The Influence of Competency, Usage of It and Career Expectation on Internal Auditor’s Effectiveness in Ggovernment Linked Companies (GLCs)
Bibliographic record
Abstract
National Audit Department (NAD) had repetitively recommended Federal Ministries/ Departments and Government-Linked Companies (GLCs) through its Auditor-General’s Report on the need to have an effective internal audit function to identify and mitigate weaknesses in their activities. Therefore, the purpose of this study is to examine factors that influence the effectiveness of the internal auditors especially for those that work in GLCs in Malaysia. It is to determine the internal auditors’ perception toward the effectiveness of internal audit activities, influenced by the individual factors related to the internal auditors themselves. This study also aims to determine the relationship between factors that contribute to the effectiveness. 300 questionnaires were distributed to internal auditors that work in GLCs in Malaysia using convenience sampling method, of which, 124 internal auditors had responded. Several statistical techniques such as the descriptive statistic, correlation and regression analysis were used to analyse the data from the survey. The result of the study showed that there were significant relationships among the factors analysed in this study which are the internal auditors’ competency, usage of information technology (IT) and career expectation. Hence, the effectiveness of internal audit will depend strongly to the attributes of the factors analysed in this study. This study will help organisations especially GLCs in understanding the factors that influence the internal auditors’ effectiveness and taking necessary action to improve their internal audit function. Consequently, the internal audit function could perform better in reporting findings and giving significant recommendations that give impact to the organisations.
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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 | 0.005 | 0.021 |
| 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) | 0.002 | 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".