Authors
Chiu-Liang Liu, Chien-Ta Ho, Tzu-Chi Wu
Published in
BMC medical informatics and decision making. Jul 13, 2026. Epub Jul 13, 2026.
Abstract
Artificial intelligence (AI) health chatbots are increasingly used for health education and health promotion, yet the mechanisms underlying their adoption and the heterogeneity across user groups remain insufficiently understood.
Drawing on the Technology Acceptance Model (TAM), this study examined behavioral intention to use an artificial intelligence-based healthy dietary chatbot, incorporating interactivity and entertainment as key system features, and testing whether health consciousness and sociodemographic subgroups moderate technology acceptance pathways. An anonymous questionnaire survey was conducted after participants interacted with a LINE-based AI dietary chatbot. Structural equation modeling and subgroup analysis were applied to evaluate TAM relationships, moderation by health consciousness, and boundary conditions across demographic groups.
Perceived ease of use and perceived usefulness significantly predicted behavioral intention. Interactivity was primarily associated with intention indirectly through perceived ease of use and perceived usefulness, whereas entertainment showed a stronger direct association with intention. Health consciousness moderated the interactivity-usefulness and entertainment-intention relationships. Multi-group analyses further indicated pathway heterogeneity: interaction-related effects were stronger among younger, female, and more highly educated users, whereas older users placed greater weight on enjoyment and ease of use, and males exhibited a stronger usefulness-intention link.
Overall, the findings support the applicability of TAM to artificial intelligence-based health chatbot adoption and suggest the coexistence of cognitive, interactivity-driven and affective, entertainment-driven acceptance routes. The moderating roles of health consciousness and user characteristics underscore the importance of segment-specific design and dissemination strategies to enhance adoption.
PMID:
42443865
Bibliographic data and abstract were imported from PubMed on 14 Jul 2026.
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