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Record W2129193906 · doi:10.5539/ibr.v6n4p67

Internal Auditors’ Characteristics and Audit Fees: Evidence from Egyptian Firms

2013· article· en· W2129193906 on OpenAlexvenueno aff
Dalia A. Abbass, Mahmoud Mohmad Aleqab

Bibliographic record

VenueInternational Business Research · 2013
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAuditing, Earnings Management, Governance
Canadian institutionsnot available
Fundersnot available
KeywordsInternal auditAccountingAuditBusinessJoint auditWalk-through testCompetence (human resources)Chief audit executiveWork (physics)External auditorAudit planInformation technology auditInternal controlDocumentationComputer scienceManagementEconomicsEngineering

Abstract

fetched live from OpenAlex

Reliance of external auditors on the work of internal auditors is very important but yet, complex decision tasks that require professional judgment as it is influenced by a number of factors, characteristics of internal auditors are of the most important factors to be considered. The paper obtains various criteria relating to the evaluation of internal audit organizational status, work performed, competence, and professional due care as stipulated in Professional Auditing Standards including the Egyptian Auditing Standard (EAS)No.610 “Using the work of internal auditors”. This study revealed that internal auditors’ characteristics assist in increasing external auditors’ reliance on their works and so minimize external auditors’ efforts and so fees. Data on internal audit characteristics are obtained from survey respondents of Egyptian companies and audit fee data are obtained from their annual reports. Results indicate that lower external audit fees are associated with top management support for internal auditors, not imposing constraints on internal auditors works, is ready to act upon internal audit staff findings and recommendations, adequacy of education of internal auditors, good practices for hiring and training internal auditors. Also, lower external audit fees are associated with adequacy of working paper documentation supporting internal auditors’ conclusions, sufficiency of internal auditors’ evidences.

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)0.001
metaresearch head score (Gemma)0.020
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Scholarly communication, 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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.165
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0020.003
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.006

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 head0.036
GPT teacher head0.303
Teacher spread0.267 · 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

Citations22
Published2013
Admission routes1
Has abstractyes

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