The effect of e-Servqual and public service on community satisfaction: An empirical study in government organization
Bibliographic record
Abstract
The purpose of this study is to analyze the relationship between public service and community satisfaction as well as the relationship between e-service quality and community satisfaction in government organizations. The study uses quantitative descriptive research. In addition, the study also collects data to test hypotheses or to answer questions related to the variables to be studied. The study uses a data collection technique questionnaire method which is given to respondents whose contents are in the form of written statements related to the research object, namely public services, e-service quality and community satisfaction. The population studied in this study is the community. The number of samples in this study is 470 people. The study uses a purposive sampling technique. Purposive sampling is a sampling technique with considerations that meet the criteria. The criteria are people who have downloaded and used internet-based service applications provided by the Government. Variable measurement is based on a Likert scale. Each respondent's answer choices are given a score of values arranged in stages based on a Likert Scale arranged as follows: Strongly agree (5), agree (4), Neutral (3), disagree (2) and strongly disagree (1). The data analysis technique in this study uses the Structural Equation Modeling (SEM) analysis tool from the IBM SPSS AMOS 26 statistical software package in the model and hypothesis testing. The stages of structural equation modeling and analysis are divided into seven steps, namely: (1) theoretical model development, (2) compiling a path diagram, (3) converting a path diagram into a structural equation, (4) choosing an input matrix for data analysis, (5) assessing the identification of the model, (6) evaluating the estimation of the model, (7) interpretation of the model. Based on the results of the analysis, the results show that there is a significant positive effect of public service on community satisfaction in government organizations, and there is a significant positive effect of e-service quality on people's satisfaction in government organizations. Government leaders should pay attention to the factors that are considered to influence community satisfaction. Such as paying attention to service procedures for the community, improving procedures for service, including during the process of complaints, criticism and suggestions from the community, then paying attention to the time the service process is carried out, since efficiency in solving community problems also contributes to the comfort or satisfaction felt by the community.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".