Location
Hilton Hawaiian Village, Honolulu, Hawaii
Event Website
https://hicss.hawaii.edu/
Start Date
3-1-2024 12:00 AM
End Date
6-1-2024 12:00 AM
Description
As organizations integrate AI decision tools into their decision-making processes, there is a need to understand factors that promote acceptance of decisions made with AI tools. This study draws from the theory of decisional fit and design features of an AI platform to examine the relationship between decision-making styles, procedural fairness, and decision acceptance when teams collaborate with AI decision aid to reach a decision. The results confirm the mediating relationship of procedural fairness between an intuitive decision-making style and decision acceptance. These results extend theory related to decision-making styles by identifying individual differences that predict procedural fairness and decision acceptance. Moreover, it offers guidance to managers and organizations seeking to adopt and design AI decision aids.
Recommended Citation
Askay, David; Dhillon, Anuraj; and Metcalf, Lynn, "Achieving Decisional Fit with AI-Aided Group Decisions: The Role of Intuitive Decision-Making Style in Predicting Perceived Fairness and Decision Acceptance" (2024). Hawaii International Conference on System Sciences 2024 (HICSS-57). 3.
https://aisel.aisnet.org/hicss-57/cl/machines_as_teammates/3
Achieving Decisional Fit with AI-Aided Group Decisions: The Role of Intuitive Decision-Making Style in Predicting Perceived Fairness and Decision Acceptance
Hilton Hawaiian Village, Honolulu, Hawaii
As organizations integrate AI decision tools into their decision-making processes, there is a need to understand factors that promote acceptance of decisions made with AI tools. This study draws from the theory of decisional fit and design features of an AI platform to examine the relationship between decision-making styles, procedural fairness, and decision acceptance when teams collaborate with AI decision aid to reach a decision. The results confirm the mediating relationship of procedural fairness between an intuitive decision-making style and decision acceptance. These results extend theory related to decision-making styles by identifying individual differences that predict procedural fairness and decision acceptance. Moreover, it offers guidance to managers and organizations seeking to adopt and design AI decision aids.
https://aisel.aisnet.org/hicss-57/cl/machines_as_teammates/3