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Unveiling the drivers of fintech adoption and usage: a systematic literature review

Payal Goyal et al.

International Journal of Technological Learning, Innovation and Development2025https://doi.org/10.1504/ijtlid.2025.149344article
AJG 1
Weight
0.50

What the paper says

In an era characterised by thriving popularity of cutting-edge technologies, the significance of financial technology or fintech in the finance sector requires no further demonstration. This study thoroughly investigates the driving forces behind fintech adoption based on a systematic literature review (SLR) of 97 studies from 1980 to 2023. It uncovers demand drivers such as customer trust, ease of use, government influence, etc. that fuel the usage of fintech solutions. Concurrently, it sheds light on supply drivers that propel fintech innovation such as survival in a competitive environment, technological advancements, etc. The study also reveals significant gaps in existing research such as the lack of studies on financial literacy and regulatory frameworks as drivers, as well as fintech adoption across different demographics. The findings not only provide a roadmap for financial institutions, policymakers and fintech firms but also lay a groundwork for future research in this competitive and ever-evolving landscape.

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Cite this paper

https://doi.org/https://doi.org/10.1504/ijtlid.2025.149344

Or copy a formatted citation

@article{payal2025,
  title        = {{Unveiling the drivers of fintech adoption and usage: a systematic literature review}},
  author       = {Payal Goyal et al.},
  journal      = {International Journal of Technological Learning, Innovation and Development},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1504/ijtlid.2025.149344},
}

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Unveiling the drivers of fintech adoption and usage: 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.