Role of interaction quality and trust in use of AI-based voice-assistant systems:

Purpose: The rapid growth of artificial intelligence (AI)-based voice-assistant systems (VASs) has created many opportunities for individuals to use VASs for various purposes in their daily lives. However, traditional quality success factors, such as information quality and system quality, may not be sufficient in explaining the adoption and use of AI-based VASs. This study aims to propose interaction quality as an additional, yet more important quality measure that leads to trust in an AI-based VAS and its adoption. Design/methodology/approach: The authors propose a research model that highlights the importance of interaction quality and trust as underlying mechanisms in the adoption of AI-based VASs. Based on survey methodology and data from 221 respondents, the proposed research model is tested with a partial least squares approach. Findings: The results suggest that interaction quality and trust are critical factors influencing the adoption of AI-based VASs. The findings also indicate that the impacts of traditional quality factors (i.e. information quality and system quality) occur through interaction quality in the context of AI-based VASs. Originality/value: This research adds interaction quality as a new quality factor to the traditional quality factors in the information systems success model. Further, given the interactive nature of VASs, the authors use social response theory to explain the importance of the trust mechanism when individuals interact with AI-based VASs.