Minimising exposure to cyber frauds in digital finance: perspectives from technology threat avoidance theory

Rakhi Peswani & Priyanka Vijay

American Journal of Finance and Accounting2026https://doi.org/10.1504/ajfa.2026.151476article
ABDC C
Weight
0.50

What the paper says

Leveraging technological advancements related to digitisation for financial transactions is convenient, yet it is not free of challenges. Cyber frauds, particularly related to the infringement of financial data and deceitfulness related to monetary means have become largely prevalent. This study aims to revisit the technology threat avoidance theory (TTAT) by incorporating the elements of attitude and perceived financial loss, taken as essential components. This is done in the context of cyber frauds that befall in the online payment domain. Based on the constructs, an initial model was developed to show the relationships. Regression and correlation analysis were used to validate the model after the constructs went through exploratory factor analysis. Based on the data collected from 118 online payment platform users, the findings exhibit that while the perceived threat and attitude were not found to have a significant impact on avoidance motivation, perceived financial loss depicted contrasting results. This shows that attitudes towards fraud and perceived threats were not capable of motivating online payment users to avoid fraud.

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https://doi.org/https://doi.org/10.1504/ajfa.2026.151476

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@article{rakhi2026,
  title        = {{Minimising exposure to cyber frauds in digital finance: perspectives from technology threat avoidance theory}},
  author       = {Rakhi Peswani & Priyanka Vijay},
  journal      = {American Journal of Finance and Accounting},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1504/ajfa.2026.151476},
}

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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.