Effectiveness of internal audit in local governments: The moderating role of internal and external auditors’ relations
Bibliographic record
Abstract
Research on the internal audit function is relevant for improving the quality of governance in organizations. The internal auditor is an important element of government management in the context of realizing good governance by providing quality and effective audit results. The aim of this study is to examine the factors influencing (determinants) the effectiveness of the internal audit function in Indonesian local government organizations. The research samples were 137 respondents. This study used primary data in the form of a questionnaire. The hypothesis testing technique used Partial Least Squares-Structural Equation Modelling (PLS-SEM) analysis. The results of statistical tests showed that independence, competence, and management support could increase the effectiveness of the internal audit function. However, this cooperative relationship does not moderate the influence of competence and management support on the effectiveness of internal audit. The practical implication of this study is that in order to increase the effectiveness of internal audit, internal auditors must uphold an attitude of independence, objectivity and freedom from conflicts of interest in carrying out their professional responsibilities. The practical value of this study also shows that to increase the effectiveness of public sector internal audit, internal and external auditors must increase cooperation to improve the effectiveness of internal audit, especially in discussion activities between internal and external auditors, communication between internal and external auditors, and activities to share working papers between internal and external auditors.
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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 | 0.008 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".