Examining the Role of Personality Traits, Digital Technology Skills and Competency on the Effectiveness of Fraud Risk Assessment among External Auditors
Bibliographic record
Abstract
In accordance with ISA 240, it is the responsibility of external auditors to obtain reasonable assurance that financial statements are free from material misstatement, whether caused by fraud or error. Recently, the auditing profession in Malaysia has been significantly challenged by the explosion of fraud cases and by auditors’ failure to determine the “true and fair view” of the financial statement. This incident has tarnished the reputation of the audit profession. The effectiveness of the external auditor function, especially when related to fraud risk assessment, is commonly called into question. Hence, this study aims to assess individual factors (personality traits, digital technology skills, and competency) that may contribute to the effectiveness of fraud risk assessment among external auditors. A total of 455 questionnaires were distributed to external auditors, and a total of 150 (32.96%) responses were received. Data were thoroughly analyzed using Smart-PLS 4.0. This study found that digital technology skills contribute to the effectiveness of fraud risk assessment, whereas personality traits and competency do not. The findings implied that an effective technique of fraud risk assessment among external auditors requires digital technology skills. This study contributes to the literature by confirming the critical role of digital technology skills in enhancing the effectiveness of fraud risk assessments.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".