Uncovering the Motivations and Barriers for Suppliers in Adopting Sustainable Practices
Bibliographic record
Abstract
Abstract This qualitative research investigates the motivations and barriers for suppliers in adopting sustainable practices within supply chains. Through semi-structured interviews with 20 suppliers from diverse industries and geographic locations, key themes emerge regarding supplier engagement with sustainability. The findings highlight the complex interplay of internal and external factors shaping supplier behavior, including competitive dynamics, stakeholder pressures, organizational factors, and supply chain complexities. Motivations for supplier engagement with sustainability encompass competitive advantage, financial benefits, stakeholder pressure, and internal values. However, suppliers face significant barriers, such as financial constraints, organizational inertia, and supply chain complexities, hindering their ability to adopt and implement sustainable practices effectively. The implications of these findings extend to both practice and policy, with recommendations for firms to develop tailored strategies and interventions to support suppliers in their sustainability journey. Policymakers can use the insights to inform regulatory frameworks and industry standards that promote sustainability across supply chains, fostering collaboration and knowledge sharing among stakeholders to drive collective action on sustainability initiatives. By addressing these factors effectively, stakeholders can unlock the potential for sustainable supply chain management to drive positive environmental, social, and economic outcomes, advancing the broader agenda of sustainable development.
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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 | 0.014 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".