Trust in Human-Chatbot Interaction: An Integrated Model of Performance Expectancy and Decision Making

Abstract

Abstract

DOI : https://doi.org/10.82471/gfvpd-sfd94

The communicative behaviour of users has changed with the evolution and advancements of technology, significantly altering the dynamics of human machine interactions. The rapid integration of chatbots into everyday life, particularly in healthcare, has intensified the need to understand the role of trust in human–computer interaction. While existing models of technology adoption—such as the Unified Theory of Acceptance and Use of Technology (UTAUT) and the Technology Acceptance Model (TAM)—emphasize performance expectancy and ease of use, they often overlook the socio-psychological construct of trust that shapes users’ willingness to depend on conversational agents. This study integrates UTAUT, TAM, and the Human–Computer Trust Model (HCTM) to examine how trust influences performance expectancy and decision-making in human–chatbot communication.

A web-based survey of 255 users aged 18–45 in South Asia was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results demonstrate that trust has a strong and direct positive effect on both performance expectancy and decision-making. Model evaluation indicated robust predictive power (R² values above 0.89) and acceptable fit indices (SRMR = 0.042, NFI = 0.929). These findings highlight trust as a central determinant in chatbot adoption, shifting the discourse from purely functional acceptance to relational and affective dynamics. By empirically validating trust as a key predictor of technology use outcomes, this study extends current acceptance models and provides actionable insights for the design of reliable, transparent, and ethically responsible chatbots. The findings have particular relevance for healthcare and other high-stakes domains, where user trust is indispensable for effective decision support and engagement.

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