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Record W3123109308 · doi:10.1111/1911-3846.12104

Audits of Complex Estimates as Verification of Management Numbers: How Institutional Pressures Shape Practice

2014· article· en· W3123109308 on OpenAlexvenueno aff
Emily E. Griffith, Jacqueline S. Hammersley, Kathryn Kadous

Bibliographic record

VenueContemporary Accounting Research · 2014
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAuditing, Earnings Management, Governance
Canadian institutionsnot available
Fundersnot available
KeywordsAuditNoticeAccountingProcess (computing)Internal auditBusinessProcess managementComputer sciencePolitical scienceLaw

Abstract

fetched live from OpenAlex

Abstract Auditors and regulators have invested heavily in improving audits of estimates in recent years, but problems in this area persist. We examine the causes of these problems and why they persist. To do so, we interview 24 very experienced auditors about how they audit complex accounting estimates such as fair values and impairments and what problems they experience in the process. We find that auditors overwhelmingly choose to audit the details of management's estimate rather than use other allowable approaches. The steps auditors describe and the language they use to describe those steps indicate that they follow a process of verifying individual elements of management's assertions on a piecemeal basis, resulting in overreliance on management's process, rather than engaging in a critical analysis of the overall estimate. The problems that auditors identify are consistent with this view, and include failures to notice inconsistencies among the estimate and other internal data or external conditions and overreliance on specialists to identify, evaluate, and challenge critical assumptions. We interpret these processes and problems using institutional theory and identify two root causes: standards' and firm policies' emphasis on verifying management's model, and audit firms' division of knowledge between auditors and specialists. Institutional theory proposes these conventions arise from firms extending use of procedures that are legitimate in one area (i.e., auditing accounts without significant uncertainty) to a new area (i.e., auditing complex estimates), even though they are likely less effective in the new area. These conventions are reinforced by regulators' method of inspection and by firms' reluctance to change methods without a prompt to change to a clearly better method. We argue that these institutionalized conventions thwart auditors' good‐faith attempts to engage in skeptical analysis of estimates. Thus, audit quality problems are likely to persist.

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 imitation

Not 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.

metaresearch head score (Codex)0.181
metaresearch head score (Gemma)0.407
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.181
Threshold uncertainty score0.959

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1810.407
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0050.004
Science and technology studies0.0090.037
Scholarly communication0.0220.013
Open science0.0030.012
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.0030.000

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 head0.057
GPT teacher head0.321
Teacher spread0.264 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations402
Published2014
Admission routes1
Has abstractyes

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