An empirical investigation of green supply chain management (GSCM) and environmental sustainability in Saudi manufacturing SMEs: The mediating role of operations analytics
Bibliographic record
Abstract
Incorporating sustainability principles into Supply Chain Management (SCM) has received considerable attention in recent years, with a particular emphasis on Green Supply Chain Management (GSCM), which aims to reduce environmental consequences. Saudi Arabia has launched sustainability initiatives; however, the application of green SCM methods in SMEs in Saudi Arabian manufacturing has yet to be explored. Therefore, this research aimed to empirically assess the effect of GSCM in promoting environmental sustainability in Saudi manufacturing SMEs, focusing on the mediating function of operations analytics. Therefore, this study used a quantitative research technique to discover the link between the study variables—a rigorous questionnaire obtained primary data from managers and team leaders. SPSS was used for descriptive statistics, while SMARTPLS was used for structural equation modelling. The measurement model, path analysis, and indirect impact analysis were used to validate the research constructs and analyze the hypothesized associations. The findings support the evidence of direct positive links between green manufacturing, green business practices, eco-design and environmental sustainability in Saudi manufacturing SMEs. Conversely, no evidence was found to support the function of operations analytics as a bridge between green SCM and environmental sustainability. Although operations analytics can improve green SCM processes, more studies are needed to understand its full impact on environmental sustainability outcomes.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".