government-funded research. The Bayh Dole Act helped to establish technology transfer as a primary part of many university missions by giving universities ownership of intellectual property arising from federally funded research.

References

  1. Anderson, T. W., & Darling, D. A. (1952). Asymptotic theory of certain ‘goodness-of-fit’ criteria based on stochastic processes. Annals of Mathematical Statistics, 23, 193–212.

  2. Baty, P. (2012). Rankings without reason, Inside Higher Education, May 31. http://www.insidehighered.com/views/2012/05/31/essay-danger-countries-setting-policy-based-university-rankings.

  3. Benneworth, P., & Dawley, S. (2005). Managing the university third strand innovation process? Developing innovation support services in regionally engaged universities. Knowledge, Technology, & Policy, 18(3), 74–94.

    Article  Google Scholar 

  4. Bercovitz, J., & Feldmann, M. (2001). Organizational structure as a determinant of academic patent and licensing behavior: An exploratory study of Duke, Johns Hopkins, and Pennsylvania State Universities. The Journal of Technology Transfer, 26(1–2), 21–35.

    Article  Google Scholar 

  5. Bercovitz, J., & Feldmann, M. (2006). Entrepreneurial universities and technology transfer: A conceptual framework for understanding knowledge-based economic development. Journal of Technology Transfer, 31, 175–188.

    Article  Google Scholar 

  6. Carlsson, B., Dumitriu, M., Glass, J. T., Allen Nard, C., & Barrett, R. (2008). Intellectual property (IP) management: Organizational processes and structures, and the role of IP donations. The Journal of Technology Transfer, 33, 549–559.

    Article  Google Scholar 

  7. Cassia, L., De Massis, A., Meoli, M., & Minola, T. (2014). Entrepreneurship research centers around the world: Research orientation, knowledge transfer and performance. The Journal of Technology Transfer, 39, 1–17.

  8. Chapple, W., Lockett, A., Siegel, D., & Wright, M. (2005). Assessing the relative performance of U.K. University Technology Transfer Offices: Parametric and non-parametric evidence. Research Policy, 34, 369–384.

    Article  Google Scholar 

  9. Clarke, K. R. (1993). Non-parametric multivariate analyses of changes in community structure. Australian Journal of Ecology, 18, 117–143.

    Article  Google Scholar 

  10. Clarke, K. R., & Gorley, R. N. (2006). PRIMER v6: User manual/tutorial. Plymouth: PRIMER-E Ltd.

  11. Clarke, K. R., & Warwick, R. M. (2001). Change in marine communities: An approach to statistical analysis and interpretation (2nd ed.). Plymouth: PRIMER-E Ltd.

  12. Debackere, K., & Veugelers, R. (2005). The role of academic technology transfer organizations in improving industry science links. Research Policy, 34, 321–342.

    Article  Google Scholar 

  13. Drucker, P. F. (1973). Management: Tasks, responsibilities, practices. California: Claremont Harper & Row.

    Google Scholar 

  14. Eisenhardt, K. M. (1989). Building theories from case study research. The Academy of Management Review, 14(4), 532–550.

    Google Scholar 

  15. Eisenhardt, K. M., & Graebner, M. E. (2007). Theory building from cases: Opportunities and challenges. Academy of Management Journal, 50(1), 25–32.

    Article  Google Scholar 

  16. Etzkowitz, H., & Leydesdorffr, L. (2000). The dynamics of innovation: From National Systems and ‘‘Mode 2’’ to a Triple Helix of university–industry–government relations. Research Policy, 29, 109–123.

    Article  Google Scholar 

  17. Fisher, D., & Atkinson-Grosjean, J. (2002). Brokers on the boundary: Academy–industry liaison in Canadian Universities. Higher Education, 44(3/4), 449–467.

    Article  Google Scholar 

  18. Geuna, A., & Muscio, A. (2009). The governance of university knowledge transfer: A critical review of the Literature. Minerva, 47, 93–114.

    Article  Google Scholar 

  19. Huyghe, A., Knockaert, M., Wright, M., & Piva, E. (2014). Technology Transfer Offices as boundary spanners in the pre- spin-off process: The case of a hybrid model. Small Business Economics, 43, 289–307.

    Article  Google Scholar 

  20. Jones-Evans, D., Klofsten, M., Andersson, E., & Pandya, D. (1999). Creating a bridge between university and industry in small European countries: The role of the Industrial Liaison Office. R&D Management, 29(1), 47–56.

    Article  Google Scholar 

  21. Levene, H. (1960). Robust tests for equality of variances. In I. Olkin (Ed.), Contributions to probability and statistics. Palo Alto, CA: Stanford Univ. Press.

    Google Scholar 

  22. Link, A. N., & Siegel, D. S. (2005). Generating science-based growth: An econometric analysis of the impact of organizational incentives on university–industry technology transfer. The European Journal of Finance, 11(3), 169–181.

    Article  Google Scholar 

  23. Litan, R.E., Mitchell,L., & Reedy E.J., 2007. Commercializing university innovations: Alternative approaches. SSRN, http://ssrn.com/abstract=976005.

