Influence of features on adoption intention of AI health assistants: insights from mind perception theory

Abstract

Purpose With the rapid expansion of AI use in healthcare, chatbot-based AI health assistants are increasingly deployed for symptom checking, screening support and health promotion. However, public acceptance remains limited because many users question the reliability and diagnostic value of AI-provided health information and feel that these systems cannot respond to their emotions during interaction. Against this backdrop, this study asks: which AI features are associated with users' perceived diagnosticity and perceived empathy, and how perceived diagnosticity and perceived empathy relate to adoption intention, and whether these relationships vary by age. Design/methodology/approach A cross-sectional online survey was conducted, yielding 215 valid responses. The proposed model links AI feature cues (accuracy, responsiveness, personalization, affinity and anthropomorphism) to perceived diagnosticity and perceived empathy, which in turn predict adoption intention, with age as a moderator. Partial least squares structural equation modeling was used to assess the measurement and structural models and to test the hypothesized paths and moderation effects. Findings Accuracy and responsiveness were positively associated with perceived diagnosticity, whereas personalization was not. Affinity was positively associated with perceived empathy, while anthropomorphism showed no significant association. Both perceived diagnosticity and perceived empathy were positively related to adoption intention, with perceived diagnosticity exhibiting a stronger effect than perceived empathy. Moreover, age positively moderated the relationship between perceived diagnosticity and adoption intention, whereas the moderation effect of age on the perceived empathy and adoption intention link was not significant, indicating that the positive impact of diagnosticity on adoption intention becomes stronger as age increases. Originality/value This study contributes to research in three ways. First, it extends beyond general technological beliefs by modeling adoption as a social cognitive evaluation grounded in mind perception. Second, it empirically differentiates which features primarily function as competence cues versus warmth cues in AI health assistants. Third, it demonstrates that competence-related evaluation (perceived diagnosticity) is stronger than warmth-related evaluation (perceived empathy) for adoption intention in a higher-risk medical setting and identifies age as a key boundary condition.

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