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Trusting Humans or Bots? Examining Trust Transfer and Algorithm Aversion in China’s E-Government Services

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  • Yifan Song

    (Graduate School of Technology Management, Ritsumeikan University, Osaka 567-8570, Japan)

  • Takashi Natori

    (Graduate School of Technology Management, Ritsumeikan University, Osaka 567-8570, Japan)

  • Xintao Yu

    (School of Economics and Management, Liaoning University of Technology, Jinzhou 211189, China)

Abstract

Despite the increasing integration of government chatbots (GCs) into digital public service delivery, their real-world effectiveness remains limited. Drawing on the literature on algorithm aversion, trust-transfer theory, and perceived risk theory, this study investigates how the type of service agent (human vs. GCs) influences citizens’ trust of e-government services (TOE) and e-government service adoption intention (EGA). Furthermore, it explores whether the effect of trust of government (TOG) on TOE differs across agent types, and whether perceived risk (PR) serves as a boundary condition in this trust-transfer process. An online scenario-based experiment was conducted with a sample of 318 Chinese citizens. Data were analyzed using the Mann–Whitney U test and partial least squares structural equation modeling (PLS-SEM). The results reveal that, within the Chinese e-government context, citizens perceive higher risk (PR) and report lower adoption intention (EGA) when interacting with GCs compared to human agents—an indication of algorithm aversion. However, high levels of TOG mitigate this aversion by enhancing TOE. Importantly, PR moderates the strength of this trust-transfer effect, serving as a critical boundary condition.

Suggested Citation

  • Yifan Song & Takashi Natori & Xintao Yu, 2025. "Trusting Humans or Bots? Examining Trust Transfer and Algorithm Aversion in China’s E-Government Services," Administrative Sciences, MDPI, vol. 15(8), pages 1-28, August.
    • Handle: RePEc:gam:jadmsc:v:15:y:2025:i:8:p:308-:d:1718563
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