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Generative Artificial Intelligence in Qualitative Data Analysis: Analyzing—Or Just Chatting?

David T. Nguyen & Catherine Welch

Organizational Research Methods2025https://doi.org/10.1177/10944281251377154article
AJG 4ABDC A*
Weight
0.54

Abstract

Researchers, engineers, and entrepreneurs are enthusiastically exploring and promoting ways to apply generative artificial intelligence (GenAI) tools to qualitative data analysis. From promises of automated coding and thematic analysis to functioning as a virtual research assistant that supports researchers in diverse interpretive and analytical tasks, the potential applications of GenAI in qualitative research appear vast. In this paper, we take a step back and ask what sort of technological artifact is GenAI and evaluate whether it is appropriate for qualitative data analysis. We provide an accessible, technologically informed analysis of GenAI, specifically large language models (LLMs), and put to the test the claimed transformative potential of using GenAI in qualitative data analysis. Our evaluation illustrates significant shortcomings that, if the technology is adopted uncritically by management researchers, will introduce unacceptable epistemic risks. We explore these epistemic risks and emphasize that the essence of qualitative data analysis lies in the interpretation of meaning, an inherently human capability.

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https://doi.org/https://doi.org/10.1177/10944281251377154

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@article{david2025,
  title        = {{Generative Artificial Intelligence in Qualitative Data Analysis: Analyzing—Or Just Chatting?}},
  author       = {David T. Nguyen & Catherine Welch},
  journal      = {Organizational Research Methods},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1177/10944281251377154},
}

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Evidence weight

0.54

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.52 × 0.4 = 0.21
M · momentum0.72 × 0.15 = 0.11
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.