Assessing determinants of tax officials’ intention to continue applying e-tax in Vietnam: Attitude toward the continued application of e-tax as a mediator
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".