Assessment of Government Internal Control Systems on Financial Reporting Quality in Ghana: A Case Study of Ghana Revenue Authority
Bibliographic record
Abstract
Internal control systems cannot be underestimated as it serves as the lifeblood of most institutions in terms of its imperative roles that it plays in both tangible and intangible assets of an organization. Internal control actions on quality financial report state positive goals more especially when all parties involved adhere to their duties; thus, making the quality of financial reporting comparable, understandable, relevant, and reliable. In this regard, this study investigated the impact of government internal control systems on financial reporting quality in Ghana using Ghana Revenue Authority as the case study. Specifically, the study examined the nature and quality of financial reporting and the impact of government internal control systems on financial reporting quality. Both quota and simple random sampling techniques were used to select fifty (50) persons as the sample size of the study. Questionnaires were used to obtain data. The correlation matrix was used to examine the relationship between government internal control systems and financial reporting quality. The study finds out that contrary to apriori expectation sign monitoring as an element of internal control system has a negative impact on the financial quality reporting but was however statistically significant. The study also revealed that with a unit increase in the collection performance, the financial reporting quality of GRA will improve. The study recommended that the government should ensure that the internal control systems are well monitored and regulated.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".