Understanding virtual agents’ service quality in the context of customer service: A fit-viability perspective

Abstract

Virtual agents, a prevalent application of artificial intelligence, significantly enhance e-commerce customer service (i.e., online interactions that address customers’ concerns). This creates an innovative service model aiming to meet diverse customer demands and create value for enterprises. However, understanding the factors driving the performance of virtual agents, especially in terms of service quality, remains a gap. Expanding the fit-viability model (FVM), this study investigates the drivers and mechanisms influencing virtual agents’ service quality, considering the matching of technology, service scenarios, and individual readiness. Surveying 399 respondents experienced in virtual agent service within e-commerce, the findings reveal that personalization negatively impacts response speed but positively influences response accuracy. Technology readiness positively affects convenience and accessibility. Overall, service quality is positively influenced by response speed, response accuracy, convenience, and accessibility. This study extends the fit-viability model to virtual agent service literature, providing practitioners with a fresh perspective to enhance e-commerce customer service.