Board Gender Diversity and Sustainability: A Literature Review
Bibliographic record
Abstract
The aim of this paper is to highlight, through a systematic literature review, the impact of gender diversity on boards of directors (BoD), with regard to the concept of sustainability linked in particular to aspects such as CSR and ESG. Thus, major databases (Business Source Ultimate - EBSCO host and Scopus) were queried and 288 retrieved publications were considered. The period considered for publication is from 2010 to July 2025. The results were refined through the criterion of considering only publications from scientific journals recognised and accepted by ANVUR (National Agency for the Evaluation of the University System and Research Institutes – is an independent public Institution that evaluates, accredits and verify the quality of higher education and research in Italy. The mission is to promote excellence and continuous improvement in Italian Universities and Research Institutions) for a total of 247 articles. The results show that the presence of women on boards contributes to improved Corporate Social Responsibility (CSR) and Environmental, Social and Governance (ESG) performances, but this effect is evidently dependent on several factors, the main one being the company type, with the impact varying between family and non-family businesses. The main impacts of the presence of women on corporate boards that emerged from the literature review are more sustainable strategies, ethics and trust, communication, innovation-driven, focus on people, corporate reputation. It emerges also that in order to generate these impacts it is necessary to reach “critical mass” of women in decision-making roles. Furthermore, the influence of women and their ability to lead change is affected not only by their specific role in the organization but also by the institutional and cultural context.
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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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.020 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".