Communication intensity of CSR practices to stakeholders in the global value chains: A comparative study of labor-intensive textile industries
Bibliographic record
Abstract
The global value chain (GVC) phenomenon has allowed labor intensive firms to spread their activities across developing and emerging markets with different degrees of institutional voids. The emergence of GVCs has encouraged labor-intensive firms to operate in diverse institutional contexts, heightening the importance of transparent corporate social responsibility (CSR) practices. This paper investigates the intensity and scope of CSR communication among textile companies in two major emerging markets, Pakistan and China, to understand how local institutional environments shape CSR strategies. A content analysis of corporate websites from 14 publicly listed firms (seven from each country) reveals that Pakistani textile companies disclose more CSR information and place greater emphasis on third-party validation of their Chinese counterparts. Conversely, Chinese companies demonstrate a stronger focus on social issues than environmental ones, but communicate such initiatives less thoroughly. These findings suggest that stakeholder pressures, institutional voids, and varying cultural norms can significantly influence both the content and verification of CSR activities. By highlighting how differences in external referents and stakeholder engagement inform CSR communication, this study contributes to the literature on CSR in emerging economies and offers practical implications for managers seeking to bolster global competitiveness. Policymakers and international organizations could further support environment-friendly production ecosystems by promoting standardized CSR reporting frameworks, particularly in labor-intensive sectors exposed to reputational risks and ethical concerns.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".