The roles of trust, personalization, loss of privacy, and anthropomorphism in public acceptance of smart healthcare services

Abstract

AI-based smart healthcare services are emerging as promising tools to improve efficiency and effectiveness of healthcare service delivery. This study aimed to examine the roles of trust and three AI-specific characteristics (i.e., personalization, loss of privacy, anthropomorphism) in public acceptance of smart healthcare services based on an extended Technology Acceptance Model. The model's validity was confirmed using a partial least squares structural equation modeling technique based on data collected from 769 survey samples. Multigroup analyses were conducted to determine whether the path coefficients differed by gender, age, and usage experience. The results showed that perceived usefulness, perceived ease of use, and the three AI-specific characteristics were important determinants of public acceptance of smart healthcare services, whose roles were fully or partially mediated by trust. Trust, perceived usefulness, and personalization directly determined behavioral intention to use smart healthcare services. The relationships among antecedent factors and behavioral intention to use smart healthcare services were also moderated by gender, age, and usage experience. The study demonstrated the critical roles of personalization, loss of privacy, and anthropomorphism in shaping public trust and acceptance of smart healthcare services. The results offer important theoretical and practical implications for the design and implementation of such services.