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Invite colleaguesLarge language models in business case research methodology : Reflections and considerations for scholar practitioners
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.
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Author's Biography
Tiffany Snyder is the Director of Faculty Enrichment at Indiana Wesleyan University (IWU), overseeing the professional development of both full-time and adjunct faculty for the National and Global campus with a focus on hybrid and online programmes. Tiffany leads generative artificial intelligence (GenAI) training and teaching, from facilitating professional learning communities, delivering conference and university presentations, teaching graduate courses and consulting on graduate research and hosting on-site and online training for full-time and adjunct faculty. Tiffany has been a co-host of the Digital2Learn podcast since 2018. She earned a PhD in psychology with an emphasis on the integration of technology and learning from Grand Canyon University, a MA degree in student development counselling and administration and BS degree in psychology and leadership from IWU.
R. Joseph Childs currently holds the rank of Professor and leads innovative projects within the College of Adult and Professional Studies at Indiana Wesleyan University (IWU). He oversees the graduate management programmes serving over 3,000 international students and has led an initiative to incorporate the application of artificial intelligence (AI) across the curriculum in these programmes. In addition, Joseph teaches research methods for practitioners and serves as a research chair supervising between five and seven doctoral business students. Prior to joining IWU he served as the Dean of the College of Business and Legal Studies at Southeastern University in Lakeland, FL. His research interests include the impact of sleep deprivation on human productivity and learning effectiveness in higher education. Joseph also maintains a consulting and executive coaching practice and has worked on accounts for major corporations based in Silicon Valley as well as coaching entrepreneurs and leaders from a variety of industries. He earned a MS degree in public relations from the University of Southern Mississippi, MA degree from Western Seminary, and DBA from Golden Gate University, San Francisco, CA.
Phillip White received a DBA with management specialisation from Indiana Wesleyan University. He also holds an MBA from California State University Sacramento, a Bachelor’s in business from California State University Stanislaus and an Associate’s in business from Modesto Junior College. Philip is an Operation Iraqi Freedom veteran.
Citation
Snyder, Tiffany, Childs, R. Joseph and White, Phillip (2024, December 1). Large language models in business case research methodology : Reflections and considerations for scholar practitioners. In the Advances in Online Education: A Peer-Reviewed Journal, Volume 3, Issue 2. https://doi.org/10.69554/PDDL5720.Publications LLP