Improving Green Market Orientation, Green Supply Chain Relationship Quality, and Green Absorptive Capacity to Enhance Green Competitive Advantage in the Green Supply Chain
Bibliographic record
Abstract
This study examines the influences of market orientation, supply chain relationship quality, and absorptive capacity on competitive advantage in green supply chains. We examine the intensity of these relationships in a green supply chain context. This study aims to figure out the effects of green market orientation (GMO), green supply chain relationship quality (GRQ), and green absorptive capacity (GAC) on green competitive advantage (GCA) in a supply chain. We find a significant positive association between these constructs. It highlights the importance of antecedents such as GMO, GRQ, and GAC on improving GCA. We jointly investigate the effects of GMO, GRQ, and GAC on green supply chain competitive advantage. This study uses Process Macro 2.15 to test the mediation effect between GMO and GCA. The result shows that GRQ and GAC completely mediate the relationship between GMO and GCA and that the effect sizes are 0.11 and 0.20, respectively. This study also reruns the model to clarify whether competing models are better than our model. However, the performance of such a competing model is poor. Finally, we accept our model instead of the competing model. GMO and GRQ among team managers and employees appear to contribute positively to GCA. Although GAC does not directly influence GCA, GMO has a significant total effect on GCA when intervened by GRQ and GAC. The key contribution is that green market orientation, i.e., the employee culture and the emphasis on being environmentally responsible, is a key antecedent to GRQ, GAC, and GCA. Managerial implications of the findings are listed.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".