Large Language Models for Conducting Advanced Text Analytics Information Systems Research

Benjamin M. Ampel, Chi-Heng Yang, James Hu, Hsinchun Chen
Last updated on Jun 9, 2026
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With huge amounts of text now available, this paper offers a practical playbook (the TAISR framework) for how information-systems researchers can use large language models like GPT to analyze text in their studies. It gives concrete, literature-grounded guidance tailored to different research styles and demonstrates the approach on three business-analytics case studies. It also lays out the pitfalls and limits researchers should watch for when relying on these models.

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APA Ampel, B. M., Yang, C.-H., Hu, J., & Chen, H. (2025). Large Language Models for Conducting Advanced Text Analytics Information Systems Research. ACM Transactions on Management Information Systems, 16(1), 1–27. https://doi.org/10.1145/3682069
BibTeX
@article{ampel2025large,
  title={Large Language Models for Conducting Advanced Text Analytics Information Systems Research},
  author={Benjamin M. Ampel and Chi-Heng Yang and James Hu and Hsinchun Chen},
  journal={ACM Transactions on Management Information Systems},
  year={2025},
  doi={10.1145/3682069}
}
LLMs Computational Design Science Deep Learning
Benjamin M. Ampel

Benjamin M. Ampel

Assistant Professor in Computer Information Systems and Director, Center for CyberAI Research (CCAIR)

My research focuses on AI-enabled Cybersecurity, including Cyber Threat Intelligence, Large Language Models, and Phishing Detection.