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Record W4400289807 · doi:10.3390/jrfm17070278

Impact of Mandatory Audits of Small- and Medium-Sized Enterprises on Their Income Tax Compliance: Evidence from the Egyptian Small- and Medium-Sized Enterprise Stock Market

2024· article· en· W4400289807 on OpenAlexvenueno aff
A. Mohamed, Shengdao Gan

Bibliographic record

VenueJournal of risk and financial management · 2024
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicTaxation and Compliance Studies
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessAuditCompliance (psychology)AccountingSmall and medium-sized enterprisesStock marketIncome taxSmall businessFinancePublic economicsEconomics

Abstract

fetched live from OpenAlex

Small- and medium-sized enterprises are essential to the economies of nearly all countries, as they directly influence the GDP and tax revenue. In 2019, the European Federation of Accountants and Auditors for SMEs surveyed SME account users, revealing that tax authorities were the most common recipients of the company’s accounts, accounting for 61.40% of cases. This study, from a macroeconomic perspective, aims to uncover evidence of the impact of mandatory audits of Egyptian SMEs on their income tax compliance. It also seeks to explore the Egyptian SME tax environment to fill the knowledge gap by exploring perceptions of SMEs’ tax performances and their levels of tax. This study provides evidence that the taxpayers’ tax compliance behavior is an essential and decisive factor in the compliance of Egyptian SMEs with income tax. The results show that SME management is less persuaded by the potential benefits of mandatory audits on tax compliance than auditors and academics. Also, the study found experimental evidence confirming that the mandatory auditing of SMEs positively impacts their compliance with income tax. Additionally, this study developed a tax compliance scale (the RTRP scale) that effectively suits the nature and characteristics of SMEs, enabling the quantitative measurement of their compliance levels with income tax, as well as comparisons between SMEs.

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)0.001
metaresearch head score (Gemma)0.000
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 score0.071
Threshold uncertainty score0.683

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.034
GPT teacher head0.256
Teacher spread0.222 · 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

Citations6
Published2024
Admission routes1
Has abstractyes

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