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Digital Government and the Structural Determinants of Political Participation Among Street-Level Bureaucrats in China

Beichen Lu et al.

International Journal of Electronic Government Research2025https://doi.org/10.4018/ijegr.388562article
ABDC C
Weight
0.37

What the paper says

This study examines the determinants of institutional political participation among street-level bureaucrats in China within the context of digital government transformation, guided by an integrated theoretical framework combining Resource Mobilization Theory and Political Opportunity Structure Theory. These theories help explain how both the availability of resources and the broader institutional opportunities influence participation behaviors. Using CGSS 2021 data, which employs a stratified multi-stage probability sampling method to ensure nationwide representativeness, the research assesses how sociodemographic, socioeconomic, and digital factors impact voting behavior and broader political engagement among bureaucrats. The study highlights that digital participation and digital skills significantly increase the likelihood of institutional engagement, particularly among younger bureaucrats. In contrast, those with higher socioeconomic status are less active in traditional voting but more engaged through digital channels.

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https://doi.org/https://doi.org/10.4018/ijegr.388562

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@article{beichen2025,
  title        = {{Digital Government and the Structural Determinants of Political Participation Among Street-Level Bureaucrats in China}},
  author       = {Beichen Lu et al.},
  journal      = {International Journal of Electronic Government Research},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.4018/ijegr.388562},
}

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Digital Government and the Structural Determinants of Political Participation Among Street-Level Bureaucrats in China

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Evidence weight

0.37

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.16 × 0.4 = 0.06
M · momentum0.53 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.