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Enhancing Theorization Using Artificial Intelligence: Leveraging Large Language Models for Qualitative Analysis of Online Data

Diana Garcia Quevedo et al.

Organizational Research Methods2025https://doi.org/10.1177/10944281251339144article
AJG 4ABDC A*
Weight
0.54

Abstract

Online data are constantly growing, providing a wide range of opportunities to explore social phenomena. Large Language Models (LLMs) capture the inherent structure, contextual meaning, and nuance of human language and are the base for state-of-the-art Natural Language Processing (NLP) algorithms. In this article, we describe a method to assist qualitative researchers in the theorization process by efficiently exploring and selecting the most relevant information from a large online dataset. Using LLM-based NLP algorithms, qualitative researchers can efficiently analyze large amounts of online data while still maintaining deep contact with the data and preserving the richness of qualitative analysis. We illustrate the usefulness of our method by examining 5,516 social media posts from 18 entrepreneurs pursuing an environmental mission (ecopreneurs) to analyze their impression management tactics. By helping researchers to explore and select online data efficiently, our method enhances their analytical capabilities, leads to new insights, and ensures precision in counting and classification, thus strengthening the theorization process. We argue that LLMs push researchers to rethink research methods as the distinction between qualitative and quantitative approaches becomes blurred.

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https://doi.org/https://doi.org/10.1177/10944281251339144

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@article{diana2025,
  title        = {{Enhancing Theorization Using Artificial Intelligence: Leveraging Large Language Models for Qualitative Analysis of Online Data}},
  author       = {Diana Garcia Quevedo et al.},
  journal      = {Organizational Research Methods},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1177/10944281251339144},
}

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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.