The role of e-billing and e-SPT implementation on user satisfaction of e-filing taxpayers
Bibliographic record
Abstract
The study investigates the effects of system quality and service quality on e-filing user satisfaction as well as the effect of information quality on e-filing user satisfaction through quantitative research. The variables in this study consist of the dependent variable, namely e-filing user satisfaction, while the independent variables are system quality, information quality, and service quality. The population in this study is the taxpayer. The sample in this study includes 340 taxpayer respondents in Indonesia who were calculated using the Slovin formula, with the research instrument in the form of a questionnaire measured using a Likert scale 1 to 7. The sample collection technique in this study uses the incidental sampling method, with the research instrument using an online questionnaire distributed via social media. The sample collection method in this study used incidental sampling. The data analysis technique in this study used structural equation modeling (SEM) with SmartPLS 3.0 software tools. The results of this study indicate that system quality has a positive effect on e-filing user satisfaction. Information quality also has a positive effect on e-filing user satisfaction. Finally, service quality has a positive effect on e-filing user 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 | 0.002 | 0.014 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".