This study has several implications for university management and for policymakers in the science, technology, innovation and education policy domains.
In terms of management implications, the findings suggest that universities with different production models can be equally efficient in generating knowledge transfer outputs, and that research intensity is not a pre-requisite for efficiency. Universities can achieve efficiency by adopting a model of knowledge transfer engagement that is consistent with their resources (their subject specialisation, their research and teaching intensity, their knowledge transfer capabilities and infrastructures) without needing to replicate the knowledge transfer strategies of prominent institutions whose resources may be very different. Managers should therefore invest effort in identifying the knowledge transfer engagement strategies that best fit the institution’s resources, and aim to increase their performance in those activities. This approach may also improve the university’s reputation for excellence in specific knowledge transfer activities, which in turn may increase their ability to generate further knowledge transfer outputs. In fact, institutional reputation appears to increase knowledge transfer opportunities, with more reputable older, larger and very diversified institutions achieving greater efficiency.
Another management implication is that, rather than having an established KTO, what matters for efficiency are its practices and policies, and the professionalisation of its staff. KTOs therefore need to invest in staff training and in the development of best practices. Developing specialised, subject-specific skills and structures to support knowledge transfer, rather than generic ones, may also pay off, as suggested by the finding that more specialised universities are more efficient.
In terms of policy implications, the findings suggest that performance assessment systems need to adopt a broader definition of knowledge transfer. With a broader approach, more universities achieve efficiency, mean efficiency is higher, and the distribution of efficiency scores is less skewed. The universities that improve their efficiency typically have more arts and humanities staff, and less staff in the natural sciences and medicine; they also tend to be less research- and more teaching-
intensive. Therefore, systems of performance measurement that rely on a narrow definition of knowledge transfer tend to underestimate the efficiency of universities that do not follow the standard technology transfer model. In this vein, a criticism moved to the UK’s approach to knowledge transfer funding concerns the use of income as a measure of performance. This approach rewards universities whose knowledge transfer outputs are best measured in monetary terms, but this is unlikely to suit all university institutions, for example those whose prevailing form of
knowledge transfer is public engagement. As our analysis shows, measuring performance with different outputs produces different efficiency rankings.
As the understanding of the complexity of universities’ knowledge transfer processes grows, the development of systems to assess their performance should also
increasingly reflect this complexity, and allow for a more comprehensive assessment of knowledge transfer engagement. This is not easy to achieve. Although adopting a
broader model of knowledge transfer increases comparability among universities with different knowledge transfer strategies, methodological and data availability issues impose several constraints: the efficiency analysis could be performed only on the 97 universities that use positive quantities of all the inputs and outputs considered (which led to the exclusion of certain types of institutions such as most higher education colleges), and some knowledge transfer outputs were left out for data availability reasons. One way to achieve greater comparability of the assessed units would be to carry out efficiency analyses at departmental level, but few knowledge transfer data are systematically collected from university departments. Another approach might be to group universities according to the model of knowledge transfer they adopt, and then compare the relative performance of universities within each group (considering appropriate input and output mixes), rather than rate all universities against all others. Finally, while performance is often measured by looking at outputs, thinking about performance in terms of efficiency helps to recognise that universities work with very different resource mixes, which affect the nature of their knowledge transfer
engagement. Changes in the resources available to universities, for example because of changes in the rules governing the allocation of public funds, will also change their ability to engage in knowledge transfer. Therefore, policymakers need to think
systematically about the effect of changes in funding for research and teaching (for example, the replacement of recurrent grants with competitive funding) on the
universities’ ability to engage in knowledge transfer. The relationship between nature of funding sources and knowledge transfer strategies, which so far has been largely unexplored, would merit greater attention from both researchers and policymakers.
References
Agasisti, T. and Bonomi, F., 2013. Benchmarking universities' efficiency indicators in the presence of internal heterogeneity, Studies in Higher Education, DOI: 10.1080/03075079.2013.801423
Aigner, D.J., Lovell, C.A.K. and Schmidt, P., 1977. Formulation and estimation of stochastic frontier production function models, Journal of Econometrics, vol. 6, 21–37.
Anderson, T.R., Daim, T.U. and Lavoie, F.F., 2007. Measuring the efficiency of university technology transfer, Technovation, vol. 27, 306-318.
