Bibliographic record
Abstract
Abstract In this study, we present a nonstrategic, dynamic Bayesian model in which auditors' learning on the job and their choice of professional services jointly affect audit quality. While performing audits over time, auditors accumulate client‐specific knowledge so that their posterior beliefs about clients are updated and become more precise (that is, precision is our surrogate for audit quality) — what we call the learning effect. In addition, auditors can enrich their knowledge accumulation by performing nonaudit services (NAS) that, in fact, may influence clients' managerial decisions — what we call the business advisory effect. This advisory effect permits auditors to anticipate and to learn about changes in clients' business models, which in turn improves their advisory capacity. These dual “learning” and “advisory” effects are interdependent and mutually reinforcing. The advisory effect of NAS may increase or reduce auditors' engagement risk. We show that large professional fees can induce auditors to provide NAS that increase engagement risk and diminish audit quality. However, when NAS reduce engagement risk and increase audit quality, auditors may provide NAS without charging clients. The feature that distinguishes our study — the interdependence between the learning and advisory effects — provides new insight into the trade‐off between audit fees and audit quality. Consequently, our analysis helps explain why the scope of the audit has evolved over time and why the boundaries between audit and NAS are constantly shifting. A recent example of such a shift is that the Sarbanes‐Oxley Act adds control attestation to audits for public companies traded in U.S. markets.
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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 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".