Supply chain integration and green innovation, the role of environmental uncertainty: Evidence from Jordan
Bibliographic record
Abstract
Prior research in several industries, particularly manufacturing industries, has confirmed the use of supply chain integration (SCI) to achieve green innovation (GI). Despite this, there has been very little research into the effects of SCI on the GI of manufacturing businesses. As a result, the current study was carried out to close this gap in the literature. Data from 231 manufacturing businesses in Jordan was used to validate a framework of several hypotheses about the relationships involving SCI and GI. SCI (customer integration (CI) and supplier integration (SI) have beneficial benefits on the green product and process innovation (GPDI & GPRI), according to the results of structural equation modeling. SI's effects on green products and process innovation are moderated by environmental unpredictability. The effects of CI on green product innovation are being moderated by environmental unpredictability. Environmental uncertainty, on the other hand, does not moderate the impacts of CI on green process innovation. This research adds to our knowledge of the SCI-GI link.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".