The role of green supply chain management practices on environmental performance in the hydrocarbon industry of Bahrain: Testing the moderation of green innovation
Bibliographic record
Abstract
Recently, environmental degradation has become a global issue, and a green supply chain has been considered as the appropriate solution for it. Also, this issue gets the intentions of recent researchers. Thus, the current article aims to examine the impact of green supply chain practices such as green purchase, internal environmental management, and customer environmental cooperation on environment performance in Bahrain. The goal also includes examining the moderating role of green innovation among the nexus of green purchase, internal environmental management, customer environmental cooperation, and environmental performance in Bahrain. The primary data collection method has been executed by the study and collected data by using questionnaires. The employees of the supply chain department of the hydrocarbon industry in Bahrain are the respondents. The statistical results show that green purchase, internal environmental management and customer environmental cooperation have positive relationships with environmental performance. The outcomes also exposed that green innovation has played an influential moderating role among the nexus of green purchase, internal environmental management, customer environmental cooperation, and environmental performance in Bahrain. These findings provide guidelines to the regulators that they should develop effective policies related to the implementation of supply chain practices that improve environmental 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 | 0.005 |
| 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 | 0.000 | 0.001 |
| 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".