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Record W4388030036 · doi:10.5267/j.ijdns.2023.8.027

Assessing determinants of tax officials’ intention to continue applying e-tax in Vietnam: Attitude toward the continued application of e-tax as a mediator

2023· article· en· W4388030036 on OpenAlexvenueno aff
Thuy Thi Le Nguyen, Yen Thi Hai Mac, Minh Thi Nguyen, Viet Thi Hong Bui

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
KeywordsVietnameseTechnology acceptance modelStructural equation modelingModerationPsychologyUsabilityPublic economicsSocial psychologyBusinessEconomicsComputer science

Abstract

fetched live from OpenAlex

This paper aims to examine the mediating effect of attitude toward the continued application of e-tax in the association between perceived usefulness, ease of use, compatibility, and intention to continue applying e-tax based on empirical evidence from Vietnamese tax officials. Thereby, the research model is proposed and the hypotheses are developed on the basis of the Technology Acceptance Model (TAM). This study applies a quantitative analysis with a research sample of 343 tax officials from tax authorities at all levels within Vietnam. This study applies stratified and convenient sampling techniques. Structural equation modeling with AMOS was used to test the hypothesized relationships. The results revealed that among perceived usefulness, ease of use, and compatibility only perceived usefulness has a direct impact and positive relationship to the intention to continue applying e-tax and the attitude toward the continued application of e-tax also has a direct impact and positive relationship to the intention to continue applying e-tax. Especially, the results prove the mediating effects of attitude toward the continued application of e-tax on the relationships towards the intention to continue applying e-tax. This study contributes to both the literature and practice. The limitations and future research implications are discussed.

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)0.001
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 score0.074
Threshold uncertainty score0.242

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.091
GPT teacher head0.360
Teacher spread0.270 · 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

Citations5
Published2023
Admission routes1
Has abstractyes

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