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Introduction: People, Tensions and Impact in University Interactions Read chapter Read first chapter

2022 | OriginalPaper | Chapter

2. PhDs with Industry Partners – Assessing Collaboration and Topic Distribution Using a Text Mining Methodology

Authors : Kilian Buehling, Matthias Geissler

Published in: University-Industry Knowledge Interactions

Publisher: Springer International Publishing

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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.

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previous chapter Introduction: People, Tensions and Impact in University Interactions
next chapter The Heterogeneous Impact of Academic Patent Characteristics on Firms’ Economic Performance
Footnotes
1
We are aware that our second restriction leaves room for discussion and leads to a somewhat blurred understanding of “collaboration”. However, constructing the training sample for the classification algorithm and subsequently doing a fair amount of manual classification convinced us that donating sample materials or allowing the use of equipment did not necessarily involve an element of knowledge exchange. Firms also engage in these “giveaways” for strategic reasons, for example to expand the diffusion of specific materials/equipment among the scientific community. Notwithstanding the fact that firms may benefit from this kind of activities to some extent, we believe it is not justified to label these actions as “collaboration”.
 
2
For topic distributions following “Cao” the results did not significantly differ from 0. As the three procedures were based on different methods to determine the optimal topic number, the “Cao” procedure seems to systematically estimate a lower number. The mean number of topics according to the “Cao” procedure was 5.5 in our setting, whereas the “Griffith” and “Arun” procedures presented an average of 14.3 and 14.1 topics, respectively.
 
3
In an unreported variant, we employed a university fixed-effect model with no significant effect on the estimated coefficients.
 
4
Initial fears of “collaborative” dissertations being treated differently may be soothed by talks at a practitioner conference that assure researchers that the German bureaucracy treats all dissertations equally.
 
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Metadata
Title
PhDs with Industry Partners – Assessing Collaboration and Topic Distribution Using a Text Mining Methodology
Authors
Kilian Buehling
Matthias Geissler
Copyright Year
2022
Book
University-Industry Knowledge Interactions

Print ISBN: 978-3-030-84668-8

Electronic ISBN: 978-3-030-84669-5

Copyright Year: 2022

DOI
https://doi.org/10.1007/978-3-030-84669-5_2

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