Advancing international business research through artificial intelligence and machine learning applications
为国际商务学者提供将人工智能和机器学习技术融入研究的路线图，综述了监督学习、无监督学习、生成式AI和多模态方法，并展示它们如何丰富外来性、合法性、国际化战略等核心概念，同时指出机遇与挑战。
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.