Promoting Good Governance through Internal Audit Function (IAF): The Nigerian Experience
Bibliographic record
Abstract
The subject of governance has become a pertinent issue for business, government, politics, and the general public. In both the private and the public sectors, there is an increasing demand for good governance in terms of accountability and transparency, and internal audit function within organizations has an important role to play in the achievement of these objectives. This study therefore assessed the effects of internal audit function (IAF) on good governance in the public sector in Nigeria. Primary data was obtained through the administration of structured questionnaire to purposively selected respondents comprising Head of Internal Audit, Director of Finance and Supplies, and Head of Local Government Administration, in 33 public sector organizations in Oyo State, Southwestern Nigeria. A total of 99 respondents participated in the study. IAF was measured by independence of internal audit system, scope of work, professional competence, examination process, and management support, while quality of service, management of public resources measured good governance. Data obtained was analysed using correlation analysis and multiple regression technique. The results showed that the effectiveness of IAF in Nigerian public sector organisations was moderate since internal audit system in the public organisations was not absolutely independent and professional competence was limited due to the challenge of insufficient funds to successfully carry out its duties. Moreover, the study revealed that IAF had significant and positive effect on the quality of service delivery and management of resources in the public organisations. The study concluded that internal Audit function is a veritable tool for promoting good governance in the Nigerian Public Sector. This study therefore recommended that there should be legal mandate in public sector organisations that allows government information to be publicly published and special funds should be made available to internal auditors as it would enhance effectiveness of internal audit function and boost good governance in the organisations.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".