Internal Auditing as Value Addition to Performance Improvement in Ghana’s SOEs
Bibliographic record
Abstract
State-owned enterprises (SOEs) play a vital role in an economy, providing essential goods and services to citizens. However, they often face governance, transparency, and accountability challenges, leading to poor performance and waste of public resources. Thus, we examine the role of internal auditing in adding value to performance improvement in Ghana’s SOEs. We employ quantitative and cross-sectional survey designs to collect data from 1150 internal auditors across the SOEs and utilize macro-process modeling to analyze the data. We identify four indicators of internal auditing as value addition: internal audit effectiveness, quality, independence and resources; they have strong significant positive relationships with performance improvement (organizational performance and governance and accountability). However, these relationships are negatively moderated by organizational complexity (structural, process and systemic). We provide empirical evidence on the nuanced interplay between internal auditing, organizational complexity, and performance improvement in the context of Ghana’s SOEs, offering actionable insights for policymakers and practitioners to enhance governance and performance in emerging economies. Our findings underscore the need for SOEs to prioritize internal audit effectiveness and manage complexity to maximize performance gains.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".