The role of mGovernment applications in building trust during public crises: Evidence from the COVID-19 epidemic
Bibliographic record
Abstract
Throughout the COVID-19 pandemic, China effectively contained the virus, resulting in increased public trust in the government. Mobile government (mGovernment) applications (apps) played a critical role in this improvement. This study aims to examine how mGovernment apps build citizens' trust in governments during public crises. The DeLone and McLean Information Systems Success Model (ISSM) is used to evaluate the quality of these apps. Public satisfaction serves as an intermediary variable, while public awareness of epidemic risks in sudden public relations crises acts as a moderating variable to explore the impact of mGovernment apps on government trust. Data analysis is conducted using SPSS Statistics 22.0 and AMOS 21.0. The study's results show that the system quality, information quality, and service quality of mobile government apps influence citizens' trust in governments through the mediating effect of public satisfaction. All three factors positively correlate with public satisfaction, with service quality having the greatest impact. Similarly, system quality, information quality, and service quality are positively correlated with public trust in governments, with system quality having the most noticeable influence. There is a strong correlation between public satisfaction and trust in governments, and the mediating effect of public satisfaction is significant. In addition, epidemic risk perception moderates the relationship between public satisfaction and citizens' trust in governments.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".