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Record W3125777979 · doi:10.1111/1911-3846.12162

Differences in Auditors' Materiality Assessments When Auditing Financial Statements and Sustainability Reports

2015· article· en· W3125777979 on OpenAlexvenueno aff
Robyn Moroney, Ken T. Trotman

Bibliographic record

VenueContemporary Accounting Research · 2015
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Social Responsibility Reporting
Canadian institutionsnot available
Fundersnot available
KeywordsMateriality (auditing)AuditAccountingFinancial statementBusinessAudit riskSustainability reportingSustainabilityPolitical sciencePublic relationsCorporate social responsibility

Abstract

fetched live from OpenAlex

Abstract With increased interest in voluntary sustainability reports from investors and other stakeholders, more companies are having these reports assured. The issue of what is considered material in these assurance engagements is important, and yet research on materiality has focused only on financial statement audits. This article reports the results of an experiment where auditors assess the materiality of audit differences in the same magnitude for both a financial audit and a sustainability (water) assurance engagement. Two factors, the risk of breaching a contract and community impact, are manipulated between‐subjects. We find that auditors assess the materiality of an audit difference significantly higher for a financial case than for a water case. This difference is significantly greater when there is no risk of breaching a contract than when there is a risk of breaching a contract. The risk of breaching a contract has a stronger effect on the difference in auditors' materiality assessments when there is no community impact than when there is a community impact. Overall our findings suggest that qualitative factors have a greater impact on sustainability (water) materiality assessments than on financial statement materiality assessments when an audit difference is between 5 percent and 10 percent of a relevant base. Understanding the factors that impact material judgments in sustainability reports is important as these factors affect the reliability of the reported disclosures.

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)0.027
metaresearch head score (Gemma)0.026
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.190
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0270.026
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0020.005
Open science0.0000.002
Research integrity0.0000.001
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 head0.129
GPT teacher head0.401
Teacher spread0.273 · 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

Citations125
Published2015
Admission routes1
Has abstractyes

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