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Record W1594250970 · doi:10.5539/ass.v11n15p244

Directors’ Independence, Internal Audit Function, Ownership Concentration and Earnings Quality in Malaysia

2015· article· en· W1594250970 on OpenAlexvenueno aff
Ahmed Hussein Al-Rassas, Hasnah Kamardin

Bibliographic record

VenueAsian Social Science · 2015
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAuditing, Earnings Management, Governance
Canadian institutionsnot available
Fundersnot available
KeywordsAccrualAccountingAudit committeeBusinessEarnings qualityCorporate governanceShareholderQuality auditChief audit executiveEarningsIndependence (probability theory)AuditSample (material)Principal–agent problemQuality (philosophy)Internal auditJoint auditFinanceStatistics

Abstract

fetched live from OpenAlex

Concentration of ownership in Malaysian public listed companies contributes to agency conflict betweenmajority and minority shareholders. An effective monitoring mechanism is critical to mitigate this conflict. Thestudy aims to examine the influence of board and audit committee independence, internal audit function andownership concentration on earnings quality proxied by discretionary accruals. The sample of the study 508companies listed on the Bursa Malaysia Main Market from 2009 to 2012. Two measures of discretionaryaccruals are used: Modified Jones model (Dechow et al., 1995); and extended Modified Jones Model (Yoon etal., 2006). Using OLS regression, results of the study suggest that audit committee independence and moreinvestment in internal audit function are related to higher earnings quality. However, board of directors’independence and ownership concentration are associated with lower earnings quality. The finding indicates theimportance of audit committee independence in producing quality financial reporting. Consistent findings arefound for most variables in both models. The findings of the study have implication on the use of measurementof discretionary accruals in earnings quality studies and corporate governance practices in Malaysia.

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)0.002
metaresearch head score (Gemma)0.005
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 score0.241
Threshold uncertainty score0.584

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.002
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 head0.020
GPT teacher head0.258
Teacher spread0.237 · 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

Citations16
Published2015
Admission routes1
Has abstractyes

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