Halal food supply chains: A literature review of sustainable measures and future research directions
Bibliographic record
Abstract
Introduction. Although sustainability represents a high-profile topic in supply chain management, it remains an unexplored research area for Halal food supply chains (HFSCs). Hence, to bridge this knowledge gap, we conducted a systematic literature review to identify the measures necessary for the development of sustainable HFSCs and potential research gaps at the nexus of sustainability and Halal food literature. Study objects and methods. We carefully analyzed forty (40) papers selected from leading, highly-ranked journals to answer the following research question: “What are the measures necessary for the development of sustainable Halal food supply chains?” Results and discussion. The findings revealed that the improvement of Halal processes through the implementation of quality management systems, the effectiveness of Halal labeling, and the use of technology could enhance the economic performance of HFSCs. Furthermore, HFSC’s sustainability efforts are strengthened by enhancing trust and transparency benefitting human resource skills development, promoting animal welfare issues, and increasing regulatory compliance. The implementation of environmental protection measures is a primary driving factor for environmental sustainability activities. Environmental sustainability could be fostered by a shift to the application of greening practices and the support of environmentalism in the Halal food industry. Conclusion. The findings of this study provide critical managerial implications for Halal food practitioners as they can have a summary of the previous studies and thus use it as a benchmark for introducing sustainable measures in their Halal food firms.
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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 | 0.012 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.017 | 0.020 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".