Elaborate or Succinct? The Impact of AI Chatbots’ Language Style on Customers’ Satisfaction in Online Service

The growing prevalence of AI-powered chatbots in digital service environments has raised user expectations from mere functional efficiency to emotionally satisfying interactions. Drawing on Language Expectancy Theory (LET), this study investigates the impact of AI chatbot language style (namely, elaborate vs. succinct language) on customer service satisfaction. Across three studies, we demonstrate that customers exhibit higher satisfaction when interacting with chatbots employing elaborate language as opposed to succinct language. Furthermore, this effect is mediated by warmth and moderated by customer relationship norm orientation. The influence of elaborate language is more pronounced among customers with communal relationship norms, whereas those with exchange relationship norms respond more favorably to succinct language. Theoretically, this study enriches the literature on language style in human–computer interaction by introducing elaborateness as a pivotal communicative dimension. Practically, our results offer strategic guidance that can help service providers and developers to strategically tailor chatbot language styles to distinct customer segments, consequently enhancing service quality, fostering emotional engagement, and cultivating long-term customer loyalty within automated service systems.