Digital zakat management, transparency in zakat reporting, and the zakat payroll system toward zakat management accountability and its implications on zakat growth acceleration
Bibliographic record
Abstract
This study examines the impact of Digital Zakat Management, Transparency in Zakat Reporting, and the Zakat Payroll System on Zakat Growth Acceleration, with Zakat Management Accountability as the mediating factor. Data from muzakki and Zakat institutions were collected through surveys and questionnaires, and regression and correlation analyses were used to assess the relationships between variables. The findings reveal that digital Zakat management positively impacts Zakat management accountability and Zakat growth acceleration. Transparency in Zakat reporting also has a positive effect on Zakat management accountability. However, the Zakat payroll system does not significantly influence Zakat management accountability or Zakat growth acceleration through mediation. The study underscores the importance of accountability as a mediator between digitalization, transparency, and Zakat growth, offering insights for Zakat institutions to optimize their practices and promote equitable social impact. Future research should consider qualitative methods and comprehensive variables to understand Zakat dynamics better.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".