Moderating effects of country-level institutional quality and cultural dimensions on CSR-stock price crash risk relationship: a meta-analysis study
Bibliographic record
Abstract
Purpose This study uses meta-analysis to examine the relationship between corporate sustainability reporting (CSR) and stock price crash risk (SPCR) and to discern the moderating effects of country-level institutional quality and cultural dimensions on this link. Design/methodology/approach The study used mean correlation coefficients to test the relationship between CSR and SPCR and meta-regressions to test the moderating effects. The analysis considers 65 effect sizes from 24 empirical studies. Findings The results showed that CSR reduces the chances of SPCR. The inverse relationship between CSR and SPCR is stronger in masculine, high power distance and long-term oriented cultures and is less pronounced in individualistic, uncertainty avoidance and indulgent cultures. The inverse relationship is also stronger in countries where high-quality institutions exist. Research limitations/implications This study is based on correlation coefficient analysis and excludes studies publishing only regression results. Furthermore, it provides guidance to lessen SPCR. Findings suggest that such initiatives may mitigate the risk of stock price crashes for firms. Through meta-analysis, this research investigates the correlation between environmental, social and governance (ESG) disclosure and stock price crash occurrences, offering insights with significant implications for the European financial landscape and globally. Originality/value This is a pioneer meta-analysis that investigates the link between CSR and SPCR and the moderating effects of country-level institutional quality and cultural dimensions. Our study sheds light on the potential impact of promoting a sustainable and responsible business environment in Europe through comprehensive ESG disclosure under the Corporate Sustainability Reporting Directive (CSRD).
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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 | 0.025 | 0.070 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.029 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".