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Abstract
Diana Garcia Quevedo and Josue Kuri’s AI for Qualitative Research: A Hands-On Guide for Management Scholars (2026) offers a timely and accessible introduction to leveraging large language models (LLMs) for qualitative analysis in management research. Organized into two parts—theoretical foundations and hands-on applications—the book equips researchers with both conceptual understanding and practical programming skills to integrate AI tools responsibly into their analytical workflows. Garcia Quevedo, whose research at ESCP Business School centres on gender, entrepreneurship, and AI-driven qualitative methods, and Kuri, a principal scientist with extensive expertise in AI and network systems, provide a unique interdisciplinary perspective. This review examines the book’s structure, key contributions, and implications for the future of qualitative inquiry, highlighting its potential to transform how management scholars approach large-scale textual data while preserving the interpretive depth that defines qualitative research.
Keywords
artificial intelligence, qualitative research, large language models, management research, natural language processing
Acknowledgements
The author would like to acknowledge the Universitas Pamulang that has fully supported this article.
Publication Date
5-19-2026
Creative Commons License

This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 4.0 International License.
Recommended APA Citation
Atmawijaya, T. D. (2026). Bridging computational innovation and qualitative rigor: A practical framework for integrating AI into management research. The Qualitative Report, 31(5), 5920-5929.
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Quantitative, Qualitative, Comparative, and Historical Methodologies Commons, Social Statistics Commons
