The Effects of Auditor Affinity for Client and Perceived Client Pressure on Auditor Proposed Adjustments
Bibliographic record
Abstract
ABSTRACT This paper examines how auditors' judgments about accounting policies may differ when experiencing different levels of affinity for client management and facing different levels of pressure from client management. The theory of motivated reasoning is employed to analyze the effects of these two factors that should lead individual auditors to adopt as a directional goal the acceptance of client management's aggressive accounting. Accordingly, we predict and find that auditors experiencing greater client affinity and facing explicit client pressure suggest lower adjustments to clients' aggressive accounting, consistent with motivated reasoning's goal-related predictions. But our study goes further and investigates also how auditors react when motivated reasoning theory's “reasonableness constraint” is potentially violated by auditors who perceive excessive client pressure. We predict and find, consistent with the individual auditor's “reasonable constraint” being triggered in at least some auditors, that perception of client pressure intensity leads those auditors to propose larger adjustments to client accounting. To support our findings, we re-analyze the data from a prior motivated reasoning audit experiment, replicate that study's reported directional goal results employing methods used in this study and, in addition, find similar results to those found in this study for increased client pressure intensity on auditor judgment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.082 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".