Analyzing the Factors That Affect Auditor’s Judgment and Decision Making in Lebanese Audit Firms
Bibliographic record
Abstract
The exercise of audit judgment is essential because it is impractical to perform an audit on all types of evidence. These types of evidence are considered in forming an opinion on audited financial statements, making audit judgment a determinant of the audit’s outcome. The objective of this research is to analyze the factors that affect an auditor’s judgment and decision making (JDM) during an audit. This study used an exploratory research design, with the factor analysis approach as its methodology. However, the data were collected using the questionnaire method. The questionnaire was sent to all member auditors of the Lebanese Association of Certified Public Accountants (LACPA). A total of 310 completed questionnaires were collected and analyzed. The data analysis findings indicate that the auditor’s JDM throughout the audit process is affected by three factors: personal, task, and environmental factors. The auditor’s personal factor becomes the dominant factor because it has the largest eigenvalue of 7.949. These findings demonstrate the complex and diverse nature of auditor judgment, highlighting the significance of considering audit JDM factors. Therefore, auditors may improve their abilities to make informed and effective judgments throughout the audit process by acknowledging the importance of personal, task, and environmental factors.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 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".