Anthony, R.N., Dearden, J., Bedford, N.M., 1989, Management control systems (sixth ed.), Irwin, Homewood, IL, 185–186.
Antonelli, C., 2008. The new economics of the university: a knowledge governance approach. The Journal of Technology Transfer, vol. 33, 1–22.
Arundel, A. and Geuna, A, 2004. Proximity and the use of public science by
innovative European firms, Economics of Innovation and New Technology, vol. 13, 559–580.
Association of University Technology Managers, 2014. ‘AUTM Licensing Activity Survey: FY2014’, http://www.autm.net/resources-surveys/research-reports- databases/licensing-surveys/fy-2014-licensing-survey/ (last accessed: October 2016).
Battese, G.E. and Coelli, T.J., 1995. A model for technical inefficiency effects in a stochastic production function for panel data, Empirical Economics, vol. 20, 325–332.
Bekkers R. and Bodas Freitas I.M., 2008. Analysing preferences for knowledge transfer channels between universities and industry: to what degree do sectors also matter? Research Policy, vol. 37, 1837-53.
Belenzon, S. and Schankerman, M., 2009. University knowledge transfer: private ownership, incentives, and local development objectives, Journal of Law and Economics, vol. 52, no. 1, 111–144.
Berbegal Mirabent, J. and Solé Parellada, F., 2012. What are we measuring when evaluating universities’ efficiency?, Regional and Sectoral Economics Studies
vol. 12, no. 3, 31-46.
Berbegal Mirabent, J., Lafuente, E. and Solé Parellada, F., 2013. The pursuit of knowledge transfer activities: an efficiency analysis of Spanish universities,
Journal of Business Research, in press.
Bercovitz, J., Feldman, M., Feller, I. and Burton, R., 2001. Organisational structure as determinants of academic patent and licensing behavior: an exploratory study of Duke, Johns Hopkins, and Pennsylvania State Universities, Journal of
Technology Transfer, vol. 26, no. 1/2, 21–35.
Bourelos, E., Magnusson, M. and McKelvey, M. 2012, Investigating the complexity facing academic entrepreneurs in science and engineering: the
complementarities of research performance, networks and support structures in commercialisation, Cambridge Journal of Economics, vol. 36, 751-780.
Bozeman, B., 2000, Technology transfer and public policy: a review of research and theory, Research Policy, vol. 29, 627-655.
Bruno, G.S.F. and Orsenigo, L., 2003, Variables influencing industrial funding of academic research in Italy: an empirical analysis, International Journal of Technology Management, vol. 26, 277–302.
Bulut, H. and Moschini, G., 2006. US universities’ net returns from patenting and licensing: a quantile regression analysis, Working Paper 06-WP 432, Center for Agricultural and Rural Development, Iowa State University, September.
Caldera, A. and Debande, O., 2010, Performance of Spanish universities in technology transfer: An empirical analysis, Research Policy,vol. 39, no. 9, 1160-1173.
Castro-Martínez, E., Molas-Gallart, J. and Olmos-Peñuela, J., 2010. Knowledge transfer in the social sciences and the humanities: informal links in a public research organisation, INGENIO (CSIC-UPV) Working Papers Series 2010-12. Chapple, W., Lockett, A., Siegel, D.S. and Wright, M., 2005. Assessing the relative
performance of university technology transfer office in UK: parametric and non-parametric evidence, Research Policy, vol. 34, no. 3, 369-284.
Charnes, A., Cooper, W., Lewin, A., Seiford, L., 1994. Data Envelopment Analysis: Theory, Methodology and Applications. Boston: Kluwer Academic Publishers. Charnes, A., Cooper, W.W., Rhodes, E., 1978. Measuring the inefficiency of
decision making units, European Journal of Operational Research, vol. 2, 429– 444.
Chiang, K. and His-Tai, H., 2006. Efficiency analysis of university departments: An empirical study, Omega - the International Journal of Management Science
Curi, C., Daraio, C. and Llerena, P., 2012. University technology transfer: how, (in)efficient are French universities? Cambridge Journal of Economics, vol. 36, 629-654.
