Investigating the Effect of Supply Chain Management on Sustainable Perfprmance Focusing on Environmental Collaboration
Bibliographic record
Abstract
The purpose of this paper is proposing a comprehensive model that shows the effect of green supply chain management practices on sustainable performance focusing on environmental collaboration. 311 pieceworkers companies in the field of automotive, motorcycle and agricultural machinery were investigated. Questionnaire was used for collecting data. Structural equation model were used as a technique to analyzing the data. The results of analyzing data showed that green supply chain management practices have positive effect on sustainable performance and environmental collaboration. As mediating variable environmental collaboration has also positive effect on green supply chain management practices focusing on environmental collaboration on sustainable performance. Green supply chain can help increasing sustainable performance and environmental collaboration is also considered as an important capability for facilitating executing green supply chain management. Both positively impact society through improvements to the overall environment. This research is one of the few studies that explore the effect of green supply chain management practices on sustainable performance focusing on environmental collaboration. Green supply chain management practices plays an important role of each enterprise which is involved with supply chain activities and it will help increasing sustainable performance.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".