Green Supply Chain Management (GSCM) Practices for Sustainability Performance: An Empirical Evidence of Malaysian SMEs
Bibliographic record
Abstract
Environmental issues have been critical concern among the current businesses as various business activities might pose significant threats to the environment. Incorporating environmental aspects in business operations is perceived to be able to create value and to achieve sustainable business performance. The manufacturing sector is the key contributor to the country’s high pollution index. Green Supply Chain Management (GSCM) practices have become more prevalent in this sector in managing the environmental issues for the effectiveness of their production requirement. Nevertheless, the small and medium enterprises (SMEs) mainly are less likely to embark on environmental practices as compared to large organisations. The establishment of certified environmental management systems (EMS) is considered as a strategic management approach that defines how organisations will address their impact on the natural environment guided by ISO 14001 as a framework. This study provides empirical evidence examining the extent of GSCM practices among the SMEs through the possession of ISO 14001 and examines the impact on sustainability performance. Data were analysed using regression analyses. Results indicate that GSCM practices have a significant positive relation with sustainability performance. Eco-design practices and environmental cooperation have a positive relationship with sustainability performance. There is no relationship between green purchasing and reverse logistics practices with sustainability performance. These results imply that Malaysian SMEs adopt GSCM practices mostly through eco-design and robust cooperation among departments in dealing with environmental issues. Green practices and reverse logistics practices are still new for SMEs and do not contribute to achieving better 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".