Publication Type
PhD Dissertation
Version
publishedVersion
Publication Date
5-2022
Abstract
A growing body of management research on artificial intelligence (AI) has consistently shown that people innately distrust decisions made by AI and find such decision processes simply less fair compared to decisions made by humans. My dissertation adopts a different perspective to propose that aside from fairness concerns, AI decision methods trigger perceptions in people that their individual uniqueness has not be adequately considered and this has negative consequences for their psychological or subjective well-being.
By combining theories of uniqueness, individuality, power, and well-being, I develop five studies to provide empirical evidence that aversion to AI-mediated decisions also operates through uniqueness neglect particularly in high-stakes contexts, and this mechanism predicts significant incremental variance above other mechanisms identified in existing research. I also extend the consequences of AI decision methods beyond resistance/acceptance of the technology, linking it to subjective well-being, a critical individual outcome that predicts other important employee attitudes and behaviors such as turnover intentions and job performance.
Finally, I explore the implications of decision role on AI decision methods to examine responses of decision makers and decision recipients and identify the contexts in which uniqueness neglect is relevant for these different groups of decision stakeholders. In doing so I provide a more comprehensive understanding of the impact of AI decision methods on different stakeholders in organizations.
Keywords
artificial intelligence (AI), algorithms, decision-making, uniqueness neglect, power, subjective well-being
Degree Awarded
PhD in Business (OBHR)
Discipline
Artificial Intelligence and Robotics | Organizational Behavior and Theory
Supervisor(s)
BASHSHUR, Michael Ramsay
Publisher
Singapore Management University
City or Country
Singapore
Citation
TEO, Huei Huei Laurel.
I'm special but A.I. doesn't get it. (2022).
Available at: https://ink.library.smu.edu.sg/etd_coll/412
Copyright Owner and License
Author
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Included in
Artificial Intelligence and Robotics Commons, Organizational Behavior and Theory Commons