AI and machine learning reshape international business research

What the study found: The study argues that artificial intelligence (AI) and machine learning (ML) are transforming international business (IB) research by enabling analysis of large-scale, multimodal data and by uncovering patterns that support theoretical and empirical advances.

Why the authors say this matters: The authors conclude that AI and ML are not just analytical tools, but transformative forces reshaping the future of IB research. They say the paper provides a structured roadmap for integrating these methods into IB research.

What the researchers tested: The paper reviews key AI and ML methods, including supervised learning, unsupervised learning, generative AI, and multimodal approaches. It also shows how these methods can be applied to IB constructs such as foreignness, legitimacy, internationalization strategy, corporate governance, distance, and deglobalization.

What worked and what didn't: The abstract reports that these methods can enrich core IB constructs and support theoretical and empirical advances. It also highlights methodological challenges and the technical complexity of AI and ML, which can make them difficult for many IB scholars to use.

What to keep in mind: The abstract describes opportunities and challenges, but it does not provide specific empirical findings, comparative performance results, or detailed limitations beyond the general methodological difficulty and complexity of these approaches.

Key points

• AI and machine learning are described as transforming international business research.
• The paper presents a structured roadmap for integrating AI- and ML-based techniques into IB research.
• The review covers supervised, unsupervised, generative AI, and multimodal approaches.
• The authors link these methods to IB concepts including foreignness, legitimacy, and internationalization strategy.
• The abstract notes both opportunities and methodological challenges for using ML in IB research.