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

Yifan Song, Takashi Natori () and Xintao Yu
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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

Administrative Sciences, 2025, vol. 15, issue 8, 1-28

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.

Keywords: government chatbots (GCs); trust of government; perceived risk; trust transfer theory; algorithm aversion; e-government services (search for similar items in EconPapers)
JEL-codes: L M M0 M1 M10 M11 M12 M14 M15 M16 (search for similar items in EconPapers)
Date: 2025
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