Title: A topic modelling based bibliometric exploration of international business research

Authors: Diane A. Isabelle; Mika Westerlund

Addresses: Sprott School of Business, Carleton University, 1125 Colonel By Dr. Ottawa, ON, K1B 5S6, Canada ' Sprott School of Business, Carleton University, 1125 Colonel By Dr. Ottawa, ON, K1B 5S6, Canada

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.

Keywords: textual content analysis; topic modelling; machine learning; emerging multi-disciplinary research themes; international business research.

DOI: 10.1504/IJBBM.2023.137590

International Journal of Bibliometrics in Business and Management, 2023 Vol.2 No.4, pp.283 - 307

Received: 24 Oct 2022
Accepted: 20 May 2023
Published online: 27 Mar 2024 *

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