The Impact of Green Supply Chain Practices Towards Operational Performances Among SMEs in Penang, Malaysia
Bibliographic record
Abstract
This research aimed to examine the relationship between green supply chain practices and operational performance among small and medium enterprises (SMEs) in Penang, Malaysia.The practices of green supply chain in achieving operational performance have been a priority concern in Malaysia.Nevertheless, the concept of green supply chain practices adoption is in embryonic phase.The study used multiple regression analysis to investigate the green supply chain (GSC) practices and operational performance variables.The 120 self-administered questionnaires were randomly distributed among SMEs in Penang, Malaysia, with 59 responses collected.Theoretically, the research ascertained the positive relationship between GSC practices and operational performance variables.The findings align with the underlying theory of dynamic capabilities, which conceptualizes GSC practices and strategies to sustain operational performance within SMEs.In short, the findings of this research provided the research implications and recommendations to the researchers, industrial practitioners and policymakers who are having interest in these GSC practices and operational performance.This research served as a guideline for companies that tend to implement these GSC practices for improving its operational performance.The research revealed that both eco-design and packaging and reverse logistics practices are found to be significantly related to operational performance, but both green procurement and investment recovery practices found to be not significantly related to operational performance.There are gaps in the literature, hence, further research should be carried out on supply chain practices and operational performance.There are no fixed formulas for sustaining operational performance that match all conditions in Penang SMEs.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".