The role of religiosity, product knowledge and product assessment on digital purchasing of halal products
Bibliographic record
Abstract
The study surveys the effects of religiosity and Knowledge of halal products on both product assessment and digital purchasing interest. The study also investigates the effects of religiosity and halal product assessment on digital product purchase interest. This research method is to use quantitative methods to test the relationship between dependent and independent variables. The population of this study is consumers of halal products and the sample of this study is 564 consumers of halal products determined by the simple random sampling method. The research analysis uses the structural equation modeling partial least squares (SEM-PLS) method and uses data processing tools with SmartP LS 4.0 software. The research questionnaire contains statement items using a 7-point Likert scale, namely (1) strongly disagree, (2) disagree, (3) somewhat disagree, (4) neutral, (5) somewhat agree, (6) agree and (7) strongly agree. The independent variables of this research are Religiosity, Knowledge of halal products, and Product assessment and the dependent variable is digital purchasing interest. The stages of research data analysis are the outer model test including reliability and validity tests and the inner model test including termination tests and hypothesis tests. Based on the results of research data analysis, it can be concluded as follows: Religiosity has a positive and significant effect on the assessment of halal products, meaning that the higher the religiosity obtained by customers, the higher the assessment of halal products. Knowledge of halal products has a positive and significant effect on the assessment of halal products, meaning that the higher the knowledge of halal products, the higher the assessment of halal products. Religiosity has a positive and significant effect on interest in buying halal products, meaning that the higher the religiosity obtained by customers, the interest in buying halal products will increase. Knowledge of halal products has a positive and significant effect on buying interest, meaning that the higher the customer's perceived knowledge of halal products, the more buying interest will increase. The assessment of halal products has a positive and significant effect on interest in buying halal products, meaning that the higher the understanding of the assessment of halal products, the higher the level of interest in buying halal products.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".