The Impact of Internal Auditor Compensation and Role on External Auditors' Planning Judgments and Decisions*
Bibliographic record
Abstract
Abstract This paper reports the results of an experiment that investigates how external audit planning is affected when internal auditors have incentives and the opportunity to bias their evaluations. Specifically, we draw on attribution theory to examine how internal auditor eligibility for incentive compensation and participation in consulting (i.e., two factors that provide incentives to bias audit evaluations) affect external audit planning. In addition, we examine the effects of incentive compensation and a consulting role across two routine internal audit tasks — an objective tests of controls task and a subjective inventory valuation task — to evaluate whether their effects are contingent upon task subjectivity (i.e., opportunity to bias audit evaluations). Seventy‐six external auditors from four Big 5 public accounting firms participated in an experiment that manipulated internal auditor compensation (fixed salary versus incentive compensation), the type of work that the internal auditors routinely perform (primarily auditing versus primarily consulting), and audit task subjectivity (objective tests of controls versus subjective inventory valuation). Our results suggest that the nature of internal auditors' compensation and work affect audit planning recommendations differently. The opportunity to receive incentive compensation results in less reliance on internal auditors' work and greater budgeted audit hours, but only for the subjective task. Although a consulting role decreases perceived internal auditor objectivity, it has a limited effect on planning recommendations. Specifically, consulting has no effect on reliance, and leads to greater budgeted audit hours only when incentive compensation is available. We discuss potential explanations for the results as well as implications for audit research, practice, and regulation.
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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 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".