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Does knowledge diffusion between university and industry increase innovativeness?

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Abstract

This paper presents an econometric analysis of the impact of collaboration with universities on the innovative output of firms. We also illustrate the differences that emerge from robustness checks, based on different matching estimators and samples. Our findings strongly suggest that university collaboration has a positive influence on the innovative activity of large manufacturing firms. In contrast, there appears to be an insignificant association between university collaboration and the average service firm’s innovation output.

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Notes

  1. Contrary to the ‘linear model’ where fundamental research by university scientist leads to a discovery, the practical importance of which is recognized by a business firm, which collaborates with the university scientist in order to exploit it (Pavitt, 2003), the works by Pavitt and others explore issues such as the importance of increasing specialization and complexity, organizational behaviour, and the difficulties of matching technological opportunities with market needs.

  2. Cohen, Nelson, and Walch (2003) stressed that the Klevorick et al. (1995) finding is not necessarily inconsistent with other studies, which reported the impact from university research to be substantial.

  3. Salter et al. (2000) are discussing the potential for increased commercial exploitation of university knowledge, but they are questioning the importance of various kinds of “technology transfer” programs since the underlying assumption often is the much discredited linear model of innovation.

  4. Exact: In the estimation we specify which covariates (or variables) we attempt to match exactly on. In the study we compare two alternatives: (i) Exact matching on the continuous variable innovation input as a proportion of sales, and (ii) exact matching on a set of discrete covariates: possession of patents, public funding, financial obstacles to innovation, skill obstacles to innovation, demand pull innovations, main focus on the global market, domestic firm belonging to a group, and foreign owned company.

  5. Bias adjusted: Specifies that the bias corrected matching estimator is to be used. Bias-corrected matching estimator, which is a set of covariates distinct from the set used in matching.

  6. Distance measure: The metric for measuring the distance between two vectors of covariances. Letting ||x|| V =(x′Vx)1/2 be the vector norm with positive definite weight matrix V, we define ||z − x|| V to be the distance between the vectors x and z. We use two alternatives for V. Inverse: V is the diagonal matrix constructed by putting the inverses of the variance of the covariates on the diagonal. Mahalanobis: V=S − 1, where S is the sample covariance matrix of the covariates.

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Acknowledgements

The authors wish to express their gratitude to Paula Stephan, the participants at the World Bank workshop on University–Industry Linkages in Europe and North America, Cambridge 2005, and two anonymous referees for their valuable comments.

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Authors and Affiliations

  1. Department of Transport and Economics, Centre of Excellence for Science and Innovation and Studies, Royal Institute of Technology, Teknikringen 78B, SE-100 44, Stockholm, Sweden

    Hans Lööf

  2. Swedish Institute for Studies of Education and Research, Drottning Kristinas väg 33D, SE-114 28, Stockholm, Sweden

    Anders Broström

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  1. Hans Lööf
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Correspondence to Hans Lööf.

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Lööf, H., Broström, A. Does knowledge diffusion between university and industry increase innovativeness?. J Technol Transfer 33, 73–90 (2008). https://doi.org/10.1007/s10961-006-9001-3

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