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From institutional trust to AI adoption: a trust transfer and risk perception model of AI-enabled public service
1School of Law and Public Administration, Hunan University of Science and Technology, Xiangtan, China.
Introduction:
Artificial intelligence (AI) is increasingly used in public agencies to route inquiries, screen eligibility, support caseworkers, and automate routine service encounters. Citizen acceptance of these services depends on their links to public authority, accountability, and visible opportunities for human recourse. This study examines a trust-based mechanism connecting institutional trust, risk perception, AI service trust, and behavioral intention in China's digital government context.
Methods:
The study combined an LLM-driven agent simulation involving 936 agents across three independent seeds, a 3 × 3 factorial scenario experiment involving 900 simulated agents, and a human-validation pilot using the same questionnaire and scenario structure. The pilot generated 189 submitted records, of which 182 were retained after attention checking.
Results:
In the synthetic calibration, institutional trust is positively associated with AI service trust (IT → AST β = 0.607) and negatively associated with risk perception (IT → RP β = -0.271); risk perception is negatively associated with AI service trust (RP → AST β = -0.459); and AI service trust is positively associated with behavioral intention (AST → BI β = 0.424). The same directional pattern appears in the human-validation pilot (IT → AST β = 0.357; IT → RP β = -0.240; RP → AST β = -0.513; AST → BI β = 0.650). Scenario means also align with the simulation pattern (Pearson r = 0.803 for AST and r = 0.875 for BI across the nine cells), with the lowest pilot AST (3.667) and BI (3.413) in the fully automated high-risk condition.
Discussion:
The findings connect confidence in government institutions with service-specific trust and indicate that perceived risk constrains acceptance of AI-enabled public services. In high-stakes automated settings, visible arrangements for human review may be necessary for AI service trust to translate into intended use. Public-sector AI acceptance is therefore shaped jointly by institutional credibility, perceived risk, and service encounter design.