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Advancing international business research through artificial intelligence and machine learning applications

Ajai Gaur et al.

Journal of World Business2026https://doi.org/10.1016/j.jwb.2026.101725article
AJG 4ABDC A*
Weight
0.50

Abstract

Artificial intelligence (AI) and machine learning (ML) are transforming international business (IB) research by enabling the analysis of large-scale, multimodal data and uncovering patterns that drive theoretical and empirical advances. Yet, the methodological breadth and technical complexity of AI and ML pose significant challenges for many IB scholars. This paper offers a structured roadmap for integrating AI- and ML-based techniques into IB research. We review key methods, including supervised, unsupervised, generative AI, and multimoal approaches, and illustrate how they can enrich core IB constructs such as foreignness, legitimacy, internationalization strategy, corporate governance, distance, and deglobalization. In doing so, we highlight both opportunities and methodological challenges associated with integrating ML into IB research. By linking methodological innovation with conceptual advancement, this paper positions AI and ML not merely as analytical toolkits but as transformative forces reshaping the future of IB research.

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https://doi.org/https://doi.org/10.1016/j.jwb.2026.101725

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@article{ajai2026,
  title        = {{Advancing international business research through artificial intelligence and machine learning applications}},
  author       = {Ajai Gaur et al.},
  journal      = {Journal of World Business},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1016/j.jwb.2026.101725},
}

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Advancing international business research through artificial intelligence and machine learning applications

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.