Do Enforcement, Religiosity and Peer Influence Zakah Compliance Behavior?
Bibliographic record
Abstract
Although Zakah (Islamic tax) is considered to be a cornerstone of Islamic social system and mechanism for eradicating poverty among Muslim communities, the realization of noble socio-economic objectives of Zakah in most Muslim countries have so far remained a mirage. They are unable to bring out the destitute poor from the poverty trap and help the oppressed to be a self-reliant as possible. Low Zakah collection is one of the most crucial reasons behind this phenomenon. Yemen, for instance, is one of the poorest low-income countries in the world. In such a hard situation, Zakah has failed to appropriately fit as a fiscal instrument in fighting the plight of poverty. Therefore, this study aims to examine the determinants of Zakah payers’ decision to comply with Zakah laws. A Survey questionnaire was administered to 500 business owners (Zakah payers) out of which 274 usable questionnaires for further analysis. Based on PLS-SEM outcomes, the study revealed that Islamic religiosity and peer influence are significantly related to business Zakah compliance, while law enforcement had no influence on compliance. The findings are relevant to Zakah authorities in Yemen and Muslim countries to focus their attention on formulation of policies to further boost Zakah collection.
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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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".