Board gender diversity and corporate social responsibility: A bibliometric analysis
Bibliographic record
Abstract
The objective of this study to analyze developments in relating to board gender diversity (BGD) and corporate social responsibility (CSR) research and provide future researchers with new avenues for research in the field. A bibliometric analysis was conducted by focusing on the most productive articles, authors, journals, institutions, sponsors, and countries and as co-occurrence analyses based on 1961 peer-reviewed articles published between January 1966 and April 2021 in the Scopus database. Results revealed that the number of publications relevant to BGD and CSR has been gradually increasing, and a significant increase has been observed since 2010. Keywords such as "gender," "gender equality," "sustainable development," and "corporate social responsibility" reveal the key themes in BGD and CSR research. Cluster analysis revealed three clusters: Cluster 1 focused primarily on the board composition and board structure, Cluster 2 focused on board composition and its connection to CSR or philanthropy, and Cluster 3 (comprising more recent articles) mainly stressed the impact of gender diversity on CSR or sustainability initiatives. Results also provided different implications with future research directions. It reveals the collaboration between authors in conducting research in the domain of BGD and CSR is still lacking, suggesting further research in collaboration different authors in CSR and BGD. Journal of business ethics, Corporate governance: an international review, and Academy of management journal were the top-ranking journals in term of source co-citation, and thus journals ought to be further expanded more research in CSR and BGD to enhance their source co-citations. The most productive sponsors and institutions were in developed countries, while country co-authorship analysis revealed more research need to cooperatively be undertaken in developing countries.
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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 | 0.009 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.153 | 0.202 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".