Sustainability in Supply Chain Considering the Triple Bottom Line to Determine Business Development
Bibliographic record
Abstract
Sustainability in business processes is a buzz in the business world.This research aims to focus on the supply chain process of the business.Not only for securing their future but also for considering it as a responsibility towards society.There are a lot of innovations happening in strategies adopted for ensuring sustainable applications in the supply chain process.This study has tried to identify some breakthrough strategies in the supply chain management processes which are sustainable in nature.Some of these strategies are reducing wastage, green shipping, sustainable packaging, reuse and recycle and adopting e-commerce platforms to sell the merchandise.Secondly, to ensure that sustainability is achieved, the study has taken the TBL (triple bottom line) as the metrics to measure sustainability.The TBL has been also considered as the mediating variable between supply chain strategies and the organization growth factors.Structured Equation Modelling was performed to study the model fit.It was observed that sustainable supply chain management directly has a significant influence on Organizational Growth and on TBL.But TBL has no significant influence on Organizational Growth.Also, sustainable supply chain management indirectly has no significant influence on organizational Growth through the triple bottom line as a mediating variable.The result of One-Way ANOVA says that there is a significant impact of different sectors like electronics manufacturing companies, apparel companies, and FMCG companies on organization growth.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".