Xin Li
Situating artificial intelligence in organization: A human-machine relationship perspective
Li, Xin; Rong, Ke; Shi, Xinwei
Abstract
The increasing advancements in artificial intelligence (AI) technology have resulted in greater adoption of intelligent devices, such as industrial and service robots. Such context substantially influences the routine processes of operations, thereby promoting the ongoing evolutionary development of human-machine interaction. Here, we analyze an interesting article published in the Academy of Management Review (AMR) by Ayenda Kemp, who proposes the concept of situated AI for discussing AI-driven competitive advantages. In our alternative framework of situating AI in the organization, we identify three aspects of the human-machine
relationship—cohesion, autonomy, and equality—and associate them with three redefined situating activities—anchoring, bounding, and calibrating, to bring the full potential of AI to an organization. We believe this defined framework can further contribute to relevant literature in the field of digital economy.
Citation
Li, X., Rong, K., & Shi, X. (2023). Situating artificial intelligence in organization: A human-machine relationship perspective. Journal of Digital Economy, 2, 330-335. https://doi.org/10.1016/j.jdec.2024.01.001
Journal Article Type | Article |
---|---|
Acceptance Date | Jan 6, 2024 |
Online Publication Date | Jan 7, 2024 |
Publication Date | 2023-12 |
Deposit Date | Sep 23, 2024 |
Journal | Journal of Digital Economy |
Electronic ISSN | 2773-0670 |
Publisher | Elsevier |
Peer Reviewed | Peer Reviewed |
Volume | 2 |
Pages | 330-335 |
DOI | https://doi.org/10.1016/j.jdec.2024.01.001 |
Public URL | https://durham-repository.worktribe.com/output/2870339 |
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