PurposeThe 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/approachThe 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. FindingsThe 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/valueThis 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. Contribution to Impact

This research investigates how competition intensity and differences in cost structures affect decisions made by competing suppliers and the role that behavioral factors play as influences. We use controlled laboratory experiments to study the scenario of suppliers competing for a share of demand being outsourced by a single buyer. The buyer seeks to maximize the service level provided by suppliers by allocating based on different performance measures which create varying levels of competition intensity. The experimental treatments include those performance measures as well as differences in supplier cost structures. Our experimental results show that in the majority of cases suppliers' decisions do not confirm theoretical predictions from the Nash equilibrium, and we find patterns in those deviations. To explain them, we first evaluate behavioral factors found in the literature including bounded rationality, learning, and other-regarding behavior. We then introduce a new behavioral factor, rival-chasing. Rival-chasing builds on other-regarding behavior by considering competitors' actions in addition to their outcomes. We find that rival-chasing can explain patterns in suppliers' behavior that cannot be explained by other behavioral factors.