The role of green technology to investigate green supply chain management practice and firm performance
Bibliographic record
Abstract
This study examined the relationship between green supply chain management (GSCM) on the environment and green economic performance with the moderator prediction context, which is a very fundamental approach for developing stronger theories. The writers chose green technology as a unique moderator in the context of GSCM practices and performance. The purpose of this study is to determine the role of moderating effects of green technology in investigating the relationship between green supply chain management (GSCM) practices and firm performance (environmental and green economic performance). By employing survey methodology using a purposive sampling technique, the data collected from 96 respondents in various manufacturing firms. The hypotheses were tested through SEM-PLS using SmartPLS. The further results show that the results of hypothesis testing indicate that GSCM practices (GSCM) have a positive and significant effect on environmental performance (EP) and green economic performance (GEP). The study also found that the role of green technology as a moderating variable can strengthen the positive relationship between GSCM Practices and environmental performance. While the moderation effect of Green technology (GT) can weaken the positive relationship between GSCM Practices and green economic performance (GEP).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".