Smart green supply chain management: a configurational approach to enhance firm financial performance
Bibliographic record
Abstract
This study uses the Resource-Based View (RBV) and technology, organization, and environment (TOE) theories to examine how smart supply chain (SSC) practices affect financial performance (FP) in enterprises of various sizes. Our results show that SSC benefits larger enterprises more financially than smaller firms. SSC has a statistically significant effect on green supply chain management (GSCM) and sustainable supply chain performance (SSCP), and the strength of the relationship declines with a decline in firm size. Smaller enterprises are more receptive to competitive pressure and implement GSCM alongside SSC. Our findings show that SSCP improves financial performance, while GSCM does not, even in large enterprises. Further, mediation effects show that GSCM mediates the relationship between SSC and SSCP, whereas it does not mediate between SSC and FP across all sizes. The impact of SSC on FP is sequentially mediated via GSCM and SSCP. Using a non-linear approach (ANN), we also rank independent variables for small, medium, and large firms. Our research provides important implications.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".