Human–Ai Collaboration in Qualitative Sustainability and Supply Chain Research: Cognitive Engagement, Interpretative Depth, and Ethical Implications
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The integration of Artificial Intelligence (AI) is transforming qualitative research in sustainability and supply chain studies, particularly in areas requiring context-sensitive interpretation and decision-making. This study examines how AI tools influence researcher cognition, interpretative depth, and analytical rigor through semi-structured interviews with researchers actively using AI in qualitative workflows. Findings indicate that AI enhances efficiency, supports idea generation, and reduces analytical workload. However, it also introduces risks, including surface-level interpretations, loss of contextual nuance, and potential algorithmic bias—critical concerns in sustainable supply chain contexts involving complex socio-environmental dynamics. The study highlights prompt engineering and AI literacy as key competencies shaping research quality. It proposes a hybrid human–AI analytical approach that emphasizes critical oversight, iterative validation, and ethical reflexivity. The findings suggest that AI can strengthen qualitative research when used as a complement to, rather than a substitute for, human judgment.
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บทความนี้ได้รับการเผยแพร่ภายใต้สัญญาอนุญาต Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) ซึ่งอนุญาตให้ผู้อื่นสามารถแชร์บทความได้โดยให้เครดิตผู้เขียนและห้ามนำไปใช้เพื่อการค้าหรือดัดแปลง หากต้องการใช้งานซ้ำในลักษณะอื่น ๆ หรือการเผยแพร่ซ้ำ จำเป็นต้องได้รับอนุญาตจากวารสารเอกสารอ้างอิง
Agrawal, A., Gans, J. S., & Goldfarb, A. (2019). Exploring the impact of artificial intelligence: Prediction versus judgment. Information Economics and Policy, 47, 1–6.
Barocas, S., & Selbst, A. D. (2016). Big data’s disparate impact. California Law Review, 104, 671–732.
Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big? Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, 610–623.
Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101.
Creswell, J. W., & Poth, C. N. (2018). Qualitative inquiry and research design: Choosing among five approaches (4th ed.). SAGE Publications.
Davenport, T. H., & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard Business Review, 96(1), 108–116.
Flick, U. (2022). An introduction to qualitative research (7th ed.). SAGE Publications.
Floridi, L., & Cowls, J. (2019). A unified framework of five principles for AI in society. Harvard Data Science Review, 1(1). https://doi.org/10.1162/99608f92.8cd550d1
Gao, X., Liu, Y., & Wang, Z. (2022). Algorithmic bias and interpretive gaps in AI-supported qualitative analysis. Journal of Qualitative Technology, 10(2), 101–117.
Jarrahi, M. H. (2018). Artificial intelligence and the future of work: Human–AI symbiosis in organizational decision making. Business Horizons, 61(4), 577–586.
Liu, X., Zhang, Y., Wang, D., & Ma, L. (2021). Prompt engineering and AI literacy for researchers. AI & Society, 36(4), 559–573.
Marzuki, S., Hanim, N., & Kaeppel, K. (2023). Cognitive load and qualitative research automation: A user study of AI tools. International Journal of Qualitative Methods, 22, 1–15.
Mollick, E. (2024). Co-intelligence: Living and working with AI. Penguin.
Noble, S. U. (2018). Algorithms of oppression: How search engines reinforce racism. New York University Press.
O’Neil, C. (2016). Weapons of math destruction: How big data increases inequality and threatens democracy. Crown.
Saldaña, J. (2021). The coding manual for qualitative researchers (4th ed.). SAGE Publications.
Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12(2), 257–285.
