Factors Affecting the Implementation of Risk-Based Internal Auditing
Bibliographic record
Abstract
This paper aims to investigate the factors affecting risk-based internal audit (RBIA) implementation in public sector organizations in Saudi Arabia. This paper utilized 234 usable answered questionnaires from internal audit managers, internal auditors, accountants, and executives working in Saudi public sector agencies. The gathered data were analyzed by applying partial least squares–structural equation modeling (PLS-SEM). Results show that management support, internal auditor role, risk management system, and training in risk management all positively and significantly influence the RBIA. Improved internal auditing procedures and an efficient internal monitoring system will significantly curtail any risks impeding the organization’s goals, diminish the temptation to fabricate financial data or statistics, and enhance the accuracy of financial reporting/statements. Moreover, this study’s results have crucial implications for managers of public sector organizations, heads of internal audit departments, internal auditors, and accountants seeking to improve the reliability of internal audits and other aspects of financial information. Published research on what variables are influencing RBIA implementation is scarce. This study adds to the nascent literature by focusing on Saudi Arabian public sector organizations, establishing empirical variables based on an in-depth review of the relevant research and conducting an empirical investigation of the factors associated with RBIA implementation in the Saudi economy. By concentrating on public sector organizations in Saudi Arabia, this paper sheds light on other nations with comparable systems for governance policies and processes in their government-run entities.
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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 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".