Enhancing auditor self-efficacy through targeted fraud detection training
Bibliographic record
Abstract
Auditor self-efficacy (ASE), defined as auditors’ confidence in their capabilities to execute essential auditing tasks, is critical for audit effectiveness, particularly amid the profession's growing complexity and technological advancements. Despite extensive research on ASE in isolated competencies, limited empirical attention has been given to structured professional development interventions explicitly designed to simultaneously enhance multiple auditor competencies. This study empirically evaluated the immediate effects of a structured, interactive professional development workshop on ASE across three key domains: technical auditing skills, technological adaptation, and interpersonal communication. Grounded in Bandura’s social cognitive theory, the workshop comprised three weekly sessions (3 hours each), employing interactive exercises, mastery experiences, and structured feedback. A total of 63 practicing auditors participated, completing pre- and post-workshop evaluations using the validated Auditor Self-Efficacy (ASE) scale. Paired-sample t-tests revealed statistically significant improvements in all three domains (p < .001), with moderate-to-large effect sizes (Technical Skills: d = 0.77; Technological Adaptation: d = 0.66; Interpersonal Communication: d = 0.59). Qualitative analysis of participant reflections confirmed and enriched these findings, highlighting substantial gains in analytical proficiency, fraud detection capabilities, and communication effectiveness. The study provides clear empirical evidence supporting targeted, interactive training as a valuable tool for enhancing auditors' professional competencies and confidence. Practical implications and recommendations for future research are discussed. Keywords: Auditor Self-Efficacy, Professional Development, Auditing Training, Technical Skills, Technological Adaptation, Interpersonal Communication, Social Cognitive Theory.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".