Driving sustainable supply chains: empowering social responsibility and environmental excellence amid uncertainty
Bibliographic record
Abstract
Purpose This study investigates how consumer awareness of environmental and social issues affects retail channels. It proposes a win-win contract designed to motivate manufacturers and retailers to produce environmentally friendly products in a socially responsible manner. Notably, this research considers the uncertainty about consumers’ social awareness levels. Design/methodology/approach This study analyzes a model of a supply chain where a retailer procures products from a manufacturer to fulfill fluctuating demand influenced by consumer preferences for eco-friendly and socially responsible goods. The research conducts a comparative analysis between the optimal first-best solution and the suboptimal second-best solution to characterize the inefficiencies within the supply chain. Findings Lack of coordination in a socially and environmentally conscious market can harm profitability. Collaboration among supply chain parties enhances both sustainability and profitability, underscoring the importance of equitable surplus-sharing through appropriate contracts. Practical implications This study underscores the importance of collaboration among supply chain stakeholders to enhance performance for all parties involved. It offers valuable insights into the collective optimization of sustainability, profitability and social responsibility objectives, addressing the challenges of the complex business landscape. Originality/value This study enhances the current body of literature on sustainable supply chain management by integrating green and social initiatives while considering the uncertain reaction of consumers to the social initiatives of the supply chain. Considering the limited exploration of social factors in retail channels, it is essential to incorporate the stochastic aspects of these factors to gain a deeper understanding of their impact.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| 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".