Corporate Governance Challenges and Their Impact on Public Sector Auditing in Africa: An Exploration of Effectiveness, Accountability, and Transparency
Bibliographic record
Abstract
Corporate governance remains a fundamental issue for stakeholders in the oversight of organisations, particularly within the context of public sector auditing.Effective governance, coupled with robust auditing practices, is essential for ensuring transparency and accountability in governmental operations.However, in many African nations, corporate governance frameworks have been either inadequately implemented or have failed to achieve their intended outcomes.This study explores the challenges faced by auditees in relation to corporate governance and their subsequent impact on the efficacy of public sector auditing across Africa.Employing a phenomenological research approach, the study utilised an exploratory sequential qualitative design to gather insights from focus group discussions.A total of 33 key affinities and 153 sub-affinities, encompassing critical corporate governance issues, were identified by three focus groups from selected Supreme Audit Institutions (SAIs) in Africa.These identified affinities included audit execution and recommendations, audit acceptance, political interference, ineffective audit committees, inadequate collaboration and communication, and weaknesses in legislative oversight.Among the key themes emerging from the analysis, the auditee corporate governance policy framework was highlighted as a significant factor influencing auditing outcomes.The findings provide a detailed examination of the unique factors affecting the effectiveness of public sector audits in promoting accountability and transparency.The study proposes a comprehensive policy framework based on a resource-based theoretical perspective, designed to enhance the impact of public sector auditing in African nations.This framework is intended to guide executive governments, legislative bodies, SAIs, citizens, and other stakeholders towards improving governance and securing better public sector outcomes.The empirical evidence provided herein offers valuable insights into the complex interplay between corporate governance and auditing effectiveness, contributing to the ongoing discourse on accountability and transparency in the African public sector.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".