Developing impact measures of university-industry collaborations using text mining: evidence from Knowledge Transfer Partnerships in the UK
Dr Ainurul Rosli, Brunel, University of London
Dr Federica Rossi, Birkbeck, University of London
Dr Muthu De Silva, Birkbeck, University of London
Dr Nick Yip, University of East Anglia
This research proposes an original, replicable and scalable methodology to measure and analyse the impact of university-industry collaborations (UICs), using case studies as a base of evidence.
Case studies constitute a rich evidence base in order to understand how UICs generate impact, but their use as a source of impact measurement has so far proved elusive due to their qualitative nature. The proposed project sets out to systematically analyse 442 case studies on the impact of UICs funded through the Knowledge Transfer Partnership (KTP) scheme in the UK. In particular, the project intends to use text-mining techniques in order to derive case-level variables that can be used to measure several facets of impact, and develop causal analyses linking these impact measures to other variables derived from quantitative content analysis based upon the KTP case studies and other secondary sources. First, we will develop several constructs that allow us to characterize several facets of impact, and we will use text-mining techniques to build variables providing measures of these constructs. Second, we will demonstrate how to relate these impact variables to other variables capturing the characteristics of the collaboration and of the partners, in order to explore causal relationships. By developing a text-mining based approach to measure several characteristics of impact, and by shedding light on important causal determinants of the latter, the approach developed by this project can provide a methodological blueprint that can be used in the systematic analysis of other sets of UIC case studies.