To Talk or to Walk? Celebrity CEOs and Differentiated Corporate Social Responsibility Strategies of Chinese Listed Enterprises
Bibliographic record
Abstract
ABSTRACT By analysing the role of prestigious official media awards on CEOs, this paper explores the outcome of the conduction of substantive or strategic corporate social responsibility (CSR) enacted by celebrity CEOs. Based on the awards information uncovered by CCTV, Forbes, China Business News, Fortune and China Times, we find that enterprises notably increase substantive CSR after their CEOs have won the distinguished awards, which indicates that celebrity CEOs can exert a significant increment on corporate charitable donations, whereas no obvious improvement in strategic CSR (CSR information disclosure) has been observed. Concerning on exploring the potential mechanism, we confirm that celebrity CEOs could firmly induce more donations by enhancing CEO power and increasing corporate risk taking. Heterogeneity analysis implies that female celebrity CEOs and celebrity CEOs with socialist ideological imprint both have a pronounced effect on fostering charitable donations. Besides, the positive effect of celebrity CEOs on donations is more prominent in non‐SOEs, high media exposure enterprises and regions with strong collectivism culture. The findings of this paper provide a crucial supplement to the study of celebrity CEOs and differentiated CSR fields and have practical implications for how the Chinese capital market can stimulate managers to actively undertake social responsibility.
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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 | 0.000 |
| 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) | 0.004 | 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".