Corporate Governance and Financial Performance: The Interplay of Board Gender Diversity and Intellectual Capital
Bibliographic record
Abstract
Prior research has found mixed evidence regarding the relationships between board gender diversity (BGD) and firm value. Moreover, there is a lack of evidence on the channels through which BGD affects firm performance; hence, this paper tackles this issue. We aim to investigate the relationship between BGD and firm performance and to explore the mediating role of intellectual capital efficiency (ICE) in this relationship. Using a multivariate regression analysis and a sample of 4008 North American firms from 2002 to 2020 (14,382 firm-year observations), we find that gender diversity is positively related to financial performance, confirming that a diversified board improves board effectiveness and brings new resources to the firm, which allows it to improve its performance. More interestingly, the results of the Structural Equation Model (SEM) indicate that the relationship between gender diversity and performance is more pronounced with the mediating role of ICE. Our results are robust, controlling for the endogeneity and heteroscedasticity issues, with several controls for firm- and country-level characteristics, using alternative sample compositions and alternative econometric techniques, and including year, industry, country and firm-fixed effects. Interestingly, this paper shows strong evidence that the effect of BGD on firm value is more effective by incorporating the role of intellectual capital efficiency.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".