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Record W2972588079 · doi:10.1111/1911-3846.12565

The Role of Auditor Narcissism in Auditor‐Client Negotiations: Evidence from China

2019· article· en· W2972588079 on OpenAlexvenueno aff
Bryan K. Church, Narisa Tianjing Dai, Xi Kuang, Xuejiao Liu

Bibliographic record

VenueContemporary Accounting Research · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAuditing, Earnings Management, Governance
Canadian institutionsnot available
Fundersnot available
KeywordsNegotiationNarcissismAuditBusinessAccountingPsychologyAccrualQuality auditYield (engineering)Social psychologyPolitical sciencePublic relations

Abstract

fetched live from OpenAlex

ABSTRACT This paper reports the results of three studies (archival, experimental, and qualitative) designed to examine the effects of auditor narcissism on auditor‐client negotiations in China. We contend that narcissistic characteristics fuel auditors' competitiveness and embolden them to stand firm in negotiations, potentially lengthening the negotiation process but leading to more conservative negotiation outcomes. As predicted, our archival results suggest that auditor narcissism is positively associated with audit delay and negatively associated with clients' absolute and positive discretionary accruals. Our experimental results document that narcissistic auditors are more likely to be involved in negotiations that reach an impasse or take longer to resolve and that narcissistic auditors negotiate reported asset values that reflect less aggressive reporting choices. Our qualitative results from field interviews with practicing audit partners corroborate our archival and experimental findings. Overall, the data collected using three different research methods yield consistent results in support of our theory. Our findings shed light on factors that influence audit efficiency and quality in China. We discuss the key cultural and contextual differences between China and the West as well as the implications of these differences for future research.

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)0.005
metaresearch head score (Gemma)0.022
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.350
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.022
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.003
Open science0.0020.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.025
GPT teacher head0.282
Teacher spread0.257 · 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 designObservational
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

Citations85
Published2019
Admission routes1
Has abstractyes

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