The Impact of AIGC's Emotional Expression on Consumer Service Evaluation in Service Failure Contexts: The Mediating Role of Expectation Disconfirmation

Abstract

With the widespread application of generative artificial intelligence (AIGC) in the service sector, its performance in service failure scenarios has become a key focus for both academia and practice. Based on Expectation Disconfirmation Theory, this study employs a scenario-based experiment to investigate the impact of AIGC’s level of emotional expression (high vs. low) on consumer service evaluations in service failure contexts, as well as the underlying mechanism. The results show that compared to AIGC with low emotional expression, AIGC with high emotional expression triggers more negative service evaluations from consumers when a service failure occurs. Further mechanism analysis reveals that expectation disconfirmation plays a key mediating role: high emotional expression elevates consumers’ initial expectations, and when a service failure occurs, the significant expectation gap (i.e., negative disconfirmation) is the fundamental reason for the decline in service evaluations. This study reveals the "double-edged sword" effect of AIGC’s anthropomorphic strategy in service failure contexts, deepens the understanding of the boundary conditions of emotional expression in human-computer interaction, and provides important theoretical and practical insights for enterprises to optimize AIGC service design and implement effective expectation management and service recovery.