Abstract
Analysing 829 abstracts and articles published in Management International over the 2009-2023 period, this research highlights the difficulties of interpreting unstructured textual data and suggests in response a tool capable of providing automated analysis. It also uses Latent Dirichlet Allocation (LDA) theme modelling to uncover hidden structures and achieve a more granular understanding of the thematic framework within which the journal has operated. The spotlight here is on data pre-processing, validation and visualisation, all crucial aspects of the types of analyses that become feasible when this method is used. The paper ends by suggesting a thematic modelling best practice that should make it possible to identify major and minor trends in order that future editorial strategies may be better informed and potentially more cutting-edge in nature.
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