Indicators of university–industry knowledge transfer performance and their implications for universities: evidence from the United Kingdom

Abstract

The issue of what indicators are most appropriate in order to measure the performance of universities in knowledge transfer (KT) activities remains relatively under-investigated. The main aim of this paper is to identify and discuss the limitations to the current measurements of university–industry KT performance, and propose some directions for improvement. We argue that university–industry KT can unfold in many ways and impact many stakeholders, and that, especially in highly differentiated university systems, choosing indicators focused on a narrow range of activities and impacts might limit the ability of universities to accurately represent their KT performance. Therefore, KT indicators should include a variety of activities and reflect a variety of impacts so as to allow comparability between different institutions and avoid the creation of undesirable behavioural incentives. To illustrate these issues empirically, the authors discuss the case of the United Kingdom's Higher Education–Business and Community Interaction (HE-BCI) survey.

Keywords:

• knowledge transfer
• knowledge exchange
• performance indicators
• university–industry relationships
• HE-BCI survey

Notes

1. Rosli and Rossi (forthcoming) present a comparison among the surveys implemented in the UK, United States, Canada, Australia and Europe.

2. Since 2009, the survey has been collected and validated by the Higher Education Statistics Agency (HESA).

3. The shares of funds allocated through formula are 80% in Northern Ireland, 75% in Wales and 92% in Scotland.

4. Since we use data from HEIF funding allocations in order to support our arguments, we must restrict our comparative analysis to those institutions that receive funding based on the same formula. We have decided to focus on universities in England, whose third-mission funding is distributed 100% through formula allocation, and which constitute the largest share (81%) of UK universities.

5. Since only the quantitative information contained in Part B is used as a basis to compare and reward universities' performance, we do not analyse in detail the more qualitative information collected in Part A.

6. The 13 options are as follows: access to education, graduate retention in local region, technology transfer, supporting small- and medium-size enterprises (SMEs), attracting inward investment to region, research collaboration with industry, attracting non-local students to the region, support for community development, developing local partnerships, management development, meeting regional skills needs, meeting national skills needs, and spinoff activity.

7. We have used an agglomerative complete-linkage clustering procedure, according to which units are progressively grouped into clusters based on a measure of distance. One of the advantages of hierarchical clustering is that the number of clusters can be appropriately selected upon inspection of the dendrogram produced by the clustering algorithm, rather than having to be specified a priori.

8. According to a Kruskal-Wallis rank test, eight out of the 13 variables used to construct the clusters have statistically significant means differences across the four clusters.

9. These data referred to staff full-person equivalent (excluding atypical) by cost centre, 2010/11 (HESA 2012).

10. In this paper, we do not deal with the issue of whether it makes sense to reward the KT activities of universities that perform them on a larger scale. There could be arguments in favour of this, for example if there were significant economies of scale (that is, if larger institutions were more productive), or if there was evidence that the KT activities performed by larger institutions had somehow greater impact. However, while there is some evidence that the impact of research activities increases more than proportionally with institutional size (Katz 2000), at present we know very little about how the amount and impact of KT activities in a broader sense scale with institutional size.

11. We obtain similar results if we use as a dependent variable either (i) a variable with six categories representing different levels of HEIF funding (0 = zero; 1 = <£500,000; 2 = £500,000–£1 m; 3 = £1 m–£2 m; 4 = £2 m–£2,850,000; 5 = maximum funding (£2,850,000); or (ii) the logarithm of the institution's eligible income for HEIF purposes. In case (i) we have estimated an ordered probit, in case (ii) we have estimated an OLS regression.