A conceptual framework for digital tax administration - A systematic review
Bibliographic record
Abstract
Tax administrations worldwide have become highly digitised with a diverse and sophisticated array of e-services to enhance the taxpayer experience. Nevertheless, given the high rates of failure of e-government services, it is critical to understand the factors that are essential to the success of a digital tax system. Drawing on a systematic review of ninety-six publications across the digital taxation, taxation, and information systems (IS) literature, a comprehensive conceptual framework is developed to improve our success of digital services in tax administration. The conceptual framework identifies fifteen themes for consideration by policymakers when designing digital services in tax administrations clustered around four categories – Context, Stakeholders, Technology and Demonstrated Results. The framework should also serve as a reference point in successfully developing strategies and measures to embed digital services in tax administrations. Future research directions are also proposed based on the conceptual framework that will help advance our understanding of digital services in tax administration beyond technology acceptance models.
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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 | 0.105 | 0.174 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.010 |
| Bibliometrics | 0.096 | 0.058 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.013 | 0.021 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".