Dual-process trust formation in travel chatbots: a heuristic-systematic model of cognitive and affective pathways

Purpose Through the integration of the heuristic-systematic model (HSM) and the trust-building model, this study aims to investigate the intricate mechanisms through which online travel chatbots’ (OTCs) characteristics influence trust formation. This study specifically examines how systematic and heuristic processing routes distinctly shape cognitive and affective trust development in human–AI (artificial intelligence) interactions, addressing fundamental gaps in digital trust literature. Design/methodology/approach The investigation uses a systematic research design that systematically examines user interactions with OTC platforms. Three hundred participants engaged in structured travel planning scenarios, providing a robust data set for analyzing trust formation patterns. The methodology incorporates both controlled exposure and naturalistic interaction elements, enabling systematic examination of systematic processing (via communication quality and trendiness assessment) and heuristic processing (through anthropomorphic features and interaction enjoyment). Structural equation modeling techniques, coupled with serial mediation analysis, were used to test the theoretical framework. Findings The results reveal that OTC characteristics directly influence both affective and cognitive trust components. Communication quality and trendiness significantly impact cognitive trust, while anthropomorphism and interactional enjoyment influence affective trust. Both trust components strongly affect reuse intention, but their impact on word-of-mouth varies, with cognitive trust showing stronger direct effects. Research limitations/implications This study establishes a comprehensive framework for understanding trust formation in AI-powered travel services, offering a foundation for future research in human–agent trust dynamics. Practical implications The findings enable OTC developers to optimize trust-building features and help travel companies enhance user adoption through targeted implementation of trust-inducing characteristics. Originality/value This research advances theoretical understanding in three distinctive ways. First, it extends HSM application in digital environments by delineating how systematic and heuristic processing manifest uniquely in OTC interactions. Second, it enriches trust-building theory by revealing the distinct mediating roles of cognitive and affective trust in human–AI exchanges. Third, it establishes a comprehensive framework for understanding trust formation in AI-powered services, particularly in high-involvement decision contexts such as travel planning. These insights reveal patterns of trust development specific to human–AI interactions that diverge from traditional human–human trust formation mechanisms documented in earlier studies.