Corporate Governance and Shareholders’ Value: The Mediating Role of Internal Audit Performance—Empirical Evidence from Listed Companies in Ghana
Bibliographic record
Abstract
The relationship between corporate governance and shareholder value remains a subject of contention, with studies reporting positive, weak, or context-dependent effects. Drawing on multiple theoretical perspectives, this study examines how and when corporate governance influences shareholder value, with a focus on the mediating role of internal audit performance (IAP) among listed companies on the Ghana Stock Exchange. Using an explanatory design and a sample of 300 respondents (74.3% response rate), we employed partial least squares structural equation modelling (PLS-SEM) to test the relationships. The findings show that corporate governance significantly enhances internal audit performance, which in turn improves shareholder value. In contrast, the direct impact of corporate governance on shareholder value is insignificant. Bootstrapped tests confirm a near-full mediation effect, positioning internal audit performance as the critical engine that translates governance structures into value creation. These results help clarify the inconsistent findings on the relationship between corporate governance and shareholder value in emerging markets. We provide regulators, boards, and management with a roadmap for strengthening internal audit capabilities and aligning audit and governance functions with corporate objectives to maximize shareholder value.
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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 | 0.005 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".