Halal product supply chain and sharia banking support for halal product commerce and its implications for halal product sharia economic growth in Indonesia
Bibliographic record
Abstract
The objective of this study is to analyze the influence of the Halal Product Supply Chain and Sharia Banking Support on the economic growth of Halal Products in Indonesia. The study also considers the role of Halal Product Commerce as an intervening variable. The research was carried out in eight districts/cities located within Banten, Indonesia. The study employed the Stratified random sampling technique, using a sample size of 250 respondents. The data collected from the surveys was further analyzed utilizing Structural Equation Modeling-Partial Least Squares (SEM-PLS). The research findings and data analysis demonstrate that the Halal Product Supply Chain has a direct and substantial influence on Halal Product Commerce. In the same vein, the endorsement of Sharia Banking likewise exerts a direct and substantial impact on the commerce of Halal Products. Moreover, the Halal Product Supply Chain exerts a direct and favorable impact on the Sharia Economic Growth of Halal Products. In addition, governmental endorsement of Sharia Banking directly and positively influences the growth of the Sharia Economy, particularly in the production of Halal Products. Ultimately, the Halal Product Trading System exerts a direct and substantial impact on the Sharia Economic Growth of Halal Products. Halal Product Commerce supports the Halal Product Supply Chain and offers Sharia Banking Assistance to foster the development of the Halal Product Sharia Economy in Indonesia.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".