Indonesian Taxpayers' Compliance: A Meta-Analysis
Bibliographic record
Abstract
This research examines the effect of six independent variables: tax socialization, tax knowledge, tax awareness, tax service quality, tax sanction, and tax audit, on the compliance of Indonesian taxpayers through a meta-analysis of previous researches on Indonesian taxpayers' compliance from 2006 to 2016. The purpose of this research is to strengthen the findings of previous research that the variables above affect Indonesian taxpayers’ compliance. This research obtains empirical evidence that all of the variables above do affect Indonesian taxpayers’ compliance. With using meta-analysis approach, it is concluded that tax socialization, tax knowledge, tax awareness, tax service quality, tax sanction, and tax audit have both positive and significant relationship either for individual or institutional tax payer’s compliance. Theoretically, this research enriches the literature and gives a conclusion for previous researches about tax compliance in Indonesia, which results were varied. Practically, this research gives a clearer picture to the government about factors (tax socialization, tax knowledge, tax awareness, tax service quality, tax sanction, and tax audit) that could be improved to increase tax compliance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.024 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.020 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".