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Record W4388110018 · doi:10.5267/j.ijdns.2023.10.017

Investigation the effect of digital taxation and digital accounting on customs efficiency and port sustainability

2023· article· en· W4388110018 on OpenAlexvenueno aff
Omar M. Shubailat, Murad Ali Ahmad Al-Zaqeba, Aziz Madi, Khairil Faizal Khairi

Bibliographic record

VenueInternational Journal of Data and Network Science · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicTaxation and Compliance Studies
Canadian institutionsnot available
Fundersnot available
KeywordsSustainabilityBusinessAccountingPort (circuit theory)Compliance (psychology)Process (computing)Industrial organizationEnvironmental economicsEconomicsEngineeringComputer science

Abstract

fetched live from OpenAlex

This paper Exploration effect of digital taxation and digital accounting on customs efficiency and corporate sustainability at customs ports. Traditional tax and accounting procedures have undergone a radical transformation thanks to digital technology, which also provides more efficient methods that can have a big influence on customs operations. This article tries to clarify the interconnectivity between digital taxes, digital accounting, and the customs environment by a detailed analysis of customs-related factors such as compliance, efficiency, and sustainability. By automating compliance checks and streamlining tax-related activities, digital taxation is shown to considerably improve customs efficiency and efficiently comply with sustainability goals. Digital accounting simultaneously increases data accuracy and process efficiency, enhancing the sustainability of customs ports and significantly enhancing customs efficiency. The results of this study have significance for both customs agencies and businesses, offering the possibility of improving operational effectiveness, compliance observance, and sustainability practices. However, the necessity for continual research that concentrates on cutting-edge technology and legal frameworks is highlighted by several restrictions. By offering actual proof of the beneficial effects of digital technology on customs operations and sustainability, this study adds to the body of knowledge and lays the groundwork for further in-depth investigations.

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)0.001
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 score0.025
Threshold uncertainty score0.278

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.280
Teacher spread0.246 · 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

Citations15
Published2023
Admission routes1
Has abstractyes

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