INTERNAL AUDIT AND QUALITY OF FINANCIAL REPORTING IN THE PUBLIC SECTOR: THE CASE OF UNIVERSITY FOR DEVELOPMENT STUDIES
Bibliographic record
Abstract
Many corporate failures have occurred over the years as a result of poor financial reporting practices that have eluded investors and other consumers of financial data. The research used the University for Development Studies (UDS) as a case study. The study focused on three main goals: identifying emerging determinants of quality financial reporting, examining the efficacy and adequacy of UDS's internal control structure, and determining how much Internal Audit contributes to quality financial reporting. The research used a descriptive survey template and a sample size of 70 people who were chosen using purposive and stratified sampling techniques. To achieve objectives one and two, the analysis used binary regression, while to achieve objective three, the Best (2005) index was updated and used. Financial reporting accuracy, a computerized accounting system, and personnel competence were found to be determinants of quality financial reporting in the study. It was discovered that UDS' internal control system is ineffective since two of the five main components that make up an efficient internal control system, namely control environment and information and communication, are not properly implemented. The study found that UDS' internal audit reflects an average level of fraud prevention in terms of the robustness of auditing processes and fraud prevention indicators, with the remaining indicators indicating a high level of fraud prevention. Overall, UDS' internal auditing reveals a high degree of prevention. The University for Development Studies (UDS) should analyze, define, and enforce control setting, information, and communication components of the internal control system that are appropriate for their work processes, as well as enhance the existing components, according to the report. Keywords: Internal Audit, Financial Reporting, Public Sector, Ghana.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.037 | 0.075 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".