Okay Google, Good to Talk to You... Examining the Determinants Affecting Users' Behavioral Intention for Adopting Voice Assistants: Does Technology Self-Efficacy Matter?

This study investigates the impact of attitude and trust on the behavioral intention of adopting artificial intelligence (AI)-based voice-assistant services through the integration of parasocial relationship theory (PSR) and human–computer interaction (HCI) theory. Employing a deductive approach, partial least squares structural equation modeling (PLS-SEM) was performed to assess the proposed model based on 295 participants by using a purposive sampling method. Findings revealed that the functional components significantly impacted users' attitudes, while social attributes, such as social cognition and electronic word of mouth (e-WOM), were revealed to be critical constituents in driving users' trust. Moreover, technology self-efficacy (TSE) showed a moderating impact on the relationship between trust and behavioral intention to use voice-assistant services. Based on the findings, the study provides robust and meaningful insights for managers and designers to foster trust and form a positive attitude among the users. Additionally, the outcome of the study adds to the body of research on AI-based technology adoption and interactions by investigating the antecedents of trust and attitudes towards voice-assistant services. [ABSTRACT FROM AUTHOR]