D’Este, P. and Patel, P., 2007. University–industry linkages in the UK: What are the factors underlying the variety of interactions with industry? Research Policy, vol. 36, 1295–1313.
De Silva, L. R., Uyarra, E. and Oakey, R. 2012. Academic entrepreneurship in a resource constrained environment: diversification and synergistic effects. In: Audretsch, D. B., Lehmann, E. E., Link, A. N., Starnecker, A. and Audretsch, D., eds., Technology Transfer in a Global Economy, New York: Springer. Debackere, K. and Veugelers, R., 2005. The role of academic technology transfer
organisations in improving industry science links, Research Policy, vol. 34, no. 3, 321–342.
Deiaco, E., Hughes, A. and McKelvey, M., 2012, Universities as strategic actors in the knowledge economy, Cambridge Journal of Economics, vol. 36, 525-541. Deprins, D., Simar, L. and Tulkens, H., 1984. Measuring labour inefficiency in post offices. In M. Marchand, P. Pestieau and H. Tulkens, eds., The performance of public enterprises: concepts and measurements. Amsterdam, North-Holland, 243-267.
Drucker, P., 1977, An introductory view of management, New York: Harper College Press.
Etzkowitz, H., 2002. Incubation of incubators: innovation as a triple helix of
university–industry–government networks, Science and Public Policy, vol. 29, 115–28.
Friedman, J. and Silberman, J., 2003. University technology transfer: do incentives, management and location matter? Journal of Technology Transfer, vol. 28, 17– 30.
Geuna, A., Rossi, F., 2011, Changes to university IPR regulations in Europe and the impact on academic patenting, Research Policy, vol. 40, 1068-1076.
Grady, R. and Pratt, J., 2000. The UK technology transfer system: calls for stronger links between higher education and industry, Journal of Technology Transfer, vol. 25, no. 2, 205–211.
Griffin, R.W., 1987, Management (second ed.), Boston: Houghton Mifflin Co. Guerrero, M., Cunnigham, J.A. and Urbano, D., 2015, Economic impact of
entrepreneurial universities’ activities: an exploratory study of the United Kingdom, Research Policy, vol. 44, 748-764.
Harabi, N., 1995. Appropriability of technical innovations: an empirical analysis,
Research Policy, vol. 24, 981–92.
Henderson, R., Jaffe, A. and Trajtenberg, M., 1998. Universities as a source of
commercial technology: A detailed analysis of university patenting, 1965–1988,
The Review of Economics and Statistics, vol. 80, 119–127.
Hewitt-Dundas, N., 2012. Research intensity and knowledge transfer activity in UK universities, Research Policy, vol. 41, 262– 275.
Ho, M. H-C., Liu, J.S., Lu, W-M. and Huang, C-C., 2014. A new perspective to explore the technology transfer efficiencies in US universities, Journal of Technology Transfer, vol. 39, no. 2, 247-275.
Hughes, A., Kitson, M., 2012, Pathways to impact and the strategic role of
universities: new evidence on the breadth and depth of university knowledge exchange in the UK and the factors constraining its development, Cambridge Journal of Economics, vol. 36, 723-750.
Jensen, P.H., Palangkaraya, A. and Webster, E., 2009. A guide to metrics on knowledge transfer from universities to businesses and industry in Australia, Intellectual Property Research Institute of Australia Occasional Paper No. 03/09.
Johnes G. and Johnes J., 1993. Measuring the research performance of UK economics departments: an application of data envelopment analysis, Oxford Economic Papers 45.
Jones-Evans, J. and Klofsten, M., 2000. Comparing academic entrepreneurship in Europe – the case of Sweden and Ireland, Small Business Economics, vol. 14, 299–309.
Ken, Y., Huang, T., Wu, C.H. and Shiu, S.H., 2009. Super-efficiency DEA model for evaluating technology transfer performance of US university, Portland
International Conference on Management of Engineering & Technology (PICMET), August 2-6, in Portland, Oregon, USA.
Kim, J., Anderson, T. and Daim, T., 2008. Assessing university technology transfer: a measure of efficiency patterns, International Journal of Innovation and
Technology Management, vol. 5, no. 4, 495-526
Lach, S. and Schankerman, M., 2004. Royalty sharing and technology licensing in universities, Journal of the European Economic Association, vol. 2, no. 2–3, 252– 264.
