Large language models in business case research methodology: Reflections and considerations for scholar practitioners

Tiffany Snyder, R. Joseph Childs and Phillip White
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Tiffany Snyder: Indiana Wesleyan University, USA
R. Joseph Childs: Indiana Wesleyan University, USA
Phillip White: Consultant, Operational Consulting, USA

Advances in Online Education: A Peer-Reviewed Journal, 2024, vol. 3, issue 2, 121-133

Abstract: This paper explores the integration of large language models (LLMs) in business case research methodology, with a particular focus on their application in an applied doctoral project within a hybrid Doctor of Business Administration (DBA) programme at a private university in the US. Leveraging the capabilities of OpenAI’s ChatGPT model, this study demonstrates how LLMs can enhance the efficiency and depth of thematic analysis in qualitative research. The reflections from faculty and students reveal that while LLMs significantly streamline text analysis and uncover nuanced patterns, they must be used with ethical considerations and methodological rigour to avoid biases and ensure robust outcomes. Through a case study involving the revitalisation of membership participation at Veterans of Foreign Wars (VFW) Post 7560, the research illustrates the dual role of scholar-practitioners in balancing innovative AI applications with traditional academic standards. This paper contributes to the ongoing dialogue on disruptive technologies in academia, offering practical frameworks and philosophical insights for researchers navigating the complexities of artificial intelligence (AI) integration in higher education and business contexts.

Keywords: generative artificial intelligence; large language models; research methodologies; text analysis; scholar-practitioner; business administration; disruptive innovation (search for similar items in EconPapers)
JEL-codes: A2 I2 (search for similar items in EconPapers)
Date: 2024
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