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Record W2337605591 · doi:10.5430/ijfr.v10n4p128

Accountants’ Perception on the Factors Affecting the Adoption of International Financial Reporting Standards in Yemen

2019· article· en· W2337605591 on OpenAlexvenueno aff
Mujeeb Saif Mohsen Al-Absy, Ku Nor Izah Ku Ismail

Bibliographic record

VenueInternational Journal of Financial Research · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAuditing, Earnings Management, Governance
Canadian institutionsnot available
Fundersnot available
KeywordsInternational Financial Reporting StandardsAccountingCertificationAffect (linguistics)Government (linguistics)BusinessPerceptionEconomicsPsychologyManagement

Abstract

fetched live from OpenAlex

This study examines accountants’ perception whether or not the following factors ــgovernment policy, capital market, economic growth, external environment/international exposure, professional bodies, education level of accountants, company size, initial cost of International Financial Reporting Standards (IFRSs) adoption and culture affect the adoption of IFRSs in Yemen. It also examines the differences of opinion among academicians and practitioners with regard to these factors. A questionnaire survey involving 41 Yemeni accounting postgraduate students in Malaysia’s public Universities was conducted. The results indicate that the majority of respondents believe that the lack of government policy, absence of capital market, lack of economic growth, lack of professional bodies, weakness in the education level of accountants, the small size of the companies and initial cost of IFRSs adoption affect the adoption of IFRSs in Yemen. The study also shows that the international environment has a weak effect on IFRSs adoption while the Yemeni culture does not affect IFRSs adoption. The finding may help both policy-makers and the Yemeni Association of Certified Public Accountants (YACPA) to consider these factors and make more precise decisions regarding IFRSs adoption.

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)0.010
metaresearch head score (Gemma)0.070
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 score0.167
Threshold uncertainty score0.937

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.070
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.043
GPT teacher head0.346
Teacher spread0.303 · 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
Published2019
Admission routes1
Has abstractyes

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