Landry, R., Amara, N. and Ouimet, M., 2007. Determinants of knowledge transfer: evidence from Canadian university researchers in natural sciences and
engineering, Journal of Technology Transfer, vol. 32, no. 6, 561-592. Laursen, K. and Salter, A., 2004. Searching low and high: what types of firms use
universities as a source of innovation?, Research Policy, vol. 33, 1201-1215. Lawton Smith, H., 2007. Universities, innovation, and territorial development: a
review of the evidence, Environment and Planning C: Government and Policy, vol. 25, no. 1, 98 – 114.
Levin, R., 1986. A new look at the patent system, American Economic Review, vol. 76, no. 2, 199–202.
Levin, R. C., Klevorick, A. K., Nelson, R. R. and Winter, S.G., 1987. Appropriating the returns from industrial research and development, Brookings Papers on Economic Activity, vol. 3, 783-831.
Link, A. N. and Siegel, D. S., 2005. Generating science-based growth: an econometric analysis of the impact of organisational incentives on university–industry
technology transfer, European Journal of Finance, vol. 11, no. 3, 169–82. Lockett, A. and Wright, M., 2005. Resources, capabilities, risk capital and the
creation of university spin-out companies, Research Policy, vol. 34, 1043–1057. Lockett, A., Wright, M., Wild, A., 2014, The Institutionalisation of Third Stream
Activities in UK Higher Education: The Role of Discourse and Metrics, British Journal of Management, vol. 26, 78-92.
Meeusen, W. and Van den Broeck, J., 1977. Efficiency estimation from Cobb– Douglas production functions with composed errors, International Economic
Meyer-Krahmer, F. and Schmock, U., 1998. Science-based technologies:university– industry interactions in four fields, Research Policy, vol. 27, 835–851.
Montesinos, P., Carot, J.M., Martinez, J.M. and Mora, F., 2008. Third mission ranking for world class universities: beyond teaching and research, Higher Education in Europe, vol. 33, no. 2, 259-271.
Mowery, D., Sampat, B., 2005, Universities in national innovation systems, In J. Fagerberg , D. Mowery and R. Nelson (eds.), The Oxford Hand- book of Innovation. Oxford: Oxford University Press, 209-239.
Nelles, J. and Vorley, T., 2010. From policy to practice: engaging and embedding the third mission in contemporary universities, International Journal of Sociology and Social Policy, vol. 30, no. 7/8, 341-353.
O’Shea, R.P., Allen, T.J., Chevalier, A. and Roche, F., 2005. Entrepreneurial orientation, technology transfer and spinoff performance of U.S. universities,
Research Policy, vol. 34, no. 7, 994–1009.
Oliveira, M.D. and Teixeira, A.C., 2010. The determinants of technology transfer efficiency and the role of innovation policies: a survey, FEP Working Papers 375, Universidade do Porto, Faculdade de Economia do Porto.
Olmos-Penuela, J., Castro-Martinez, E. and D’Este, P., 2011. Knowledge transfer activities in humanities and social sciences: which determinants explain research group interactions with non-academic agents? Paper presented at the Dime-Druid Academy Winter Conference 2011.
Perkmann M., Neely A., Walsh K., 2011, How should firms evaluate success in university-industry alliances? A performance measurement system, R&D Management, vol. 41, 202-216.
Perkmann, M. and Walsh, K., 2007. University–industry relationships and open innovation: Towards a research agenda, International Journal of Management Reviews, vol. 9(4), 259-280.
Pinheiro, R., Langa, P.V., Pausits, A., 2015, The institutionalisation of universities’ third mission: introduction to the special issue, European Journal of Higher Education, vol. 5, no. 3, 227-232.
Ranga, M. and Etzkowitz, H., 2013. Triple Helix systems: an analytical framework for innovation policy and practice in the Knowledge Society, Industry and Higher Education, vol. 27, 237-262.
Ray, S.C., 2004, Data Envelopment Analysis: theory and techniques for economics and operations research. Cambridge University Press, Cambridge.
Research Councils UK, 2007, Knowledge transfer categorisation and harmonisation project, Final Report, Swindon: Research Councils UK.
