The acceptance of halal food in non-Muslim countries: Effects of religious identity, national identification, consumer ethnocentrism and consumer cosmopolitanism
Bibliographic record
Abstract
ABSTRACT: Purpose International restaurant and fast food chains such as KFC, McDonald's and Subway currently serve halal food in some non-Muslim countries, with mixed results. The purpose of this paper is to identify the factors that most influence the product judgements of halal food amongst non-Muslim consumers in non-Muslim countries and to assess the extent to which these judgements are related to willingness to consume halal food. \n\t\t\t\t Design/methodology/approach A quantitative survey method was adopted, using a total sample of 1,100 consumers in Canada, Spain and the UK. The proposed model was tested using structural equation modelling. \n\t\t\t\t Findings The results suggest that it may be possible for firms to satisfy specific niche market segments with standardised mass market products. Consumer cosmopolitanism and non-Muslim religious identity were found to be positively related to halal product judgement, and consumer ethnocentrism and national identification were negatively related to halal product judgement. There was a strong relationship between product judgement and willingness to consume halal food.\n\t\t\t\t Practical implications The findings indicate that halal marketing may provide promising business opportunities for international restaurant and fast food chains, as well as food manufacturers and retailers. However, in countries or regions where there are many consumers with high levels of national identification or consumer ethnocentrism, firms should not expect non-target consumers to accept halal products.\n\t\t\t\t Originality/value This is the first study to suggest that, in non-Muslim countries, food companies may switch entirely to halal produce for certain products as an effective market segmentation strategy targeting Muslim consumers.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".