Large Language Models in International Business Research: Opportunities, Challenges, and Prospects

International business (IB) research has evolved into a well-established discipline, yet continues to navigate a core tension between capturing the inherent complexity of global phenomena and the necessity for theoretical simplification. This complexity–simplicity paradox has led to downstream challenges—most notably, imprecise construct measurement and a predominant focus on causal inference over predictive modeling—that constrain the field’s ability to represent the dynamic nature of international business practice. This study positions large language models (LLMs) as a promising tool that complements existing analytical approaches and mitigates these challenges. Using LDA topic modeling, we identify key thematic areas within IB research, concentrating specifically on internationalization processes, knowledge transfer, cross-cultural management, and political and institutional dynamics. Our findings illustrate how LLMs can support construct refinement, theory extension, and methodological innovation. Concurrently, we recognize critical risks associated with LLM adoption, such as data biases, the “black box” nature of algorithms, and theoretical validation challenges, underscoring the necessity of methodological rigor. By bridging these methodological gaps and charting a forward-looking research agenda, this study offers a roadmap for engaging with the complexities of contemporary IB research and advancing its relevance in an era of rapid technological transformation.