Paper Type
Complete
Abstract
A growing number of organizations employ sequential human-AI collaboration in two-stage decision processes, yet little is known about how the order of agent involvement shapes the psychological responses of decision-influenced individuals. This study investigates how the sequence of human-AI collaboration—Human-before-AI versus AI-before-Human—affects perceptions of procedural fairness, distributive fairness, and process-oriented satisfaction. Drawing on Uncertainty Management Theory (UMT), we further examine how outcome favorability and AI capability moderate these sequence effects. Three online between-subjects experiments were conducted across financial investment, consumer recommendation, and organizational promotion contexts. Results consistently show that the AI-before-Human sequence elicits significantly higher fairness perceptions and satisfaction. These advantages are amplified when outcomes are unfavorable and when AI capability is perceived as low. Findings advance theory on human-AI collaboration and offer practical guidance for designing human-centered decision support systems.
Paper Number
1491
Recommended Citation
Ling, Bin; Shao, Chengwei; and Cai, Fei, "Who Leads the Dance? Individual Perceptions of Order in Human-AI Collaboration" (2026). AMCIS 2026 Proceedings. 6.
https://aisel.aisnet.org/amcis2026/ai_systdesign/ai_systdesign/6
Who Leads the Dance? Individual Perceptions of Order in Human-AI Collaboration
A growing number of organizations employ sequential human-AI collaboration in two-stage decision processes, yet little is known about how the order of agent involvement shapes the psychological responses of decision-influenced individuals. This study investigates how the sequence of human-AI collaboration—Human-before-AI versus AI-before-Human—affects perceptions of procedural fairness, distributive fairness, and process-oriented satisfaction. Drawing on Uncertainty Management Theory (UMT), we further examine how outcome favorability and AI capability moderate these sequence effects. Three online between-subjects experiments were conducted across financial investment, consumer recommendation, and organizational promotion contexts. Results consistently show that the AI-before-Human sequence elicits significantly higher fairness perceptions and satisfaction. These advantages are amplified when outcomes are unfavorable and when AI capability is perceived as low. Findings advance theory on human-AI collaboration and offer practical guidance for designing human-centered decision support systems.
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