Genarative AI, LLMs and the Future of Management Research

Speakers:
Eva Boxenbaum (Copenhagen Business School)
Teppo Felin (Utah State University)
Matthew Grimes (Cambridge Judge Business School)
Christine Moser (VU Amsterdam)
Christopher Wickert (VU Amsterdam)

Large language models (LLMs) are increasingly impacting scientific research and scholarly work. As advanced AI systems capable of mimicking elements of human reasoning, LLMs may automate, augment or reconfigure many of the data analysis, theorizing and writing processes now done by humans. In this context, this expert panel consisting of editors of leading journals and recognized thought experts on AI will shed light on the likely scenarios of the use of LLMs for scholarly work and research in the management and organizational domain. The experts will discuss the value of the technology for research and scholarship in this domain; the challenges and trade-offs that are involved in ensuring that the technology is used ethically and transparently, and what guardrails they see as important for the community, balanced against the strength of the knowledge domain and the interests of its scholarly community.

Readings:
Cornelissen, J., Höllerer, M. A., Boxenbaum, E., Faraj, S., & Gehman, J. (2024). Large Language Models and the Future of Organization Theory. Organization Theory, 5(1).
Felin, Teppo and Holweg, Matthias (2024). Theory Is All You Need: AI, Human Cognition, and Decision Making (February 24, 2024).
Gatrell C., Muzio D., Post C., Wickert C. (2024). Here, there and everywhere: On the responsible use of artificial intelligence (AI) in management research and the peer-review process. JMS.
Grimes M., Von Krogh G., Feuerriegel S., Rink F., Gruber M. (2023). From scarcity to abundance: Scholars and scholarship in an age of generative artificial intelligence. AMJ, 66, 1617–1624.
Lindebaum, D., Moser, C. and Islam, G. (2024), Big Data, Proxies, Algorithmic Decision-Making and the Future of Management Theory. JMS