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Record W1976219397 · doi:10.5539/ass.v8n13p192

The Influence of Culture on Financial Reporting Quality in Malaysia

2012· article· en· W1976219397 on OpenAlexvenueno aff
Hafiza Aishah Hashim

Bibliographic record

VenueAsian Social Science · 2012
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAuditing, Earnings Management, Governance
Canadian institutionsnot available
Fundersnot available
KeywordsCorporate governanceAccountingAccrualEthnic groupMalayContext (archaeology)BusinessQuality (philosophy)Government (linguistics)FinancePolitical scienceLawGeography

Abstract

fetched live from OpenAlex

The purpose of this study is to investigate whether financial reporting quality relates to national culture. Besides the conventional corporate governance mechanism of the board of directors and substitute corporate governance mechanism of ownership structure, culture and religious traditions have been considered as having an important influence on corporate governance systems employed in any one country. Malaysia is a multiethnic society with Chinese and Malays dominating economics and politics in Malaysia. Ethnicity acts as a suitable surrogate for culture in Malaysia, which has a multiracial society, each section of which still maintains its own unique ethnic identity and values. This study uses a discretionary component of the accrual quality model as a measure for financial reporting quality to examine the association between ethnicity and financial reporting quality. This study finds no significant relationship between the race of chairman and race of CEO and accrual quality. Interestingly, this study reports higher financial reporting quality associated with firms dominated by Malay directors. The finding of this study suggests that the quality of financial reporting cannot be culturally free and is impacted largely by government policy. This study offers an alternative explanation on the association between governance and financial reporting quality by examining the role of ethnicity that explicates the unique institutional context of an Asian country.

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)0.029
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 score0.517
Threshold uncertainty score0.979

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.029
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.015
GPT teacher head0.280
Teacher spread0.265 · 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

Citations35
Published2012
Admission routes1
Has abstractyes

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