To pay or not to pay: Measuring the effect of religiosity in the ABC theory
Bibliographic record
Abstract
This study identifies the role of religiosity in the willingness to pay for halal transportation among Muslim consumers in Malaysia by applying the ABC theory. Applying a purposive sampling method, data were gathered from questionnaires distributed to Muslim consumers at malls in Kuala Lumpur and Putrajaya. From 250 Muslims who were approached, 200 respondents agreed to answer the questionnaire. SMART-PLS 3.3.2. was used to analyse the data for this study using a Structural Equation Modelling (SEM) approach. Out of six direct hypotheses tested, five hypotheses were found supported. From four hypotheses on mediation, only one was found as unsupported. Religiosity was found to have a moderation effect between knowledge and the WTP for halal transportation. The findings provide useful information on the WTP for halal transportation. Related parties such as governments, halal transport service providers and customers can use these findings to plan further actions in order to enhance the WTP for halal transportation. The study reveals the capability of the ABC theory to identify the factors of the WTP for halal transportation among Muslim consumers in Malaysia. The findings also show the moderation effects of religiosity on the WTP for halal transportation. The study also incorporates awareness as a mediator and as a sequential mediator within the model. The findings also enrich the literature on the WTP in halal studies.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".