Reconciling the personalization–privacy paradox via DoctorBots: The roles of service robot acceptance model elements and technology anxiety

Abstract The role of artificial intelligence (AI)‐based DoctorBots in improving healthcare and the medical industry is expected to increase significantly in the coming decades. However, contrary to the general view that AI can gradually replace human work, user acceptance of DoctorBots has become an obstacle to the development of AI medical diagnosis. Building on the service robot acceptance model (sRAM), this study investigates the potential of the functional, socioemotional, and relational elements of DoctorBots to reconcile the personalization–privacy paradox, thus enhancing user acceptance. Via two scenario‐based experiments with 398 participants, this study reveals that the negative influence of the personalization–privacy paradox on user acceptance is exacerbated when users' technology anxiety is high. In addition, an online survey of 400 DoctorBot users indicates that ease of use, subjective social norms, social presence, and rapport are effective in addressing both nonpersonalization (NPC) and privacy concerns (PVC). These findings suggest that the healthcare industry can leverage DoctorBots to implement self‐diagnosis. Specifically, DoctorBots' functional elements are effective in mitigating users' NPC, and their relational elements are effective in extenuating users' PVC.