Young muslim generation’s preferences for using digital platforms for Zakat payments: A cross-country study of Indonesia and Malaysia
Bibliographic record
Abstract
The young Muslim generation’s embracing digital platforms for Zakat payments represents a dynamic fusion of enduring religious values with the modern digital landscape, heralding a new era in Islamic charitable practices. This trend illustrates a profound transformation within the Islamic world, where the pillars of faith are being reimagined and revitalized through the lens of technological advancement. The present study delved into the factors influencing the young Muslim generation’s preference for digital platforms in Zakat transactions across Indonesia and Malaysia. We examined variables such as Performance Expectancy, Effort Expectancy, Social Influence, Trust, Zakat Literacy, and Digital Infrastructure, aiming to discern their impact on the propensity for digital Zakat contributions with the extension of Unified Theory of Acceptance and Use of Technology (UTAUT) model. The research encompassed a diverse sample of 382 participants and utilized advanced methodologies, specifically Partial Least Squares Structural Equation Modeling (PLS-SEM) and PLS Multi Group Analysis (PLS-MGA), for rigorous data analysis. The results indicated that Effort Expectancy, Social Influence, Digital Infrastructure, and Zakat Literacy notably influenced the use of digital platforms for Zakat. Furthermore, PLS-MGA uncovered significant cross-country differences where Digital Infrastructure showed a more pronounced positive impact in Malaysian context, whereas Social Influence had a greater effect in Indonesia. These findings offer critical insights into the young Muslim community’s digital engagement for religious financial obligations, underscoring the need for tailored digital Zakat solutions that cater to the unique preferences of this demographic. This research not only enriches the understanding of digital adoption in religious practices but also challenges the notion of a universal approach, advocating for context-specific strategies in the realm of digital religious financial services. Future researchers are suggested to consider longitudinal investigations as well as examining cross-regional contexts in this realm of research.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".