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Record W2791844550 · doi:10.1108/ijaim-08-2016-0077

The effect of culture on accounting conservatism during adoption of IFRS in the EU

2018· article· en· W2791844550 on OpenAlexaff
Daniel Zéghal, Zouhour Lahmar

Bibliographic record

VenueInternational Journal of Accounting and Information Management · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAuditing, Earnings Management, Governance
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsConservatismAccountingAccrualOriginalityHofstede's cultural dimensions theoryInternational Financial Reporting StandardsSample (material)European unionUncertainty avoidanceTest (biology)BusinessEconomicsPolitical scienceSociologyLawSocial scienceInternational tradePoliticsEarnings

Abstract

fetched live from OpenAlex

Purpose This paper aims to examine the impact of culture on accounting conservatism during transition to international standards. Design/methodology/approach The sample used in this analysis consists of 15 countries of the European Union that have adopted International financial reporting standards (IFRS) pursuing Regulation N° 1606/2002. The study covers the 2000-2010 period. Two conservatism measures are used, the Basu (1997) measure to account for conditional conservatism and the accruals measure to account for unconditional conservatism. To test the impact of culture, the six dimensions of Hofstede (1980, 2010) are used. Findings The results of the analysis show that variation of conditional conservatism is influenced by the six cultural dimensions. However, unconditional conservatism is only affected by power distance. Originality/value The results of the study are interesting and provide a better understanding of the adoption of IFRS worldwide. The role of culture in explaining accounting practices after adopting a single set of accounting standards is particularly highlighted.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.609
Threshold uncertainty score0.402

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.003
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 head0.004
GPT teacher head0.214
Teacher spread0.210 · 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

Citations39
Published2018
Admission routes1
Has abstractyes

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