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Record W4280551640 · doi:10.1111/1911-3838.12308

Mandatory Disclosure of Engagement Partner Identity: Insights from Practice*

2022· article· en· W4280551640 on OpenAlexaffvenueabout
Veena L. Brown, Jodi L. Gissel, Adam Vitalis

Bibliographic record

VenueAccounting Perspectives · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAuditing, Earnings Management, Governance
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsAccountabilityAuditReputationBusinessMandateTransparency (behavior)Public relationsAccountingQuality (philosophy)Political science

Abstract

fetched live from OpenAlex

ABSTRACT This study uses semistructured interviews to gain insights from 19 practicing Canadian audit partners into the practical implications of the engagement partner identity mandate requiring firms to disclose the identity of the engagement partner(s) auditing Canadian publicly traded companies. Building on prior literature that suggests accountability can reach a ceiling, we explore whether audit partners perceive incremental increases in accountability pressures to be effective in increasing audit quality. Based on the existing literature, we propose a nonlinear relation between accountability and performance (audit quality, in the current context), reflecting this ceiling effect. We find partners believe they are reaching, or are at, a ceiling level of accountability and that further initiatives to increase their accountability are ineffective in eliciting procedural changes in the audit or the audit's outcome. Contrary to regulators' motives for the disclosure, our interviewed partners do not believe the transparency of publicly disclosing their names will further increase their level of accountability or overall audit quality. We document that one possible reason for the disconnect is that partners are less concerned with managing external reputation than with managing internal reputation, which they believe has a more direct impact on their careers. We also discuss partners' perceptions of the required disclosure's impact on individual reputations, client risk choices, personal safety, and partner recruitment. We offer suggestions for future research building on the partners' insights.

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)0.001
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.820
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.005
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.012
GPT teacher head0.248
Teacher spread0.235 · 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

Citations7
Published2022
Admission routes3
Has abstractyes

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