The effect of e-service quality on user satisfaction and loyalty in accessing e-government information
Bibliographic record
Abstract
Digitization has had a profound impact on changing consumer behavior and the reorientation of online services by service providers in both the public and private sectors. This includes the use of information and communication technology and the internet adopted in the public sector largely known as e-government, which intensifies the use of websites to bridge the relationship between public institutions and users. The purpose of the study was to analyze the effect of e-service quality on user loyalty through user satisfaction of public service websites. The study was conducted on 250 users of public service websites in Indonesia. The analytical tool used is Structural Equation Modeling with the help of AMOS software. The study found that the quality of e-service has a significant effect on user satisfaction and user loyalty, user satisfaction has a significant effect on user loyalty, and user satisfaction partially mediates the effect of e-service quality on user loyalty. The results of the study underscore the importance of improving the quality of e-government through e-quality services, especially in government organizations to provide opportunities for the public and the private sector to access government services with integrated services efficiently through the use of the internet and online channels.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".