The Effects of Industry Specialization on Auditors' Inherent Risk Assessments and Confidence Judgements*
Bibliographic record
Abstract
Abstract This study experimentally examines how industry specialization affects auditors' inherent risk assessments and their confidence in those risk assessments. Two groups of participants ‐ experienced banking specialist auditors and equally experienced nonbanking auditors ‐ provided inherent risk assessments for a hypothetical banking client for two financial statement accounts. They assessed inherent risk for an industry‐specific account (loans receivable) and for a nonindustry‐specific account (property and equipment). The results indicate that nonbanking auditors assessed inherent risk significantly higher than industry specialists for all but the valuation assertion for the loans receivable account. However, the difference between the nonbanking auditors' and banking specialists' inherent risk assessments was not as great for the property and equipment account. Further, nonspecialists were less confident about the appropriateness of their inherent risk assessments compared with industry specialists. Potential implications for research and practice are discussed in light of the study's findings.
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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 | 0.017 | 0.133 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".