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Record W4281397664 · doi:10.5539/ibr.v15n6p88

Potential Threats to Audit Firm Independence: Evidence from Italy on Audit Quality

2022· article· en· W4281397664 on OpenAlexvenueno aff
Marco Angelo Marinoni, Anna Maria Fellegara, Andrea Lippi

Bibliographic record

VenueInternational Business Research · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicRisk Management in Financial Firms
Canadian institutionsnot available
Fundersnot available
KeywordsAuditCredibilityAccountingAuditor independenceQuality auditBusinessBig FourAccrualExternal auditorJoint auditActuarial scienceInternal auditEarningsPolitical science

Abstract

fetched live from OpenAlex

Purpose – This study aims to investigate the potential threats to the independence of an auditor who provides both auditing and non-audit services (NAS), in terms of credibility of and confidence in audit quality. Design/methodology/approach – In this study, we first replicate the results of Campa and Donnelly (2016) using hand-collected publicly available data for a sample of 91 Italian manufacturing public companies audited by a Big 4 and non-Big 4 audit firm over a longer time horizon (2015˗2019) using the panel data approach, based on three interconnected regression models. Findings – Previous studies in this area did not find a unique interpretation of the association between auditor independence and provision of NAS. Our findings reveal that auditor independence, as measured by the magnitude of discretionary accruals, is compromised by the provision of NAS, especially when unexpected audit fees are lower than expected. Enhanced credibility can lead to greater confidence in audit value. This study’s results should be of interest to European and U.S. legislators, to improve financial reporting quality. Originality/value – In the wake of the global financial crisis and loss of confidence in the role of auditors, this study investigates the supposed threats, to to aim to enhance the credibility of and confidence in audit quality, especially in settings outside the Anglosphere. This study would contribute to the literature to support the more binding approach for audit firms.

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)0.003
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.526
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.002
Open science0.0030.004
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0080.005

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 head0.117
GPT teacher head0.391
Teacher spread0.274 · 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; both teacher heads agree on what is shown here.

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

Citations1
Published2022
Admission routes1
Has abstractyes

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