IDEAS home Printed from https://ideas.repec.org/a/aza/aoe000/y2024v3i2p121-133.html
   My bibliography  Save this article

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

Author

Listed:
  • Snyder, Tiffany

    (Indiana Wesleyan University, USA)

  • Childs, R. Joseph

    (Indiana Wesleyan University, USA)

  • White, Phillip

    (Consultant, Operational Consulting, USA)

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.

Suggested Citation

  • Snyder, Tiffany & Childs, R. Joseph & White, Phillip, 2024. "Large language models in business case research methodology : Reflections and considerations for scholar practitioners," Advances in Online Education: A Peer-Reviewed Journal, Henry Stewart Publications, vol. 3(2), pages 121-133, December.
  • Handle: RePEc:aza:aoe000:y:2024:v:3:i:2:p:121-133
    as

    Download full text from publisher

    File URL: https://hstalks.com/article/8903/download/
    Download Restriction: Requires a paid subscription for full access.

    File URL: https://hstalks.com/article/8903/
    Download Restriction: Requires a paid subscription for full access.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    More about this item

    Keywords

    generative artificial intelligence; large language models; research methodologies; text analysis; scholar-practitioner; business administration; disruptive innovation;
    All these keywords.

    JEL classification:

    • I2 - Health, Education, and Welfare - - Education
    • A2 - General Economics and Teaching - - Economic Education and Teaching of Economics

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:aza:aoe000:y:2024:v:3:i:2:p:121-133. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Henry Stewart Talks (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.