  24. Lockett, A., Wright, M., & Franklin, S. (2003). Technology transfer and universities’ spin-out strategies. Small Business Economics, 20(2), 185–200.

    Article  Google Scholar 

  25. Markman, G. D., Gianiodis, P. T., Phan, P. H., & Balkin, D. B. (2004). Entrepreneurship from the Ivory tower: Do incentive systems matter? The Journal of Technology Transfer, 29(3/4), 353–364.

    Article  Google Scholar 

  26. Markman, G. D., Phan, P. H., Balkin, D. B., & Gianiodis, P. T. (2005). Entrepreneurship and university-based technology transfer. Journal of Business Venturing, 20, 241–263.

    Article  Google Scholar 

  27. Meoli, M., Paleari, S., & Vismara, S. (2013). Completing the technology transfer process: M&As of science-based IPOs. Small Business Economics, 40(2), 227–248.

    Article  Google Scholar 

  28. Merton, R. K. (1968). The Matthew effect in science. Science, 159(3810), 56–63.

    Article  Google Scholar 

  29. O’Gorman, C., Byrne, O., & Pandya, D. (2008). How scientists commercialise new knowledge via entrepreneurship. Journal of Technology Transfer, 33, 23–43.

    Article  Google Scholar 

  30. Park, J. B., Ryu, T. K., & Gibson, D. V. (2010). How scientists commercialise new knowledge via entrepreneurship. Journal of Technology Transfer, 35, 237–252.

    Article  Google Scholar 

  31. Powell, W. W. (1990). Neither market nor hierarchy: Network forms of organization. Research in Organizational Behaviour, 12, 295–336.

    Google Scholar 

  32. Sala, A., Landoni, P., & Verganti, R. (2011). R&D networks: An evaluation framework. International Journal of Technology Management, 53(1), 19–43.

    Article  Google Scholar 

  33. Sampat, B. N. (2006). Patenting and US academic research in the 20th Century: The world before and after Bayh–Dole. Research Policy, 35(6), 772–789.

    Article  Google Scholar 

  34. Siegel, D. S., Waldman, D. A., Atwater, E. L., & Link, A. N. (2004). Toward a model of the effective transfer of scientific knowledge from academicians to practitioners: Qualitative evidence from the commercialization of university technologies. Journal of Engineering and Technology Management, 21, 115–142.

    Article  Google Scholar 

  35. Van Looy, B., Landoni, P., Callaert, J., Van Pottelsberghe, B., Sapsalis, E., & Debackere, K. (2011). Entrepreneurial effectiveness of European universities: An empirical assessment of antecedents and trade-offs. Research Policy, 40, 553–564.

    Article  Google Scholar 

  36. Walberg, H. J., & Tsai, S. L. (1983). Matthew effects in education. American Educational Research Journal, 20(3), 359–373.

    Google Scholar 

  37. Yin, R. K. (2003). Case study research: Design and methods (3rd ed.). Thousand Oaks, CA: Sage.

    Google Scholar 

  38. Young, T. A. (2007). Establishing a technology transfer office (§6.2). In A. Krattiger, R. T. Mahoney, L. Nelsen, et al. (Eds.), Intellectual property management in health and agricultural innovation: A handbook of best practices (Vols. 1–2). Mihr-Usa.

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Acknowledgments

We wish to thank an anonymous reviewer for his valuable comments which have contributed significantly to the development of this paper. We also wish to thank the editor involved for his patience and guidance.

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Correspondence to P. Landoni.

Appendix 1: comparison of the results considering different editions of the THE ranking

Appendix 1: comparison of the results considering different editions of the THE ranking

To control the generalizability of the results, we compared the datasets pertaining to four THE ranking editions: 2010–2011, 2011–2012, 2012–2013 and 2013–2014 (Fig. 6).

Fig. 6
figure6

Models and configurations in THE ranking editions from 2010–2011 to 2013–2014

The comparison showed that, although most of the universities have changed their position in the four editions, there has been no major replacement within the top 200 world universities. Few universities do not appear continuously in the list. Furthermore, the only replacements are at the bottom of the ranking. For example considering the last ranking, 9 universities rated in 2012–2013 are not present in the 2013–2014 ranking, and of these only 5 are new universities while the other 4 were already present in previous editions.

There is no evidence of a major change in the distribution of the models in the sample. This can be explained by two main considerations: the low rate of replacement in the ranking, and the fact that organizational change, in institutionalized contexts, takes many years. There are no significant changes in the universities ranked in the first 200 positions in 2010–2011, 2011–2012, 2012–2013 and 2013–2014. Only three universities changed their organizational KTO model: two UK universities adopted external models in 2010–2011 and one USA University adopted a mix model in 2011–2012. These data do not allow identification of a trend, but all changes were from an internal structure to an external/mix one.

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Brescia, F., Colombo, G. & Landoni, P. Organizational structures of Knowledge Transfer Offices: an analysis of the world’s top-ranked universities. J Technol Transf 41, 132–151 (2016). https://doi.org/10.1007/s10961-014-9384-5

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Keywords

  • Knowledge Transfer Organization (KTO)
  • TTO
  • Technology transfer
  • Organizational model
  • Higher education institution
  • University

JEL Classification

  • O31
  • O32
  • I23