Paper Type
Short
Paper Number
PACIS2026-1244
Description
As AI agents increasingly assist humans in tasks with uncertainty and interdependence, they are evolving from passive tools into agentic collaborators to act autonomously without explicit prompting. This shift transfers traditional human-initiated collaboration into novel AI-initiated collaboration. While such solicitation facilitates task processing, it introduces a psychological trade-off between human perceived status loss and AI accountability attribution. Drawing on the functionalist perspective on team effectiveness, this study examines how AI solicitation shapes user engagement intention and explores the cognitive boundary for these opposite effects. We find that AI solicitation simultaneously enhances perceived accountability while triggering perceived status loss, yielding an overall positive effect on engagement intention. Importantly, solicitation initiated by AI is fundamentally distinct from human solicitation, and perceived human distinctiveness determines which pathway becomes dominant. These findings advance human–AI collaboration research by revealing the paradoxical mechanism underlying AI-initiated collaboration and provide actionable insights for designing agentic AI systems.
Recommended Citation
Zhu, Yunchang; Lu, Xianghua; and Zhang, Cheng, "When AI Takes the Lead: How AI Solicitation Shapes Users Engagement in Human-AI Collaboration" (2026). PACIS 2026 Proceedings. 2.
https://aisel.aisnet.org/pacis2026/hci_robotic/hci_robotic/2
When AI Takes the Lead: How AI Solicitation Shapes Users Engagement in Human-AI Collaboration
As AI agents increasingly assist humans in tasks with uncertainty and interdependence, they are evolving from passive tools into agentic collaborators to act autonomously without explicit prompting. This shift transfers traditional human-initiated collaboration into novel AI-initiated collaboration. While such solicitation facilitates task processing, it introduces a psychological trade-off between human perceived status loss and AI accountability attribution. Drawing on the functionalist perspective on team effectiveness, this study examines how AI solicitation shapes user engagement intention and explores the cognitive boundary for these opposite effects. We find that AI solicitation simultaneously enhances perceived accountability while triggering perceived status loss, yielding an overall positive effect on engagement intention. Importantly, solicitation initiated by AI is fundamentally distinct from human solicitation, and perceived human distinctiveness determines which pathway becomes dominant. These findings advance human–AI collaboration research by revealing the paradoxical mechanism underlying AI-initiated collaboration and provide actionable insights for designing agentic AI systems.
Comments
12-HCI