PhDs with Industry Partners – Assessing Collaboration and Topic Distribution Using a Text Mining Methodology
Kilian Buehling () and
Matthias Geissler
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Kilian Buehling: Technische Universität Dresden, Research Group Knowledge and Technology Transfer
Matthias Geissler: Rationalisierungs- und Innovationszentrum der Deutschen Wirtschaft e. V., RKW Kompetenzzentrum, Digitalization and Innovation Section
Chapter Chapter 2 in University-Industry Knowledge Interactions, 2022, pp 9-24 from Springer
Abstract:
Abstract Collaboration between universities and industry partners is thought to facilitate knowledge diffusion and provide resources and new ideas for academic researchers. However, recent evidence also suggests a possible trade-off or cost with regard to individual productivity. Given its focus on quantitative output, the literature is rather silent on possible qualitative shifts in researchers’ agendas when engaging with industry partners. We contribute to a discussion on potential negative effects of university-industry engagement by comparing the topic distributions of PhD theses based on collaborative and noncollaborative research. The results indicate little difference between the two kinds of dissertation projects. We conclude that fears of agenda setting in collaborative research are unwarranted.
Keywords: University-industry collaboration; Latent Dirichlet analysis; Topic modelling (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:spr:inschp:978-3-030-84669-5_2
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DOI: 10.1007/978-3-030-84669-5_2
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