The relationship between supply chain management and environmental sustainability: The mediating role of eco culinary product
Bibliographic record
Abstract
Betawi Ora is an indigenous tribe that lives in Bekasi which is a region of West Java. In terms of cultural distribution, Betawi itself is a tribe originating from Jakarta, but geographically it spreads to the West Java region. The existence of Eco-culinary products cannot be separated from the supply chain management system and environmental sustainability because Eco-culinary is a food that represents the existence of raw materials provided by the environment where the community exists. Based on the background and urgency of this research, the purpose of this research is to find out the relationship between Supply Chain Management, Eco-culinary Product and Environmental Sustainability both directly and indirectly where Eco-culinaru Product mediates the relationship between Supply Chain variables Management and Environmental Sustainability variables. The respondents in this study were 210 managers of Betawi Ora restaurants in Bekasi West Java, Indonesia and employees who maintain supply chain management in that restaurant. Data collection and processing were carried out using the Generalized Structured Component Analysis (GSCA) method. The analytical approach uses the least squares method in the parameter estimation process. The research method is a quantitative survey, analysis of research data is performed using structural equation modeling partial least squares (SEM-PLS) with statistical data processing tools, namely Smart PLS 4.0 software. Results of this research show that supply chain management had a significant effect on environmental sustainability which is mediated by eco-culinary products. Thus, it can be concluded that good quality of supply chain management will be able to produce quality products, and it must be an important consideration that the goal of everything is to create and maintain Environmental Sustainability.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".