The role of green supply chain management (GSCM) on the competitiveness and performance of Indonesian manufacturing companies
Bibliographic record
Abstract
There are several environmental issues that are in the spotlight globally, including: global warming, depletion of the ozone layer, the greenhouse effect, and acid rain. This problem is a concern and needs serious action for human survival. Supply chain activities are suspected of contributing to environmental damage. The purpose of this study is to analyze the effect of Green Supply Chain Management on competitiveness, the effect of Green Supply Chain Management on Performance and the effect of competitiveness on Performance. The study used quantitative methods and research data were obtained using online questionnaires distributed via social media. The research respondents were managers of manufacturing companies in Indonesia and the number of samples used was 540 respondents who were determined using a purposive sampling technique. Data analysis in this study used Structural Equation Modeling (SEM) and software used for data processing by SmartPLS. The results showed that Green Supply Chain Management had a positive and significant effect on competitiveness, Green Supply Chain Management did not have any positive and significant effect on Performance and competitiveness had a positive and significant effect on 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 | 0.002 | 0.003 |
| 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 | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".