Effects of women on corporate boards: An integrative review from a political capital perspective
Bibliographic record
Abstract
This research synthesizes the literature that investigated the influence of WOCB on various firm outcomes. We organize our review around the meaning of WOCB, the sources of WOCB influence, the outcomes of WOCB impact, and contextual factors. In general, 503 articles with 558 predominantly panel studies show that the relationships of WOCB to various outcomes (particularly corporate social responsibility, firm transparency and gender equity) are beneficial but varied. Integrating prior views, we provide a framework leveraging political capital and board capital perspectives. The framework centers focus on the power and influence of WOCB, and distinguishes the sources of power from the exercise of power, board functions from organizational outcomes, and capital valuation contingencies from incentives to engage in board roles. Studies measuring political capital, which we identify as sources of WOCB’s influence, document the beneficial associations of a critical mass of 30–33% women, assigning women to board committees and women’s enhanced power resulting from multiple directorships and knowledge capital. Studies examining contextual factors show that societal gender equity, the presence of women in top management, and WOCB’s independence strengthen WOCB’s correlations with beneficial outcomes. These features facilitate interpretation of the mixed findings, identify limitations, and suggest directions for future research.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".