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Record W53237573 · doi:10.1108/ijaim-05-2016-0054

Monitoring function of the board and audit fees: contingent upon ownership concentration

2017· article· en· W53237573 on OpenAlexaffabout
Richard Bozec, Mohamed Dia

Bibliographic record

VenueInternational Journal of Accounting and Information Management · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsLaurentian UniversityUniversity of Ottawa
Fundersnot available
KeywordsVotingAccountingCorporate governanceExpropriationCash flowShareholderBusinessSample (material)Panel dataPrincipal–agent problemAuditPrincipal (computer security)EconomicsEconometricsFinancePoliticsLawMarket economyPolitical science

Abstract

fetched live from OpenAlex

Purpose The aim of this paper is to revisit the board independence–audit fees (BI–AF) relationship while taking into account the ownership structure of the firm. Two effects are unfolding along the ownership concentration spectrum: separation of ownership and control (principal–agent problems) and separation of voting and cash flow rights (principal–principal problems). Design/methodology/approach The study is conducted over a seven-year period (2002-2008) using panel regressions on a sample of Canadian publicly traded companies. The authors use a moderated regression analysis incorporating two-way interactive terms (ownership × BI) and a sub-group analysis. Findings The results show a positive and significant relationship between BI and AF when ownership is concentrated in the hands of a dominant/controlling shareholder. The higher the gap between voting and cash flow rights of the ultimate owner, the stronger the relationship between BI and AF. Overall, evidence supports both the demand-based perspective on AF and the expropriation effect argument. Practical implications Results support a one-size-fits-all approach to governance despite growing concerns from academics and interest groups about the appropriateness of pursuing such strategy when ownership is concentrated in the hands of a dominant/controlling shareholder. Originality/value By taking the excess voting rights into account (difference between voting rights and cash-flow rights of the ultimate owner), the authors propose a refined classification of the sample firms along the ownership concentration spectrum.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.265
Threshold uncertainty score0.787

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.007
Open science0.0000.000
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 head0.014
GPT teacher head0.222
Teacher spread0.208 · 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.

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

Citations29
Published2017
Admission routes2
Has abstractyes

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