Religiosity, halal food consumption, and physical well-being: An extension of the TPB
Bibliographic record
Abstract
This research used the Theory of Planned Behaviour (TPB) theoretical framework to extend and contribute to prior research on halal purchase behaviour. The main purpose of this study is to contribute to the literature by focusing on the relationship between religiosity and physical well-being and identifying the mediating halal-food consumption that affects on physical well-being. We applied non-probability convenience sampling to administer questionnaires among 315 Pakistani Muslim and Non-Muslim respondents currently living in Pakistan, the USA, Canada, Australia, and Germany, during the winter of 2019–2020. The study used a partial-least-squares structural-equation-modeling (PLS-SEM) technique to investigate the data, which provides evidence of reliability and validity. Further, we used the PLS-SEM technique in investigating the relationship among religiosity, halal-food consumption, and physical well-being. The results show that the behavioural intention to buy halal food mediated the relationship between religiosity and physical well-being. Halal-food consumption mediated the relationship between subjective norms and physical well-being. However, it did not mediate the relationship between attitude and physical well-being, perceived behavioural control, and physical well-being. Further, this study also found that behavioural intention to buy halal food has a significant direct positive relationship with religiosity, attitude, subjective norms, and perceived behavioural control. The findings have important implications for food manufacturers and marketers in devising a policy on marketing campaigns to attract very health-conscious customers.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".