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Record W2588567828 · doi:10.2308/accr-51703

The Effects of Auditor Affinity for Client and Perceived Client Pressure on Auditor Proposed Adjustments

2017· article· en· W2588567828 on OpenAlexaff
Christopher Koch, Steven E. Salterio

Bibliographic record

VenueThe Accounting Review · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAuditing, Earnings Management, Governance
Canadian institutionsQueen's University
Fundersnot available
KeywordsAuditAccountingConstraint (computer-aided design)PerceptionPsychologyBusinessReplicateStatistics

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.031
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.619
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.031
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.011
GPT teacher head0.252
Teacher spread0.241 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations116
Published2017
Admission routes1
Has abstractyes

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