Insight into how legal and ethical considerations of artificial intelligence enhance the effectiveness of cyber forensic accounting

Pham Quang Huy & Vu Kien Phuc

Journal of Global Information Technology Management2025https://doi.org/10.1080/1097198x.2025.2480972article
AJG 2ABDC B
Weight
0.41

Abstract

The aim of this study is to explore the correlation between legal and ethical considerations (LEC) and the effectiveness of cyber forensic accounting (ECFA). This study investigates the role of artificial intelligence competencies (AIC) as a mediator in the link between LEC and ECFA. This research confirms and delineates the relationship among LEC, ECFA, and AIC through a hypothetico-deductive approach. The statistical database was generated from a paper-and-pencil survey administered to a cross-sectional sample of 723 respondents using convenience and snowball sampling methods. The partial least squares structural equation modeling was implemented to evaluate the measurement and structural models. An analysis of the results indicated that LEC exerted a significant and favorable influence on ECFA. Furthermore, it illustrated that AIC partially mediated the link between LEC and ECFA. Furthermore, the study’s outcomes would aid the Government in improving and methodically formulating strategies to foster the implementation of forensic accounting and the artificial intelligence application.

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https://doi.org/https://doi.org/10.1080/1097198x.2025.2480972

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@article{pham2025,
  title        = {{Insight into how legal and ethical considerations of artificial intelligence enhance the effectiveness of cyber forensic accounting}},
  author       = {Pham Quang Huy & Vu Kien Phuc},
  journal      = {Journal of Global Information Technology Management},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1080/1097198x.2025.2480972},
}

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Evidence weight

0.41

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

F · citation impact0.25 × 0.4 = 0.10
M · momentum0.55 × 0.15 = 0.08
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.