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Taxing digital assets in emerging economies: a lesson from a systematic literature review

Nidya Hapsari et al.

International Journal of Technology, Policy and Management2026https://doi.org/10.1504/ijtpm.2026.152568article
AJG 1
Weight
0.50

What the paper says

Digital assets, currently narrowed down to cryptographic digital financial assets (DFAs), bring significant trading volume and technological innovation. Considering the risks and benefits of their rapid growth, this study aims to outline the urgencies of governments in emerging economies for regulating and taxing DFA transactions and identify which DFAs should be prioritised. Employing the systematic literature reviews (SLRs) method and preferred reporting items for systematic reviews and meta-analyses (PRISMA) on 529 peer-reviewed papers published during 2019-2024, the study concluded that, first, the main rationale for emerging middle-income countries to regulate DFAs was to reduce uncertainties due to DFAs' transaction nature, rather than optimising their potential benefits. Second, cryptocurrencies were the most popular DFAs in research interests and trading demand. Third, taxation primarily responded to DFAs' opportunities and threats through its future sustainability role. Therefore, governments should build a legal framework, straightforward taxonomy and well-articulated guidelines on cryptocurrency taxation.

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Cite this paper

https://doi.org/https://doi.org/10.1504/ijtpm.2026.152568

Or copy a formatted citation

@article{nidya2026,
  title        = {{Taxing digital assets in emerging economies: a lesson from a systematic literature review}},
  author       = {Nidya Hapsari et al.},
  journal      = {International Journal of Technology, Policy and Management},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1504/ijtpm.2026.152568},
}

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Taxing digital assets in emerging economies: a lesson from a systematic literature review

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.