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Tracing university–industry knowledge transfer through a text mining approach

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Abstract

This study investigates knowledge transfer of university research to industry moving forward from traditional indicators by using methods from computational linguistics. We introduce a novel empirical use of pattern recognition and text mining tools to compare scientific publications to company documents. The contribution of the paper is twofold; first, a new method for tracing knowledge transfer is suggested and, second, our understanding of university–industry knowledge transfer is increased by introducing an additional perspective. We find that common text mining tools are suitable to identify concrete chunks of research knowledge within the collaborating industry. The method proves direct links between published university research and the information disclosed by companies in their websites and documents. We offer an extension to commonly used concepts, which rely either on qualitative case studies or the assessment of commercial indicators for the assessment of university research. Our empirical evidence shows that knowledge exchange can be detected with this approach, and, given some additions in the tools selection and adaption, it has the potential to become a supplementary method for the research community.

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Notes

  1. Technology transfer and knowledge transfer are in the literature strongly interrelated concepts and are widely used as interchangeable terms (Grimpe and Hussinger 2013; Sung and Gibson 2000).

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Acknowledgements

We thank the people performing the human validation of our results and the helpful comments we received on several conferences.

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  1. Department of Management Engineering, Technical University of Denmark, 2800, Kgs. Lyngby, Denmark

    Sabrina L. Woltmann & Lars Alkærsig

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  1. Sabrina L. Woltmann
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Correspondence to Sabrina L. Woltmann.

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Woltmann, S.L., Alkærsig, L. Tracing university–industry knowledge transfer through a text mining approach. Scientometrics 117, 449–472 (2018). https://doi.org/10.1007/s11192-018-2849-9

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