Corporate social responsibility decisions in apparel supply chains: The role of negative emotions in Bangladesh and Pakistan
Bibliographic record
Abstract
Abstract This article integrates the global value chain literature with the micro organization literature on negative emotions to explore the drivers of fear and anger among supplier factory senior managers in apparel supply chains after Rana Plaza—a major industrial disaster—and their influence on decisions on CSR practices. Based on a comparative study around Dhaka and Lahore—two key apparel manufacturing hubs—this study elucidates that supplier factory senior managers experienced similar market tensions but different social tensions after the Rana Plaza incident. Crucially, similar market tensions helped create market fear and anger, but different social tensions led to social fear and anger in Bangladesh but not in Pakistan, therefore influencing the way supplier factory senior managers take decisions regarding CSR practices. By conceptualizing communal alignment and competitive CSR, this research finally advances the global value chain literature and contributes to the current conversations on negative emotions in organizations.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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 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".