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Unveiling the underlying mechanism of customers’ resistance to AI–powered healthcare: An information input perspective

Author

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  • Wang, Ran
  • Chang, Yaping

Abstract

A growing body of research has consistently demonstrated that consumers prefer human healthcare providers over AI, a phenomenon termed algorithm aversion. Some studies have suggested that the difficulty in tracking the internal state of AI (i.e., the black box) results in algorithm aversion, while others have highlighted that a cognitive bias toward AI (i.e., uniqueness neglect) contributes to it. This research integrates these two perspectives by introducing the concept of anticipated communication barrier. We demonstrate that anticipated communication barrier and uniqueness neglect serially mediate the algorithm aversion effect in healthcare (Studies 1–3), that individuals with high self-efficacy in communicating with AI (Study 2) do not exhibit algorithm aversion, and that enhancing individuals’ self-efficacy in communicating with AI through task-based interventions may reduce algorithm aversion (Study 3). Our research introduces a novel underlying mechanism of algorithm aversion, identifies a boundary condition for it, and offers managerial implications for AI-powered healthcare industry.

Suggested Citation

  • Wang, Ran & Chang, Yaping, 2026. "Unveiling the underlying mechanism of customers’ resistance to AI–powered healthcare: An information input perspective," Journal of Business Research, Elsevier, vol. 214(C).
    • Handle: RePEc:eee:jbrese:v:214:y:2026:i:c:s0148296326003383
    DOI: 10.1016/j.jbusres.2026.116303
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