Audit-Planning Judgments and Client-Employee Compensation Contracts
Bibliographic record
Abstract
This paper investigates, in a laboratory setting, the impact of different types of client-employee compensation contracts on auditors' audit-planning judgments. Self-interested client-executive actions (motivated by executive incentive pay) have been claimed to be at the core of a recent large public company failure and the associated demise of the company's global auditors (Byrne et al. 2002). However, we know relatively little about how client-employee compensation contracts affect the planning choices of auditors. Our main result is that audit-planning judgments are greater (i.e., audit risk is assessed higher and the level of evidence required to perform the audit is assessed higher) if the bonuses are based on financial performance measures rather than nonfinancial performance measures. We also find that audit-planning judgments are greater (i.e., audit risk is assessed higher, internal controls are assessed weaker, and more substantive evidence is required) if client-employee compensation comprises a fixed salary plus bonuses, based on either financial or nonfinancial performance measures, rather than comprises a fixed salary only; however, we find only partial support for the finding with respect to nonfinancial measures. An important implication of these findings is that audit firms may need to pay careful attention to how auditors are trained in strategic systems auditing approaches that rely more on understanding a client's nonfinancial performance measures and less on transaction-based testing.
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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 | 0.155 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".