Corporate social responsibility disclosure and catering to investor sentiment in China
Bibliographic record
Abstract
Purpose The purpose of the paper is to examine the impact of investor sentiment on managers’ decisions to provide CSR disclosures. The core issue focuses on whether, why and how managers adjust their approach to CSR disclosure to cater to the investor sentiment. Design/methodology/approach On the basis of 13,488 observations of A-share listed companies, the authors examine the impacts of investor sentiment on CSR disclosure, which is measured separately by the propensity to issue a standalone CSR report and the quality of CSR reports. Furthermore, the authors examine the moderating role of institutional factors in China. Findings The authors find that during low-sentiment periods, managers are more likely to issue a standalone CSR report and the quality of CSR reports is higher, and vice versa. Additionally, the authors find that the negative correlations between CSR disclosure and investor sentiment are stronger in state-owned enterprises. Research limitations/implications First, the measurement of investor sentiment reflects only a part of characteristics of investor sentiment. Second, the authors pay less attention to the specific items of a CSR report. Originality/value The study contributes to the literature on CSR disclosure and investor sentiment by combining the two fields together. Furthermore, the study deepens the understanding of the institutional context in China and contributes to research on the predictors of CSR disclosure.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".