A topic modelling based bibliometric exploration of international business research Online publication date: Wed, 27-Mar-2024
by Diane A. Isabelle; Mika Westerlund
International Journal of Bibliometrics in Business and Management (IJBBM), Vol. 2, No. 4, 2023
Abstract: This paper explores the application of machine learning topic modelling to contrast findings with traditional bibliometric approaches and to identify research themes and trends from international business conferences. We apply topic modelling to discover latent themes in a corpus of 934 conference proceeding abstracts from the Annual Meetings of the Academy of International Business (AIB). Using a similar period, we then contrast our findings with that of studies using traditional bibliometric methods. Our analysis reveals that research presented in AIB conferences can be categorised under six broad topics: 1) internationalisation; 2) business model; 3) resources; 4) firm-specific advantages; 5) emerging economies; 6) strategic orientation. The study proposes new research directions based on these findings and discuss applied insights for various stakeholders. Furthermore, it demonstrates the usage of topic modelling as a valuable computer aided content analytic tool for the social sciences.
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