Social management capabilities of multinational buying firms and their emerging market suppliers: An exploratory study of the clothing industry
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it