Determining the Factors of Tax Agents’ Readiness Towards the Digitalisation of Tax Administration
Bibliographic record
Abstract
The increase in digital transformation through Industry 4.0 has become a top priority for both businesses and governments on a worldwide scale, thus forcing the need for updating the tax system.With substantial technological advancements and accelerating demands to save costs, many tax authorities are undergoing a considerable transformation from the conventional to an online platform in engaging with taxpayers and tax agents; however, the tax agents responsible for advising and assisting companies or individuals with income tax matters may not be ready for such a transformation.This study aims to examine tax agents' readiness towards the digitalisation of tax administration in Malaysia.By using the probability sampling technique, a list of tax agents was obtained from the Inland Revenue Board Malaysia (IRBM)'s website and data were collected from the structured questionnaires distributed to the IRBM-registered tax agents in Malaysia.Considering that Malaysia is still at an early stage of fully digitalising its tax system, the findings of this study are, therefore, important for many parties involved and the body of knowledge.By selecting 173 respondents that consist of tax agents listed with the Inland Revenue Board Malaysia (IRBM), this study employed Quadrant Analysis to determine the factors of priority in supporting the readiness towards the digitalisation of tax administration.Based on the findings, the three elements that demand immediate attention from the policymakers are perceived ease of use, technology infrastructure, and government policy and support.These findings are crucial for policymakers who intend to speed up the digital transformation process to improve the efficiency and effectiveness of tax administration.This study should be able to ascertain the factors that influence tax agents' readiness to deal with the digitalisation of Malaysian tax administration.There is a paucity of research on the digitalisation of tax administration, particularly for services to taxpayers that should have been fully digitalised years ago.This study should contribute to the literature by identifying the factors that contribute to the delay in fully digitalising tax administration in Malaysia.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".