Find the Good. Seek the Unity: A Hidden Markov Model of Human-AI Delegation Dynamics

As AI becomes integral to enterprise decision-making, this study explores the collaborative dynamics between managers and AI systems, focusing on human willingness to delegate tasks to AI. Grounded in the “agentic” systems delegation framework and instance-based learning theory, we employed a hidden Markov model in a longitudinal study of the dynamic delegation decision-making process involving 875 store managers. We found that there is a potential polarization in managers’ delegation willingness, with managers who recognize the capability of AI exhibiting high delegation willingness and fostering increased collaboration with AI over time—in contrast to their counterparts who are inclined to reduce AI’s involvement. During human-AI interactions, managers’ continuous performance appraisal of AI shapes their dynamic delegation willingness, which in turn affects their assessment of AI capability. This process forms a delegation feedback loop that drives the dynamics of delegation behaviors. Our study indicates that managers with a high willingness to delegate tend to outperform their counterparts and offers valuable insights for human-AI collaborative intelligence in organizational settings.

• 저널MIS Quarterly · 49(3) · 1185–1204
• 토픽Ethics and Social Impacts of AI · Safety Research
• DOI10.25300/misq/2024/18232
• 저자Junming Liu, Wei Thoo Yue, Alvin Chung Man Leung, Xin Zhang