The impact of e-service quality on public trust and public satisfaction in e-government public services
Bibliographic record
Abstract
Responsive, timely, and friendly service quality plays a central role in shaping trust between the government and citizens. With the improvement of service quality, the public feels valued and heard, reinforcing the mutual trust relationship between the government and citizens. In this regard, technology-enabled service processes can streamline time and cost, while automation reduces the risk of human errors. Through web platforms or applications, the government can provide easier access for citizens to various services without the need to physically visit government offices. Good and quality public services are not only aimed at meeting the practical needs of the public but also play a role in shaping the mutual trust relationship between the government and citizens. Therefore, the concept of e-service quality, which encompasses the quality of services provided through electronic platforms, becomes crucial. The objective of this research is to explore the extent to which e-service quality can influence the level of public satisfaction. The research method employs a quantitative approach using primary data sources, where random sampling is applied as the sampling technique. The research respondents are citizens using digital public service platforms organized by the local government of Jakarta. The sample size used in this study is 262. The variables tested in this research involve e-service quality, public trust, and public satisfaction. In analyzing the data, this research utilizes SmartPLS 4 software. The analysis results show that e-service quality has a significant influence on the formation of public trust. Furthermore, findings indicate that e-service quality also significantly affects public satisfaction. However, the analysis results do not support the idea that public trust mediates the relationship between e-service quality and public satisfaction. This signifies that while public trust directly contributes to public satisfaction with public services, other unmeasured factors also play a role in shaping public perceptions and satisfaction.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".