How Symbolic Representation and Emotional Labor Shape Citizens’ Burden Tolerance in Public Service Encounters: Evidence from China

The growing use of AI agents in citizen–government interactions challenges human-centered theories of representative bureaucracy. Drawing on symbolic representation and emotional labor, this study examines how service agents’ identity cues and emotional displays in public service encounters shape citizens’ tolerance of administrative burdens. This study employs a 2 (agent type: human bureaucrat vs. humanized chatbot) × 2 (agent gender: male vs. female)×2 (emotional labor: present vs. absent) between-subjects vignette experiment in China. Specifically, the study seeks to address three research questions: (1) Does symbolic representation, operationalized through agent type and agent gender, affect citizens’ burden tolerance? (2) Does the display of emotional labor by service agents influence citizens’ burden tolerance? (3) Does emotional labor moderate the relationship between symbolic representation and citizens’ burden tolerance? This research aims to extend the theoretical boundaries of representative bureaucracy in the digital age by examining the symbolic implications of non-human, technological agents. It further theorizes emotional labor as both a positive driver of citizens’ burden tolerance and a substitutive encounter cue that reduces reliance on representational and interface-based heuristics.