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Generative AI and Empirical Research Methods in Operations Management

Author

Listed:
  • Timofey Shalpegin

    (Prince Mohammad Bin Salman College of Business and Entrepreneurship (Saudi Arabia, Makkah) - MBSC)

  • Tyson R. Browning

    (TCU - Texas Christian University)

  • Ajay Kumar

    (EM - EMLyon Business School)

  • Guangzhi Shang

    (ASU - Arizona State University [Tempe])

  • Jason Thatcher

    (University of Colorado [Boulder])

  • Jan C. Fransoo

    (Tilburg University (The Netherlands, Tilburg) - TiU)

  • Matthias Holweg

    (University of Oxford)

  • Benn Lawson

    (University of Oxford)

Abstract

Generative Artificial Intelligence (Gen-AI) is arguably the fastest-adopted technology in history (Mariani and Dwivedi 2024). Like past transformative technologies—such as computers and the Internet—Gen-AI brings new opportunities and challenges to research. However, its distinctive features may result in an adoption pattern and impact that differ from those of earlier technologies. Anthony et al. (2023) offered a novel perspective on studying AI. Traditionally, technologies are viewed either as tools to improve performance or as mediums to enhance collaboration; however, AI can be seen as a counterpart or an agent interacting with human agents (c.f. Bendoly et al. 2024; Angelopoulos et al. 2023). Along these lines, the popular press has already labeled Gen-AI models as a "superhuman research assistant" in the research process (The Economist 2023). With the formation of such hybrid teams in research, it is more critical than ever to define the roles of team members.

Suggested Citation

  • Timofey Shalpegin & Tyson R. Browning & Ajay Kumar & Guangzhi Shang & Jason Thatcher & Jan C. Fransoo & Matthias Holweg & Benn Lawson, 2025. "Generative AI and Empirical Research Methods in Operations Management," Post-Print hal-05531900, HAL.
    • Handle: RePEc:hal:journl:hal-05531900
    DOI: 10.1002/joom.1371
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