Rogers, E., Yin, J. and Hoffmann, J., 2000. Assessing the effectiveness of technology transfer offices at U.S. research universities, The Journal of the Association of University Technology Managers, vol. 12, 47–80.
Rossi, F., Rosli, A., 2015, Indicators of university-industry knowledge transfer performance and their implications for universities: evidence from the United Kingdom, Studies in Higher Education, vol. 40, no. 10, 1970-1991.
Rosli, A., Rossi, F., 2015, Assessing the impact of knowledge transfer policies. An international comparison of models and indicators of universities’ knowledge transfer performance, in U. Hilpert (ed.), Handbook on politics and technology, London: Routledge.
Sánchez-Barrioluengo, M., 2014, Articulating the ‘three-missions’ in Spanish universities, Research Policy, vol. 43, no. 10, 1760–1773.
Schartinger, D., Schibany, A. and Gassler, H., 2001. Interactive relations between universities and firms: Empirical evidence for Austria, Journal of Technology Transfer, vol. 26, no. 3, 255-69.
Siegel, D.S., Waldman, D. and Link, A., 2003. Assessing the impact of
organisational practices on the relative productivity of university technology transfer offices: an exploratory study, ResearchPolicy, vol. 32, 27-48. Siegel, D.S, Veugelers, R. and Wright, M., 2007. Technology transfer offices and
commercialisation of university intellectual property: performance and policy implications, Oxford Review of Economic Policy, vol. 23, 640-660.
Simar, L. and Wilson, P.W., 2000. A general methodology for bootstrapping in nonparametric frontier models, Journal of Applied Statistics, vol. 27, 779–802. Simar, L. and Wilson, P.W., 2007. Estimation and inference in two-stage, semi-
parametric models of production processes, Journal of Econometrics, vol. 136, 31–64.
Sorensen, J. and Chambers, D., 2008. Evaluating academic technology transfer performance by how well access to knowledge is facilitated – defining an access metric, Journal of Technology Transfer, vol. 33, no. 5, 534–47.
Thursby, J. G. and Kemp, S., 2002. Growth and productive efficiency of university intellectual property licensing, Research Policy, vol. 31, 109-124.
Thursby, J.G. and Thursby, M.C., 2002. Who is selling the Ivory Tower? Sources of growth in university licensing, Management Science, vol. 48, no. 1, 90–104.
Von Tunzelmann, N. and Kraemer Mbula, E., 2003. Changes in research assessment practices in other countries since 1999. HEFCE commissioned report. SPRU University of Sussex.
Vorley, T. and Lawton Smith, H., 2007. Universities and the knowledge-based
economy, Environment and Planning C: Government andPolicy, vol. 25, no. 6, 775 – 778.
Wright, M., Clarysse, B., Lockett, A. and Knockaert, M., 2008. Mid-range universities’ linkages with industry: Knowledge types and the role of intermediaries, Research Policy, vol. 37, no. 8, 1205-1223.
1 Dedicated public funds for knowledge transfer engagement, where they exist, are usually small compared to public funding for research and teaching; for example, in England in 2014/15, funding allocated to support universities’ knowledge transfer activities (160 million GBP) amounted to about 5% of the recurrent grants allocated for teaching and research (3.1 billion GDP) (Higher Education Funding Council for England, 2016).
2 These studies have been identified based on the reviews by Berbegal Mirabent and Solé Parellada (2012), Oliveira and Teixeira (2010), Siegel, Veugelaers and Wright (2007), and a further search for articles on Google Scholar using the keywords “university” AND “efficiency” AND “technology transfer” OR “knowledge transfer”.
3
Consultancy income is a problematic variable since university policies vary: when academics are allowed to engage in private consulting, at least to some extent, the related incomes are unreported, leading to an underestimation of the real amount of consulting performed. This problem is mitigated by the fact that, in recent years, most universities have simultaneously tightened the rules on academic consulting and increased the incentives for academics to declare their consulting activities to the university. Providing consulting activities through the universities rather than privately has been made safer (most universities offer free indemnity insurance policies) and easier (most universities now have a contracting system for staff consulting activities; Perkmann and Walsh, 2007).
4 I am grateful to an anonymous reviewer for this remark.