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Record W2971205206 · doi:10.1111/1911-3846.12560

Implications of the Joint Provision of CSR Assurance and Financial Audit for Auditors' Assessment of Going‐Concern Risk

2019· article· en· W2971205206 on OpenAlexvenueno aff
Lorenzo Dal Maso, Gerald J. Lobo, Francesco Mazzi, Luc Paugam

Bibliographic record

VenueContemporary Accounting Research · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAuditing, Earnings Management, Governance
Canadian institutionsnot available
Fundersnot available
KeywordsAuditBusinessAccountingCorporate social responsibilityEarningsJoint auditFinancial AuditFinanceInternal auditPublic relationsPolitical science

Abstract

fetched live from OpenAlex

ABSTRACT We examine whether the joint provision of corporate social responsibility (CSR) assurance services and financial audit by the same audit firm influences auditors' assessment of going‐concern risk. We predict that the provision of CSR assurance and financial audit by the same audit firm creates CSR‐related knowledge spillovers from the CSR assurance team to the financial audit engagement team, which helps in the auditor's assessment of going‐concern risk. Using more than 28,000 firm‐year observations from 55 countries, we document that, relative to audit firms that provide only the financial audit, audit firms that provide both CSR assurance and financial audit for the same client (i) issue more frequent going‐concern opinions and have lower Type II going‐concern errors, (ii) have clients that book larger environmental and litigation provisions, (iii) report earnings that are more persistent and value‐relevant and are less likely to book income‐decreasing earnings restatements, and (iv) do not charge higher audit fees or total fees. Our results are important especially because of firms' increasing exposure to CSR risks and the growing number of countries that require assurance of CSR reports.

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)0.004
metaresearch head score (Gemma)0.012
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.070
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.035
GPT teacher head0.310
Teacher spread0.276 · 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

Citations131
Published2019
Admission routes1
Has abstractyes

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