Empirical analysis of board diversity and the financial performance deposit money banks in Nigeria
Bibliographic record
Abstract
This study examined the effect of board diversity on the financial performance of deposit money banks in Nigeria. The study also examined the relationship between board independence and financial performance of deposit money banks in Nigeria. For the purpose of this study, the proxy for financial performance is profit margin while the proxies for board diversity and board independence are the ratio of female directors to total board size and ratio of non-executive directors to total board size, respectively. The data for the study were sourced from the annual reports of 10 listed deposit money banks in Nigeria from 2008 to 2017. The data were analyzed using pooled Ordinary Least Square regression. The results show that the coefficient of board diversity was positively signed and statistically significant at 5% (=0.34, =0.02); the coefficient of board independence was positively signed and statistically significant at 5% (= 0.11, =0.02). The study concludes that both gender diversity and board independence positively affect the financial performance of deposit money banks in Nigeria. Therefore, the study recommends that deposit money banks should encourage appointment of qualified female directors on the board. In addition, deposit money banks should ensure the independence of the board by appointing independent non-executive director who are well experienced in bank management.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".