The effects of e-government, e-billing and e-filing on taxpayer compliance: A case of taxpayers in Indonesia
Bibliographic record
Abstract
The purpose of this study is to analyze the effects of the application of e-government, e-billing and e-SPT on taxpayer compliance. This type of research is quantitative research. The variables in this study consist of one dependent variable and three independent variables. The dependent variable is taxpayer compliance, while the independent variables are application of e-government, application of e-billing, and application of e-filing (e-SPT). The population in this study is Indonesian taxpayer. The sample in this research was 430 respondents who filled out the Likert Scale questionnaire. The sampling technique in this study was incidental sampling, with research instruments using online questionnaires distributed via social media. The data analysis technique in this study used a structural equation model (SEM) with SmartPLS 3.0 software. The results of this study indicate that the application of e-government had a positive and significant effect on taxpayer compliance, the application of e-billing had a positive and significant effect on taxpayer compliance, and the application of e-SPT had a positive and significant effect on taxpayer compliance.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".