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All Journal Journal of Business Innovation and Accounting Research
Pham Hải Nam
Hanoi economics, business, and management community

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Forensic Accounting Innovation and Perceived Fraud Detection Effectiveness in Vietnamese Public Sector Organizations Pham Hải Nam
Journal of Business Innovation and Accounting Research Vol. 3 No. 4 (2026): Journal of Business Innovation and Accounting Research
Publisher : PT. BATARI EDU CALYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56442/mwx7dw75

Abstract

Purpose: This study examines how forensic accounting techniques, specialized knowledge, investigative audit capability, and fraud risk assessment relate to perceived fraud detection effectiveness in Vietnamese public sector organizations. Design/methodology/approach: A cross-sectional questionnaire survey was conducted between January and March 2024 among forensic accountants, internal and external auditors, and financial officers in Hanoi, Ho Chi Minh City, and Da Nang. Of 350 distributed questionnaires, 285 usable responses were reported (81.4%). Measures used five-point Likert scales. The source analysis employed descriptive statistics, Pearson correlations, multiple regression, group mean comparisons, and structural equation modeling. Findings: The original analysis reports positive associations for all four predictors (standardized coefficients: techniques β = 0.187; knowledge β = 0.412; investigative capability β = 0.298; fraud risk assessment β = 0.267; all reported p < 0.01), with R² = 0.673. Knowledge was the highest-ranked predictor in the source regression, while financial transaction analysis and data analytics received the highest technique ratings. Originality/value: The study adds Vietnam-specific, occupation-sensitive evidence to a literature dominated by other national settings and develops an integrated discussion of competencies, investigative procedures, and risk assessment. Caution: The findings concern self-reported effectiveness rather than objectively verified fraud recoveries, and the published aggregate statistics require reconciliation against the original dataset before submission.
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