Increasing Transparency in Global Supply Chains: The Case of the Fast Fashion Industry
Bibliographic record
Abstract
The fast fashion industry is subject to growing calls for transparency, from civil society groups as well as consumers. Despite universal pressure on retailers to disclose information on supply chain practices, uptake of transparency policies and practices has been heterogenous amongst large fast fashion companies. In this paper, we explain variation in transparency practices through a comparison of the four largest fast fashion retailers: H&M, Inditex, Gap, and Fast Retailing. Drawing on cross-case comparison and within-case process tracing, we offer insights into why some retailers are more transparent than others. Our findings suggest that sustainability scandals are a necessary but insufficient condition for motivating firms to increase transparency in their supply chains. Scandals can be an important driver of increased transparency, but only when accompanied by support from senior management and alignment with domestic norms about appropriate corporate conduct. These findings contribute to the literature on transnational business governance, corporate transparency, and sustainable supply-chain management.
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How this classification was reachedexpand
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".