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Record W4206562176 · doi:10.5430/afr.v11n1p13

The Demand for External Audit Quality: The Contribution of Agency Theory in the Context of Cameroon

2022· article· en· W4206562176 on OpenAlexvenueno aff
Michael Forzeh Fossung, Samuel Tanjeh Mukah, Kueda Wamba Berthelo, Motika Eubert Nsai

Bibliographic record

VenueAccounting and Finance Research · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAuditing, Earnings Management, Governance
Canadian institutionsnot available
Fundersnot available
KeywordsAgency costAuditContext (archaeology)Agency (philosophy)BusinessShareholderPrincipal–agent problemAccountingCreditorQuality auditJoint auditQuality (philosophy)EconomicsFinanceInternal auditCorporate governanceDebtSociology

Abstract

fetched live from OpenAlex

This study examines the effect of agency theory on the demand for external audit quality in Cameroon. Specifically, it looks at the impact of shareholder/manager agency cost, shareholders/creditors agency cost, and majority/minority shareholders agency cost on external audit quality demand in Cameroon. The focus is on a sample of 171 companies drawn from the regions of Littoral, Centre and North-West using questionnaires. We assess the explanatory power of agency theory on the demand for a better quality of audit in the Cameroonian context by modelling external audit quality as a function of agency costs. The logistic regression analysis allows us to study the nature of any possible interaction. The analysis shows that while an increase in shareholder/creditor agency cost and an increase in shareholder/manager agency cost negatively affect the demand for audit quality, the majority/minority agency cost and the size of the audited client positively and significantly affect the demand for audit quality.

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)0.002
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.066
Threshold uncertainty score0.130

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.031
GPT teacher head0.315
Teacher spread0.284 · 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 designTheoretical or conceptual
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

Citations10
Published2022
Admission routes1
Has abstractyes

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