Lower than expected but still willing to use: User acceptance toward current intelligent conversational agents

Abstract

Intelligent conversational agents (ICAs) are revolutionizing how humans interact with information systems. Designed to provide human-like service, ICAs are generally evaluated by users in comparison to their human counterparts, often resulting in less-than-expected user experiences. Our research investigates user acceptance of ICAs in this suboptimal condition of commercial customer service. Drawing from the dual perspectives of expectancy confirmation theory and task technology fit theory, we theorize and test an integrated research model on the collective impact of user expectancy confirmation regarding ICA capabilities and their assessment of service-ICA fit on user acceptance. Results from a field survey of 350 users of five ICAs deployed by major Canadian telecom service providers reveal the significant influence of both user expectancy confirmation with ICA capabilities and their assessment of ICA fit-to-service, with the latter playing a more prominent role in shaping user acceptance. Even though ICA performance may not always meet user expectations, users are still willing to engage with ICA services when they perceive the ICA as a fitting solution for their specific service complexity and availability requirements.