Is corporate governance a significant factor in corporate social responsibility disclosure? Insights from China
Bibliographic record
Abstract
This comprehensive study delves into the intricate relationship between corporate governance and Corporate Social Responsibility Disclosure (CSRD) within the framework of China’s institutional landscape. By analyzing an extensive dataset comprising 35,435 firm-year observations from 3,889 A-share listed companies spanning the years 2006 to 2019, the research scrutinizes various governance mechanisms, including board size, independence, CEO duality, and ownership concentration.The investigation affirms that larger boards and a higher proportion of independent directors exert a positive influence on CSRD. In contrast, a substantial shareholding ratio held by the largest shareholder proves to be a hindrance to the transparent disclosure of CSR initiatives. While the impact of CEO duality on CSRD is noted, the statistical significance of this relationship remains inconclusive.These findings underscore the nuanced dynamics of governance and ownership structures in shaping CSR initiatives. The findings highlight the nuanced impact of governance and ownership structures on CSR initiatives, offering valuable insights for managers and policymakers navigating CSR strategies in China’s business landscape. The insights garnered from this study hold valuable implications for both corporate managers and policymakers navigating the landscape of CSR strategies within the unique contours of China’s business environment. AcknowledgmentThis paper is co-funded by the European Union through the European Education and Culture Executive Agency (EACEA) within the project “Embracing EU corporate social responsibility: challenges and opportunities of business-society bonds transformation in Ukraine” – 101094100 – EECORE – ERASMUS-JMO-2022-HEI-TCH-RSCH-UA-IBA / ERASMUS-JMO-2022-HEI-TCHRSCH https://eecore.snau.edu.ua/Oleh PASKO expresses sincere gratitude for the support received from the Kirkland Research Program, generously provided by the Leaders of Change Foundation established by the Polish-American Freedom Foundation.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".