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Record W3043743005 · doi:10.5430/ijfr.v11n4p556

Impact of External Auditing Quality and Audit Committees on Accounting Conservatism and the Performance of Industrial Firms Listed at the Amman Stock Exchange

2020· article· en· W3043743005 on OpenAlexvenueno aff
Mahmod AL-Rawashdeh Abdalwahab, Riham Alkabbji

Bibliographic record

VenueInternational Journal of Financial Research · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAuditing, Earnings Management, Governance
Canadian institutionsnot available
Fundersnot available
KeywordsAccountingBusinessAuditQuality auditJoint auditChief audit executiveAudit evidenceAccrualConservatismAudit planStock exchangeAudit committeeWalk-through testAuditor independenceInformation technology auditInternal auditFinanceEarnings

Abstract

fetched live from OpenAlex

This study aims to investigate the impact of external auditing quality and audit committees on accounting conservatism and the performance of industrial firms listed at the Amman Stock Exchange. The study has relied on the industrial sector which comprised of 70 industrial firms, while the sample size comprised 54 firms. Audit quality has been addressed in terms of audit quality features (communication with world audit offices, keep client period, audit fees, accounting specialist in the customer industry), while audit committees quality has been analyzed (audit committees independence, audit committees expertise, audit committees meetings). The accruals model has been used to measure accounting conservatism. The firm`s performance has been measured through its market value. The study concluded that audit committees have no impact on accounting conservatism and firm`s performance, while audit quality features have a positive impact on accounting conservatism and firm`s performance. The study has recommended the need to activate the role of audit committees in public shareholding industrial firms since they positively have an impact on raising audit efficiency and control the regulate the firm`s internal control system.

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)0.022
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 score0.453
Threshold uncertainty score0.986

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.022
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
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 head0.103
GPT teacher head0.357
Teacher spread0.255 · 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

Citations15
Published2020
Admission routes1
Has abstractyes

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