Does corporate governance structures promote shareholders or stakeholders value maximization? Evidence from African banks
Bibliographic record
Abstract
Purpose The purpose of this paper is to explore the relationship between corporate governance structures and stakeholder and shareholder value maximization perspectives in 267 African banks from 2006 to 2011. Design/methodology/approach The authors used the Prais–Winsten ordinary least squares and random effect regression models to explore this relationship to ensure consistency and efficiency in results. The data for this study were collected from Bankscope. Findings The results of this study show that corporate governance structures such as CEO duality, nonexecutive members and extreme large board size lead to a reduction in both shareholder and stakeholder value maximization. However, audit independence and board size also promote both shareholder and stakeholder value maximization. Although gender diversity promotes profit maximization, it was not significant in any of the models estimated. The results further suggest that the same corporate governance structures promote and detract shareholder and stakeholder value maximization in Africa although the effect of corporate governance structures was weightier on shareholder value maximization confirming the agency theory. Practical implications From these findings, bank management must pursue the institution of good corporate governance structures and avoid weak corporate governance structures to promote shareholder and stakeholder value maximization. Also equity holders may have to pay particular attention to corporate governance structures because they benefit the most from the institution of good corporate governance structures. Originality/value This study explores and compares how corporate governance structures promote shareholder and stakeholder value maximization separately in African banks. To the best of the authors’ knowledge, this is the first of such studies.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".