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Mapping the entrepreneurial university literature: a text mining approach

Mapping the entrepreneurial university literature: a text mining approach

Arroyabe, Marta F., Schumann, Martin and Fernandez De Arroyabe Arranz, Carlos ORCID logoORCID: https://orcid.org/0000-0002-6866-0684 (2022) Mapping the entrepreneurial university literature: a text mining approach. Studies in Higher Education, 47 (5). pp. 955-963. ISSN 0307-5079 (Print), 1470-174X (Online) (doi:10.1080/03075079.2022.2055318)

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Abstract

Since the introduction of the concept of entrepreneurial university in 1980s, the number of studies has dramatically increased, in particular since 2015. This had made the literature on the entrepreneurial university complex, fragmented and difficult to navigate. This paper provides a comprehensive review of all topics covered in the body of literature on the entrepreneurial university and identifies the most salient topics and papers within this literature, making use of text-mining techniques. Our paper employs topic modelling that reveals the underlying semantic structure of texts to identify the different underlying. Our study systematically analyses 1,110 papers over the period 1983–2020 using the Latent Dirichlet Allocation algorithm. Our analysis shows that the entrepreneurial university is fragmented around different topics that are very diverse. We find a total of 20 differentiated topics. Our study suggests that topics related to the overarching theme of academic entrepreneurship, in particular to commercialisation of research and the triple helix model are very popular within the entrepreneurial university literature. Finally, our analysis reveals that case-study type of research is losing momentum, giving path to nascent topics of research in the areas of entrepreneurial capability and university-industry alliances, which are becoming very popular within the entrepreneurial university literature.

Item Type: Article
Uncontrolled Keywords: entrepreneurial university; academic entrepreneurship; text mining; topic modelling; latent dirichlet allocation algorithm
Subjects: H Social Sciences > HB Economic Theory
L Education > LB Theory and practice of education > LB2300 Higher Education
Faculty / School / Research Centre / Research Group: Faculty of Business
Faculty of Business > Department of Systems Management & Strategy
Greenwich Business School > Networks and Urban Systems Centre (NUSC)
Last Modified: 02 Dec 2024 15:55
URI: http://gala.gre.ac.uk/id/eprint/36591

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