Root Cause Analysis and Its Effect on Auditors' Judgments and Decisions in an Integrated Audit*
Bibliographic record
Abstract
ABSTRACT This study evaluates whether auditor use of root cause analysis (RCA) for an identified client misstatement affects auditors' assessments of underlying control issues and materiality in an integrated audit setting. We also test whether auditor cognitive style moderates these effects given prior findings that a misfit between task structure and cognitive style undermines performance. This research is motivated by concerns about integrated audit quality and auditors failing to consider control deficiencies indicated by client misstatements. We randomly assigned 147 auditors to four RCA treatments (No RCA, Unstructured RCA, Structured “5 Whys” RCA, Structured “Fishbone” RCA). As predicted, the results suggest that auditors using (not using) structured RCA are more (less) likely to identify control‐related root causes of a financial misstatement and judge the misstatement to be more (less) material. Also consistent with our prediction, we find that the assessed severity of identified control deficiencies mediates RCA's effect on materiality judgments. Finally, the materiality judgment results reveal the expected significant interaction between structured RCA method and auditor cognitive style, suggesting the importance of allowing flexibility in the specific RCA method applied in practice. Overall, the study's results provide evidence that auditor use of RCA as a judgment framework prompts deeper evaluation of audit findings and stronger consideration of critical links between financial reporting and related internal controls in integrated audit settings.
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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 | 0.021 | 0.144 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".