Sustainable Environmental Manufacturing Practice (SEMP) and Firm Performance: Moderating Role of Environmental Regulation
Bibliographic record
Abstract
Theoretical evidence shows that a considerable amount of attention has been given to environmental issues in academic researches in the past years and the link between sustainable environmental practices and firm performance remains inconclusive. One of the reasons for this inconsistent relationship is due to the increasing regulatory requirements of the environmental sustainable practices which have become increasingly stringent on yearly basis. This study investigates the moderating effect of environmental regulation on the relationship between sustainable environmental manufacturing practices and firm performance. Data was collected by using a mail survey questionnaire from the manufacturing companies in Malaysia and analyzed with PLS-SEM. The result of the empirical investigation found that environmental regulation only moderates the relationship between SEMP and environmental performance. The relationships between SEMP; and financial and operational performance were not significantly moderated by stringent environmental regulation. The study recommends that environmental policy makers should revisit the blueprint about environmental regulation on environmental practices to provide supportive environmental policies that will enhance a better financial and operational performance in manufacturing industry.
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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 | 0.017 |
| 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 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".