Corporate–NGO collaboration and CSR disclosure – the moderating role of corporate profitability
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.000 |
| 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 it