Forthcoming and Online First Articles

International Journal of Bibliometrics in Business and Management

International Journal of Bibliometrics in Business and Management (IJBBM)

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International Journal of Bibliometrics in Business and Management (2 papers in press)

Regular Issues

  • A topic modelling based bibliometric exploration of international business research   Order a copy of this article
    by Diane A. Isabelle, Mika Westerlund 
    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.2023.10057330
  • Mapping the research landscape of artificial neural networks in stock market applications: a bibliometric analysis and future research directions   Order a copy of this article
    by Manpreet Kaur, Amit Kumar, Anil Kumar Mittal 
    Abstract: Artificial neural network (ANN) models have revolutionised various stock market operations due to their superior ability to deal with nonlinear and chaotic data. The present study aims to systematically map the conceptual and intellectual structure of ANN research in the entire domain of the stock market based on bibliometric analysis and network visualisation of 1,483 articles published during the period 19922022. The analysis revealed exponential growth in articles since 2018, with China as the major contributor. The upward publication trend evinces the contemporary relevance of the concerned field and its growing fascination in researchers community. Furthermore, the co-word analysis demonstrated seven thematic clusters and the cluster stock price forecasting remained the dominant one. In addition, the current study uncovered the challenges and knowledge gaps by intensively reviewing the relevant literature in the field. Based on the findings, the study provides valuable recommendations for future researchers and stock market practitioners regarding emerging research areas, the input selection approaches, parameter optimisation methods, and hybridisation of ANN models, and thus enables them to enhance the functional efficiency of models. Moreover, the study can also help regulators and policy-makers in managing the risks caused by uncertainties in the stock market by designing proactive strategies.
    Keywords: neural networks; bibliometric; stock market; forecasting; visualisation; artificial neural network; ANN.
    DOI: 10.1504/IJBBM.2023.10060284