Understanding the influence of perceived intelligence and perceived anthropomorphism on users’ intention to adopt healthcare chatbots

Objectives: Healthcare chatbots have become essential tools for delivering health information and alleviating systemic pressures on medical resources. However, little research exists on how the AI attributes of healthcare chatbots affect users' adoption intentions. Methods: Drawing on the stimulus-organism-response framework and tri-dimensional trusting beliefs theory, this study considers two AI attributes-perceived intelligence and perceived anthropomorphism-as stimuli and explores their effects on users' functional perceptions (perceived usefulness, perceived ease of use, perceived fairness, perceived performance risk) and relational perceptions (competence trust, benevolence trust, integrity trust), and subsequently users' intention to adopt healthcare chatbots. The research model was assessed using partial least squares structural equation modeling, based on survey data from 416 respondents in China. Results: The results demonstrate that both perceived intelligence and perceived anthropomorphism significantly influence users' functional and relational perceptions. Perceived intelligence and perceived anthropomorphism enhance users' intention to adopt healthcare chatbots through perceived usefulness, competence trust, and benevolence trust. Competence trust and benevolence trust mediate the effects of perceived intelligence and perceived anthropomorphism on users' intention to adopt healthcare chatbots, while integrity trust did not significantly affect user adoption intention. Conclusions: The findings enhance the theoretical understanding of healthcare chatbot adoption and provide practical insights for their design and implementation.