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Record W2944143793 · doi:10.5267/j.ac.2019.5.001

Empirical analysis of board diversity and the financial performance deposit money banks in Nigeria

2019· article· en· W2944143793 on OpenAlexvenueno aff
A.D. Adesanmi, O.A. Sanyaolu, Muideen Adejare Isiaka, Oluwasheun Fadipe

Bibliographic record

VenueAccounting · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIslamic Finance and Banking Studies
Canadian institutionsnot available
Fundersnot available
KeywordsDiversity (politics)BusinessFinancial systemFinancePolitical science

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.343

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.009
GPT teacher head0.206
Teacher spread0.196 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations13
Published2019
Admission routes1
Has abstractyes

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