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.
t Theory. JMS
Below we summarize the panel discussion. The panel featured leading scholars in the field who discussed the opportunities and challenges presented by this rapidly evolving technology.
AI and Human Collaboration in Research
The discussion centered on the notion that AI, particularly LLMs, will not replace human researchers but rather augment their capabilities. While some panelists acknowledged the potential for AI to surpass human intelligence in specific domains, the consensus was that human judgment and creativity remain essential for effective research.
Applications of AI in Management Research
The panelists identified several potential applications of AI in management research, including:
Enhanced Literature Review: LLMs can efficiently identify and synthesize relevant research from vast academic databases, saving researchers time and effort.
Data Collection and Analysis: AI can automate data collection processes and perform complex statistical analysis, allowing researchers to focus on interpretation and meaning-making.
Theory Development and Hypothesis Generation: LLMs can identify patterns and relationships in large datasets, potentially leading to the development of novel research questions and theoretical frameworks.
Ethical Considerations and the Future of Research
The panel highlighted the importance of using AI responsibly and ethically in management research. Concerns were raised regarding the potential for bias in AI algorithms and the need for transparency in the development and application of these tools.
Conclusion
The panelists concluded that generative AI and LLMs hold immense potential to revolutionize management research. By leveraging these tools effectively, researchers can gain new insights, accelerate discovery, and ultimately contribute to a deeper understanding of complex organizational phenomena. However, careful consideration must be given to the ethical implications of AI use, ensuring that human judgment and critical thinking remain at the forefront of the research process.
Additional Notes
The webinar also highlighted the limitations of AI, particularly its inability to grasp the contextual nuances of knowledge application. Human expertise remains crucial in translating research findings into practical solutions for real-world management challenges.
This report provides a brief overview of the discussion. It is recommended to view the entire video for a more comprehensive understanding of the panelists' perspectives.
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