User Acceptance of AI-Based Smart Healthcare Devices: An Integrated TAM–ISSM Model with Perceived Risk

Abstract

The rapid advancement of artificial intelligence (AI) has accelerated the development of smart healthcare devices capable of providing continuous, contactless monitoring through radar-based sensing and automated data analysis. Despite their potential to enhance personal health management and support early detection of abnormalities, user acceptance of these technologies remains uncertain due to concerns related to usability, information reliability, and perceived risks. This study develops and empirically validates an integrated model that combines the Technology Acceptance Model (TAM) and the Information Systems Success Model (ISSM), while incorporating perceived risk as a moderating variable. Data were collected from 223 respondents through an online survey and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results show that system quality and information quality significantly influence perceived usefulness and perceived ease of use, which subsequently shape users’ attitudes and behavioral intentions. Perceived usefulness emerged as the strongest determinant of adoption intention. The moderation analysis further reveals that perceived risk weakens the positive effect of perceived usefulness on attitude but does not alter the influence of perceived ease of use. These findings highlight the combined importance of technical performance, cognitive evaluations, and psychological concerns in promoting user acceptance of AI-enabled smart healthcare devices.