ArticleScientific reports2026
Examining public acceptance intentions for government digital human through expectation confirmation and technology acceptance models.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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5 authors.
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Abstract
Government digital humans represent a significant application of the combination of generative artificial intelligence and digital governance. This study aims to explore the factors influencing public acceptance of government digital humans, providing insights for enhancing their services and user experience. By integrating expectation confirmation theory (ECT) and the technology acceptance model (TAM), this study expands upon ECT by incorporating additional user perception factors. A structural equation model is employed to explain the influencing factors and their interrelationships, while a mediation effect model tests the mechanisms of these factors. The findings indicate that public expectation confirmation enhances satisfaction with government digital humans, which positively impacts acceptance intention. Perceived information quality, perceived intelligence, perceived convenience, perceived attractiveness, perceived usefulness, and artificial intelligence trust collectively mediate the relationship between public expectation confirmation, satisfaction, and acceptance intention. The findings highlight the importance of enhancing the perceived usefulness, perceived intelligence, perceived information quality, and perceived attractiveness of government digital humans. Relevant departments should focus on improving these attributes to optimize digital government services and enhance user experience and utilization rates.
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