Social management capabilities of multinational buying firms and their emerging market suppliers: An exploratory study of the clothing industry
Bibliographic record
Abstract
Abstract For sustainability, research in operations and supply chain management historically emphasized the development of environmental rather than social capabilities. However, factory disasters in Bangladesh, an emerging market and the second largest clothing exporter in the world, revealed enormous challenges in the implementation of social sustainability in complex global supply chains. Against the backdrop of a building collapse in Bangladesh's clothing industry, this research uses multiple case studies from two time periods to explore the skills, practices, relationships and processes – collectively termed “social management capabilities” (SMCs) – that help buyers and suppliers respond to stakeholder pressures; address regulatory gaps; and improve social performance. The study not only captures the perspectives of both multinational buyers and their emerging market suppliers, but also provides supplementary evidence from other key stakeholders, such as NGOs and unions. Our findings show that, in the absence of intense stakeholder pressure, buyers can lay the foundation for improved social performance by using their own auditors and collaborating with suppliers rather than using third‐party auditors. However, in the face of acute attention from customers, NGOs and media, we observed that consultative buyer‐consortium audits emerged, and shared third‐party audits offered other advantages such as increased transparency and improvements in worker education and training. Finally, we present research propositions derived from our empirical study to guide future research on implementing social sustainability in emerging markets.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".