The effect of auditing quality and internal control on financial resilience in public sector organi-zations: Information quality as the mediating factor
Bibliographic record
Abstract
In dynamic socio-economic environments, public sector organizations and companies with strong financial resilience are better equipped to adapt to economic changes, socio-economic fluctuations, and shifts in the business landscape with greater flexibility. Financial resilience also has implications for an organization's liquidity. One of the critical factors influencing financial resilience in public sector organizations is the quality of its audit. Ensuring high-quality audits is vital for assessing the accuracy and reliability of an organization's financial statements. This study aims to investigate the impact of audit quality and internal control on financial security, with information quality serving as a mediating factor. Quantitative research methods were employed to collect and statistically analyze the data. The study gathered information through questionnaires distributed to the Auditors of the Supreme Audit Board, with a sample size of 321 participants. The data was then processed using SmartPLS software. The research findings demonstrate a significant relationship between audit quality and internal control, positively influencing the organization’s financial resilience. Furthermore, the study reveals that information quality acts as a crucial mediator, linking audit quality and internal control to financial security. The analysis shows that audit quality significantly affects information quality. However, the direct impact of audit quality on financial resilience is not significant. On the other hand, internal control significantly influences both information quality and the organization’s financial resilience. Additionally, the quality of information also has a significant effect on the organization’s financial resilience.
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".