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Reducing consumers’ resistance to AI agents in online healthcare consultations: the role of human-AI teaming from a trust transfer perspective

Author

Listed:
  • Du, Gang
  • Zhou, Chuanmei
  • Cheng, Xusen

Abstract

While collaborative models that integrate human doctors and AI agents are increasingly critical in online healthcare consultations, little is known about how variations in doctors’ professional titles (e.g., senior vs. junior) within these teams influence healthcare consumers’ resistance to AI agents. Drawing on trust transfer theory, this study examines how various compositions of human doctor-AI teams affect consumers’ resistance to the AI agent. Across four scenario-based studies, we discovered that human doctor-AI teaming can reduce consumers’ resistance to the AI agent. Specifically, senior doctor-AI teaming more effectively reduces consumers’ resistance to the AI agent by enhancing trust transfer compared to junior doctor-AI teaming. However, this effect was not observed when the severity of the diseases consulted by consumers was low or the AI agent served as a partner. This study enriches human-AI collaboration research and suggests strategies to improve consumer acceptance of AI in healthcare services.

Suggested Citation

  • Du, Gang & Zhou, Chuanmei & Cheng, Xusen, 2026. "Reducing consumers’ resistance to AI agents in online healthcare consultations: the role of human-AI teaming from a trust transfer perspective," Journal of Business Research, Elsevier, vol. 206(C).
    • Handle: RePEc:eee:jbrese:v:206:y:2026:i:c:s0148296325007829
    DOI: 10.1016/j.jbusres.2025.115959
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    References listed on IDEAS

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