Privacy and empathy in AI chatbots: A dual-route analysis of cognitive and emotional trust in financial services

Abstract

Trust remains a critical barrier to the adoption of artificial intelligence (AI) chatbots in financial services, yet extant research has not systematically examined how distinct communication strategies differentially shape cognitive and emotional trust. Grounded in Dual Process Theory and enriched by Theory of Mind, this study investigates how privacy communication and empathetic language influence users’ trust formation through complementary cognitive (System 2) and affective (System 1) pathways. Privacy communication is theorized as a structural assurance that activates analytical evaluation of the chatbot’s competence and integrity, whereas empathetic language triggers intuitive perceptions of warmth and benevolence aligned with the experiential dimension of mind perception. A 2 × 2 between-subjects experiment (N = 163) using a GPT-based investment chatbot in a simulated banking scenario reveals that privacy communication significantly enhances cognitive trust (F = 15.43, p < .001, η2 = .088), while empathetic language significantly increases emotional trust (F = 15.76, p < .001, η2 = .090). Both factors independently strengthen overall trusting beliefs, with no significant interaction effects, confirming an additive dual-pathway model. These findings advance dual-process trust theory in AI-mediated financial services and offer actionable interface-design insights for banking executives seeking to build user confidence through complementary rational and relational trust mechanisms.