Acceptance of Zakat E-payment System: A Perception of Undergraduates
Bibliographic record
Abstract
Zakat E-payment is one of the product innovations that is a widely spread method to facilitate the contribution of zakat in the present world. Online payment is an important method of transaction widely practiced across the globe. Due to its flexibility and convenience, it has been a popular payment method recently, especially in critical religious matters such as zakat, one of the pillars of Islam. The main objective of the study is to analyse the acceptance of the zakat e-payment system from the perception of undergraduates as they are potential payors in the future as they enter the workforce. Specifically, factors influencing the acceptance of zakat e-payment will be determined. The study’s conceptual framework is based on the Theory of Acceptance (TAM), where six variables or constructs, namely financial literacy (FL), perceived usefulness (PU), Perceived Ease of Use (PEU), Enjoyment (ENJ), Attitude (ATT) and Behavioural Intention (BI). This cross-section study employs a data set comprising 210 undergraduate students. Data was collected using a close-ended questionnaire. The results, among others, recommend that zakat e-payment system providers modify or create highly usable applications. Therefore, marketing initiatives should focus on promoting these zakat e-payment system characteristics.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".