How Does Information Interactivity Promote Customer Trustiness and Positive WOM in AI-Powered Chatbots? Examining Significant Roles of Perceived Values and Active Involvement

The advancement in artificial intelligence (AI)-powered automation has accelerated the integration of AI-powered chatbots into our daily routines, opening novel channels for dynamic information flow and participatory dialogue. Whilst prior studies have examined chatbot interactivity and related outcomes, the mechanism through which information interactivity is translated into relational and advocacy outcomes remains insufficiently theorized, and its conceptual demarcation from active involvement remains underdeveloped. Grounded in Uses and Gratifications (U&G) theory, this study develops and tests a process model of AI-powered chatbot use. In this model, information interactivity is treated as an AI-powered communicative affordance, perceived value represents the mechanism through which gratifications are realized, and active involvement is conceptualized as a situational psychological state that influences customer trustiness and positive word-of-mouth (WOM). Using structural equation modeling on survey data from 588 AI-powered chatbot users, the study finds that information interactivity positively predicts functional, psychosocial, and hedonic value, all of which significantly enhance active involvement. Active involvement, in turn, exerts a significant positive effect on customer trustiness, and customer trustiness significantly promotes positive WOM. By contrast, the direct effect of active involvement on positive WOM is not significant, suggesting that trustiness functions as the more proximal mechanism through which involvement is translated into advocacy. These findings contribute to research grounded in U&G theory by demonstrating how functional, psychosocial, and hedonic value link chatbot interactivity to relational and advocacy outcomes. They also suggest several practical considerations for the development of chatbot services that are more responsive to users’ expectations and trustiness formation.