Investigating halal food Supply chain management, halal certification and traceability on SMEs performance
Bibliographic record
Abstract
This study aims to provide implications for the application of the halal logistics traceability model to food commodities in Indonesia and to be able to recommend alternative policy scenarios for the government. The system dynamic approach is used to model the traceability of halal logistics for food commodities in Indonesia. The urgency of this research is that it will contribute to improving the quality of halal logistics which has implications for the halal industry in Indonesia. This research also has implications for food quality and safety which helps food security in Indonesia, the relationship between Halal Certification and Traceability on SMEs Performance, analyzing the relationship between Halal food supply chain management and Halal Certification and Traceability. This research method is a quantitative survey, research data obtained by distributing online questionnaires to 390 food SME owners who have implemented the Halal Assurance Management System. Data analysis used a structural equation model (SEM) with SmartPLS 3.0 software. The stages of data analysis are validity, reliability and significance tests. The sampling technique used is non-probability sampling. The questionnaire used in this study uses a Google form which will be distributed to respondents. This questionnaire measurement method uses a Likert scale of 5, namely Strongly Disagree (STS), (2) Disagree Answers (TS), (3) Neutral Answers (N), (4) Agree Answers (S), Strongly Agree (SS). The independent variable used in this research is halal supply chain management. The dependent variables used in this study are halal certification and traceability and SMEs Performance. The results of this study indicate that Halal food supply chain management has a positive and significant effect on the performance of SMEs, Halal Certification and Traceability have a positive and significant effect on SMEs Performance, Halal Certification and Traceability have a positive and significant effect on Halal food supply chain management.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".