Corporate–NGO collaboration and CSR disclosure – the moderating role of corporate profitability
Bibliographic record
Abstract
Purpose This research investigates the influence of corporate–NGO collaborations on corporate social responsibility (CSR) disclosure measured in three different ways (i.e. extent, level and quality) in low-income developing economies. Additionally, it examines the moderating effect of corporate profitability in the relationship between corporate–NGO collaborations and CSR disclosure. Design/methodology/approach This research uses multivariate regression analysis based on data collected from 201 non-financial firms listed on the Pakistan Stock Exchange (PSE). Findings The findings reveal that corporations with NGO partnerships are more likely to disclose CSR information and provide high-quality information regarding workers, the environment and community-related issues. Further, corporate profitability positively moderates the corporate–NGO collaborations and CSR disclosure relationship. Research limitations/implications Research limitations are presented in the conclusion section. Practical implications The findings underline the crucial significance of NGOs and their associated normative isomorphism logics for CSR disclosure in low-income countries with weak law enforcement and relatively ineffective state institutions, which were previously believed to lack such institutions. Originality/value While some research has suggested that companies in developing countries perceive significant pressure from NGOs to adopt social disclosure, no study has specifically explored how internally driven corporate–NGO collaboration (as opposed to external NGO activist pressures) promotes CSR disclosure specifically in developing economies.
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".