How the affordance and psychological empowerment promoting AI-based medical consultation usage: A mixed-methods approach

Objectives: This study establishes a research model based on the theories of affordance and the theory of psychological empowerment to understand users' intentions in using artificial intelligence-based medical consultations (AIMCs), offering implications for their effective design. Methods: A two-stage mixed-methods research design was employed. The first stage involved qualitative interviews to conceptualize the main affordances of AIMCs and usage intentions, and the second phase comprised a quantitative study with 425 valid samples analyzed via partial least squares structural equation modeling. Results: The research results identified four AIMC affordances (i.e., human-AI interaction, human-like diagnosis, personalized treatment, and health information security) and two usage intentions (i.e., assist health decisions, and relieve health anxiety). The results of the quantitative analysis indicate that the four affordances significantly enhance perceived cognitive empowerment, whereas three affordances (excluding health information security) positively influence perceived emotional empowerment. Both cognitive and emotional empowerments were found to significantly affect users' AIMCs usage intentions. Additionally, we found that different disease types (acute and chronic) play an important moderating role in the relationship between perceived cognitive empowerment and relieve health anxiety. Conclusions: To increase psychological motivation and user adoption, AIMCs should be optimized with intuitive interactions, human-like diagnoses, and personalized care, while features should be tailored to the needs associated with acute and chronic conditions. On the basis of affordance and psychological empowerment theories, this study provides actionable insights for the development, design, and implementation of AIMCs.