Understanding Initial Trust in AI Financial Chatbots: Integrating Diffusion of Innovation Theory with AI Design Characteristics

Artificial intelligence (AI)-powered financial chatbots are increasingly transforming customer interaction within the FinTech ecosystem by providing automated financial advisory services, personalized recommendations, and real-time financial assistance. Despite their growing adoption, concerns regarding transparency, technological uncertainty, and trustworthiness continue to hinder users’ willingness to rely on AI-enabled financial systems. Drawing upon Computers Are Social Actors Paradigm (CASA) and Diffusion of Innovations (DOI) theory, the present study proposes a conceptual framework to explain the formation of initial trust in AI financial chatbots through AI design characteristics. Specifically, the study examines the influence of perceived explainability, perceived anthropomorphism, perceived autonomy, and interactivity on innovation perceptions, namely relative advantage, compatibility, and complexity. Further, the study investigates how these innovation perceptions shape customers’ initial trust toward AI financial chatbots, which subsequently influences chatbot usage intention and customer engagement. In addition, the study incorporates AI anxiety as a moderating variable to examine whether users’ anxiety toward AI technologies weakens the relationship between initial trust and usage intention. The proposed framework contributes to the emerging literature on AI-enabled financial services by integrating AI-specific design characteristics with innovation diffusion and social response theories in the context of trust formation and technology adoption. The study also offers practical implications for FinTech firms and financial institutions in designing transparent, trustworthy, and engaging AI chatbot systems capable of enhancing customer adoption and long-term engagement with digital financial services.