Exploring the Affiliation of Corporate Social Responsibility, Innovation Performance, and CEO Gender Diversity: Evidence from the U.S.
Bibliographic record
Abstract
This paper examines the relationship between CSR activities and innovation performance with the moderating effect of CEO gender in the U.S. market. This paper provides evidence about the relationship between CSR and innovation performance from the resources-based views by replacing the common measurements of innovation and R&D expenditures with the number of patents and citations to better measure the innovation quality rather than quantity. The current paper verifies the relationship between CSR and innovation in S&P 500 U.S. listed companies and fills the gaps in the current research on the moderating effect of CEO gender on this relationship. The paper analyzed the panel data for 1204 observations from various databases (Compustat, KLD, U.S. patents by words and Excompustat) from 2014 to 2018. Specifically, the number of patents and citations is set as the measurement of the explanatory variable; innovation performance and CSR scores from KLD are treated as the dependent variable and the proportion of female directors in the top management as the method of moderating indicator. The result in this paper shows a positive correlation between CSR and innovation performance in the U.S. At the same time, the moderating effect of CEO gender has an insignificant impact on this relationship. The findings suggest that the female CEOs do not have a positive relationship with corporate innovation. These results will help companies realize the importance of CSR activities and how to balance gender diversity in their strategies.
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How this classification was reachedexpand
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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".