Role of multinational buyers in ensuring worker voice through social dialogue: an exploratory study of the export oriented garment industry
Bibliographic record
Abstract
Purpose This study aims to explore how multinational lead buyers can play an active role in ensuring worker voices in garment supplier factories where workers have limited space to raise their voices, and how buyers’ involvement increases the possibilities of worker voices mitigating barriers to social dialogues and enhancing mutual interests of buyers and workers in garment factories. Design/methodology/approach Using a qualitative research approach and multiple embedded case study method, this study considered buyer−supplier dyads as the unit of analysis, i.e. two multinational lead buyers and their four corresponding suppliers in the garment industry of Bangladesh. Focus group discussion and key informant in-depth interviews were techniques applied to collect factory-level data, and within and cross-case analysis techniques were applied to develop an overall understanding. Findings The results of this study reveal that the opportunities for workers to voice their concerns through social dialogue in garment supplier factories are limited due to various obstacles. Similarly, the role of multinational lead buyers in addressing these issues is found to be less than ideal. This study also shows that buyers can take short-term and long-term initiatives to ensure social dialogues. Moreover, this study presents how social dialogues can meet the expectations of multinational buyers and their garment suppliers. Research limitations/implications While this study focuses exclusively on the garment industry, similar scenarios also exist across a multitude of other industries. Thus, future research could extend this study’s scope to various sectors, providing a more comprehensive understanding of the general state of worker voices in Bangladesh. This study stands to make significant contributions to literature in the fields of global value chains, human relations and international business. It will pose critical perspectives on how upstream value chain suppliers can fortify worker rights through social dialogue, and elucidate the means and motives for lead buyers to play a more active role in this endeavour. Originality/value This study is distinct in its approach, integrating buyer−supplier roles to pave the way for enhanced worker voice opportunities through social dialogue in garment supplier factories.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".