Investigating the factors influencing users’ adoption of artificial intelligence health assistants based on an extended UTAUT model
Jiayu Su, Yuhui Wang, Hongyi Liu, Zuopeng Zhang, Zhe Wang, Zhirong Li
Abstract
As an emerging healthcare technology, artificial intelligence (AI) health assistants have garnered significant attention. However, the acceptance and intention of ordinary users to adopt AI health assistants require further exploration. This study aims to identify factors influencing users' intentions to use AI health assistants and enhance understanding of the acceptance mechanisms for this technology. Based on the unified theory of acceptance and use of technology (UTAUT), we expanded the variables of perceived trust (PT) and perceived risk (PR). We recruited 373 Chinese ordinary users online and analyzed the data using covariance-based structural equation modeling (CB-SEM). The results indicate that the original UTAUT structure is robust, performance expectancy (PE), effort expectancy (EE), and social influence (SI) significantly positively affect behavioral intention (BI), while facilitating conditions (FC) do not show a significant impact. Additionally, perceived trust is closely related to performance expectancy, effort expectancy, and behavioral intention, negatively impacting perceived risk. Conversely, perceived risk adversely affects behavioral intention. Our findings provide valuable practical insights for developers and operators of AI health assistants.