The Impact of Asnaf Entrepreneur’s Intention Towards Decision and the Movement of Zakat Collection
Bibliographic record
Abstract
Decision is an issue that needs to be explored in detail due to its relationship to an individual’s psychology. In the context of zakat that also acts as microfinancing (non-refundable fund), decision plays an important role in terms of collection and distribution. However, previous literature about zakat has not clarified this phenomenon especially in the context of Asnaf entrepreneurs. Asnaf entrepreneurs represent micro entrepreneurs who depend on zakat funds. Then, by benefiting the funds and getting support from the zakat institutions, Asnaf entrepreneurs become less dependent on the support and become zakat payers. Therefore, grounded by Theory of Planned Behaviour (TPB) framework, the purpose of this paper is to investigate the impact of Asnaf Intention’s towards decision as the zakat payers. A total of 274 Asnaf entrepreneurs from Kelantan and Selangor had participated in this study. Data were collected through self-administered survey questionnaires. The main statistical technique used in this study is Structural Equation Modelling (SEM) by using Analysis of Moment Structure (AMOS) version 23. The study has shown that subjective norms are found to be most dominant predictor towards decision closely followed by attitude, and perceived behavioural control. The above findings have added several implications towards theory, both practical and managerial. In general, the findings of the study are consistent with the theory of planned behaviour. Moreover, Asnaf entrepreneur’s intention gives the better impact towards decision and zakat collection.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".