Product standardisation in the food service industry: post-purchase attitudes and repurchase intentions of non-Muslims after consuming halal food
Bibliographic record
Abstract
In order to satisfy the Muslim market segment, many restaurant and fast food companies in Western countries have standardised their products by switching to halal. The purpose of this research is to discover the extent to which non-Muslim consumers in non-Muslim countries experience cognitive dissonance when they think about restaurants and fast food outlets having likely served them halal-produced food, and the extent to which these consumers intend to repurchase halal food. Data came from a total sample of 1097 non-Muslim consumers in Canada, Spain and the United Kingdom. The full model, with religious identity, ethnic identification and interest in animal welfare as antecedents of cognitive dissonance, explained 35% of the variance in consumers’ repurchase intentions. Our findings suggest that many non-Muslims do not have a particular issue with consuming halal food, but they may react negatively if they unintentionally consume halal food and perceive that they have been deprived of information, or worse still, deliberately deceived. The research makes a number of contributions to marketing knowledge with regard to the negative spillover effects that can result from faith-based product standardisation, and the influences of consumer interest in animal welfare and deprivation of product information on consumer behaviour.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".