Behavioral drivers of AI nursing acceptance in the Greater Bay Area: a family-caregiver perspective on trust and risk

Abstract

The rapid aging of the population in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) has increased demand for smart healthcare solutions. Artificial intelligence (AI)-based nursing technologies show promise in alleviating care burdens, yet family caregivers—often the primary decision-makers—exhibit low adoption rates due to trust issues and risk perception. This study investigated factors influencing caregivers’ behavioral intention to adopt AI nursing technologies by developing an extended Unified Theory of Acceptance and Use of Technology (UTAUT) model incorporating trust and perceived risk. A cross-sectional survey was conducted across hospitals and care institutions in the GBA (n = 163) and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). Results indicated that trust, perceived usefulness (performance expectancy), and institutional support (facilitating conditions) were positively associated with intention to adopt. Social influence also had a positive effect but was significantly weakened by perceived risk, while age moderated the effect of perceived difficulty on adoption intention. The findings highlight the importance of improving system transparency, tailoring interface design for older users, and building trust through institutional support, suggesting that policymakers and developers should prioritize inclusive, age-adaptive AI design and ethical governance to enhance caregiver acceptance and AI integration in older population.