Cognizant of The Importance of Tax Compliance Intention among Individual Taxpayers in Malaysia
Bibliographic record
Abstract
Previous studies on tax compliance intention by individual taxpayers have been largely empirical, with no consensus among researchers regarding the factors that influence tax compliance. The present research aims to identify the determinants that significantly affect the intention of tax compliance among individual taxpayers in Malaysia to provide valuable insights for policymakers and stakeholders. It seeks to determine the impact of five key elements: tax audit, penalty rate, tax fairness, government spending, and financial position on individual tax compliance. The theory of planned behaviour is used in tax compliance literature and has been applied to form the framework and to develop the hypotheses. A quantitative study was conducted using individual Malaysian taxpayers, and a questionnaire design was utilised to collect data via Google Forms. Out of the 384 questionnaires distributed, only 242 samples of individual taxpayers answered and were usable for this study. The researchers developed and tested five hypotheses. The results suggest that tax audits, penalty rates, and government spending positively impact an individual’s intention to comply with taxes. However, tax fairness and financial position do not affect tax compliance intention. Therefore, three of the five hypotheses were proven correct and supported. The study results are helpful for the Malaysian government and tax authorities in identifying the root cause of non-compliance. Malaysia is facing significant issues related to tax non-compliance, and the tax authority is struggling to investigate the reasons behind non-compliance due to a lack of resources. Identifying the reasons behind tax non-compliance is crucial for policymakers, and this study can help pinpoint the underlying factors contributing to non-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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".