Factors Influencing Professional Judgment of Auditors in Malaysia
Bibliographic record
Abstract
The purpose of this study was to examine the factors influencing professional judgment of Malaysian auditors. A questionnaire was used to measure the level of professional judgment and factors influencing the judgment such as gender, knowledge, position level, experience and also firm size. The multiple regression results showed that the position level and experience to be statistically significant in determining the level of professional judgment of auditors. Gender, knowledge and firm size have no significant relationship with professional judgment. As for gender, past research has shown mixed results and this study proves that there is no gender differences among Malaysian auditors in terms of their professional judgment. Even though past research has shown that knowledge has a positive relationship with professional judgment, this study finds no significant relationship between the two variables. With regard to firm size, this study finds similar results of prior study that there is no significant relationship between firm size and judgment. In terms of the practical implications, this study provides insights into significant factors that influence professional judgment of Malaysian auditors. Besides, the management of audit firms can place emphasis on establishing training to their employees especially for the junior staff. Exposing junior auditors at the early stage might improve their professional judgment when facing with complexities of assignments.
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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 | 0.023 |
| 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".