The mediating role of green supply chain management in the relationship between eco-efficiency and SMEs sustainability performance
Bibliographic record
Abstract
The purpose of this study is to analyze the relationship between eco-efficiency and sustainability performance, the relationship between eco-supply chain management and sustainability performance and analyze the mediating role of Eco-Supply Chain Management on the relationship between eco-efficiency and sustainability performance. The study method is a quantitative survey which is executed through the partial least square structural equation model (SEM-PLS) with a statistical data processing tool, namely SmartPLS 4.0 software. Research data was obtained by distributing online questionnaires through social media designed using a Likert scale of 7. Respondents in this study were 590 SMEs owners in Java Island, Indonesia. The results of the study indicate that eco-efficiency had a positive and significant effect on sustainability performance, green supply chain management (GSCM) had a positive and significant effect on sustainability performance. GSCM also plays a full mediating role in the relationship between eco-efficiency and sustainability performance. This research is expected to be a source of reference for further research in discussing issues related to the implementation of eco-efficiency and GSCM.
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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 | 0.002 | 0.006 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".