MBA Journals Peer-reviewed management research from Consortium e-Learning Network

NOLEGEIN Journal of Corporate & Business Laws

When Technology Maturity Matters: a Moderated Regression Study of Forensic Analytics and Fraud Detection

  1. David Sunday Araoti

Abstract

Purpose: Data‐driven forensic analytics (DDFA) is widely promoted as a key tool for fraud detection, yet organisations report mixed results. This study investigates whether the effectiveness of DDFA depends on the organisation’s technological maturity – defined asthe sophistication of IT infrastructure, data governance, and analytical skills. Design/Methodology/Approach: A two‐wave longitudinal survey was conducted with 210 finance and audit professionals in a large emerging economy; 178 completed both waves (retention 84.8%). Data were analysed using PLS‐SEM (SmartPLS 4.0), including measurement model assessment (CFA, AVE, HTMT), structural path analysis, and moderation testing via the product indicator approach with 5,000 bootstrap resamples. Endogeneity, common method bias, and non‐response bias were rigorously tested. Phase 2 semi‐structured interviews with 15 audit directors provided qualitative validation. Findings: DDFA has a significant positive association with fraud detection effectiveness (FDE) (β = 0.39, p < 0.001). Technological maturity significantly moderates this relationship (interaction β = 0.22, p = 0.001). Simple slope analysis shows that the DDFA‐FDE association is strong and significant at high technological maturity (β = 0.48, p < 0.001) but weak and non‐significant at low maturity (β = 0.19, p = 0.08). The model explains 61% of variance in FDE (R2 = 0.61). Qualitative interviews confirm that immature infrastructures – poor data quality, legacy systems, and lack of analytical skills – severely limit the value of forensic analytics. Practical Implications: Organisations should not invest in advanced forensic analytics without simultaneously upgrading their technological base. A practical maturity assessment tool is provided. Investments in data governance, system integration, and analytical skills are prerequisites, not complements. Originality/Value: This study is the first to empirically demonstrate technological maturity as a boundary condition for analytics effectiveness in fraud detection, resolving conflicting prior findings. It extends the Information Systems Success Model by introducing infrastructure quality as a key moderator.

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