Effect of Green Practices on Organizational Performance: An Empirical Study
Bibliographic record
Abstract
Global manufacturing businesses have contributed to energy and resource consumption, pollution. Along with governmental legislation, social and market pressure that growing as awareness about environmental issues increases. To tackle such problems the study focuses on the analysis of the direct consequence of Green Manufacturing (GM) practices on operational performance in the manufacturing industry. A model for evaluating the effect of GM is developed taking into consideration as a fundamental variable that affects the causal relationship between GM practices and operational performance. A structural equation model was proposed and investigated across the manufacturing industry in India. A structured survey questionnaire was used to gather empirical data from 400 Indian companies. A total of 203 usable responses were obtained giving a response rate of 53%. The data was analyzed using SPSS- AMOS software. The results revealed that GM practices directly and positively affected operational performance. The results indicated that the structural equation model remained invariant across the Industry. The implementation of Green practices in manufacturing has been recognized as a mean to improve economic and environmental performance that increases competitiveness and urge innovation. The study provides further evidence to managers and practitioners on the effect of GM practices on operational performance in developing countries like India.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".