Applying generative artificial intelligence applications for academic research on firms’ nonlinear internationalization

Purpose This study aims to critically evaluate the applicability of generative artificial intelligence (GenAI) tools for academic research in international business (IB), specifically focusing on the topic of firms’ nonlinear internationalization. It assesses these tools’ key performance dimensions: correctness, hallucinations and thoroughness. Design/methodology/approach This research adopts an exploratory approach, examining a comprehensive set of GenAI tools: eight chatbots and four AI-driven applications designed for academic purposes. The evaluation focuses on the capabilities and limitations of these tools in generating accurate research-related content for IB scholars. Findings This study finds that while GenAI tools capture some aspects of nonlinear internationalization, they often produce partially accurate and/or biased results. Common issues include providing fictitious sources, incorrect publication data and vague or incorrect answers. Thus, substantial development is still needed for GenAI tools to become reliable for scientific research. Practical implications Researchers should use GenAI tools with caution, verifying the accuracy of generated content and citations independently. A cautious approach is crucial to maintain the integrity and quality of academic research. Social implications This study raises awareness about ethical and practical challenges of using AI in academia, including issues related to plagiarism and misinformation. It underscores the importance of critical evaluation when using GenAI tools for research. Originality/value This paper contributes to the emerging literature on the role of GenAI in academic research by providing a critical assessment of the usability and limitations of current tools in studying complex IB phenomena. By using nonlinear internationalization as an example, it demonstrates how GenAI may support or hinder IB scholarship.