Knowledge and Technology Transfer Activities between Firms and Universities in Switzerland: An Analysis Based on Firm Data

Abstract

This study explores the factors determining the propensity of Swiss firms to get involved in knowledge and technology transfer (KTT) activities with universities and other research institutions in order to gain new scientific knowledge in research fields which are relevant for their own innovation activities. The data used in this study were collected by a postal survey among Swiss enterprises. We found that the propensity to KTT activities is positively correlated with the share of employees with tertiary-level education, the existence of R&D activities, firm age and firm size. Further, it is negatively correlated with obstacles of KTT activities related to the functioning of the interface between firms and universities. The analysis of five specific forms of KTT activities showed that there are differences among these forms as to the explanatory factors identified for the variable for overall KTT activities, particularly with respect to human capital intensity, some obstacles and firm size.

Keywords:

• Knowledge and technology transfer
• innovation activities
• R&D activities

Acknowledgements

The authors gratefully acknowledge the financial support of the ETH-Board. Also the fruitful comments and suggestions of the participants of the World Bank–CMI Workshop on University–Industry Linkages in Europe and North America, Cambridge, UK, 26–27 September 2005 and of the Research Seminar, Department of Managerial Economics, Strategy and Innovation, Katholieke Universiteit Leuven (KUL), Leuven, Belgium, 29 November 2007, of two anonymous referees as well as of the editor-in-chief of this journal are also gratefully acknowledged.

Notes

¹ Economics: see, e.g. Vol. 63, No. 3 of the Journal of Economic Behaviour and Organization of July 2007 (edited by A. Jaffe, J. Lerner, S. Stern and M. Thursby) on “Academic Science and Entrepreneurship: Dual Engines of Growth”; Vol. 34, No. 3 of Research Policy of April 2005 (edited by A. N. Link and D. S. Siegel) dedicated to “University-Based Technology Initiatives”; Vol. 28, Nos. 3/4 of the Journal of Technology Transfer of August 2003 devoted to the “Symposium on the State of the Science and Practice of Technology Transfer”. Policy: see, e.g. OECD (1999, 2002, 2003).

² Especially the Swiss literature on this topic is quite scarce: Vock et al. (2004) presented and discussed the results of a survey on codified forms of KTT (number of R&D projects in cooperation with firms, patents, licences); this survey was addressed to technology transfer offices at universities. Thierstein et al. (2002) investigated the spin-offs/start-ups of graduates of the universities of Eastern Switzerland, Berwert et al. (2002) the spin-offs/start-ups of Swiss technical universities. The study of Lenz (1998) dealt mainly with horizontal innovation cooperation between firms.

³ See Onida and Malerba (1989), Geisler (1997), Mayer (2000) and Schartinger et al. (2001) for studies dealing with firm obstacles of KTT activities.

⁴ Versions of the questionnaire in German, French and Italian are available at www.kof.ethz.ch

⁵ An alternative method of constructing these variables would be to calculate the average score for each of the original variables and use this average to build a dummy variable (e.g. the value 1 was given firms with an average score ≥ 3). Some preliminary tests showed that that there are not significant differences.

⁶ The five-factor solution was chosen according to statistical criteria that are implemented in the software we used (SAS). The most important of them is Kaiser's measure (MSA) that amounted to the high value 0.941 and the root mean square off-diagonal residuals (RMSE), which is quite small. Also the plot of eigenvalues as a function of the number of possible factors (not shown here) points to a five-factor solution. In addition, we took a look whether these results made sense in economic terms.

⁷ As a referee mentioned such an argumentation implicates that this kind of impediments are endogenous to the KTT process. An argument against the endogenous character of obstacle variables could be that not all of them show positive coefficients and it is not clear why some of these obstacles should be endogenous (positive coefficients) and some others not (negative coefficients). But from a conceptual point of view one cannot reject the endogeneity argument. From an econometric point of view we assess the likelihood of a significant correlation with the residuals of the estimation equations as quite small, given that obstacle variables are pseudo-quantitative variables that are generated by principal component factor analysis (see Section 6.2).

⁸ We do not see any reason why the specific form of activities should be dependent on firm age.

⁹ The STATA procedure mprobit estimates M-equation probit models by the method of simulated maximum likelihood. The Geweke–Hajivassiliou–Keane (GHK) simulator is applied to evaluate the M-dimensional Normal integrals in the likelihood function (for a description of the GHK simulator see Greene, 2003).