Digital Risk Management and Cybercrime Prevention in Emerging Market Telecommunications: A Multi-Model Quantitative Analysis

Abstract Digital risk management (IRM) is a foundational pillar of cybersecurity governance frameworks, yet its empirical relationship to cybercrime-prevention outcomes in telecommunications in developing economies remains underspecified. This study examines the predictive value of digital risk management for cybercrime prevention among mobile network operators (MNOs), using a quantitative cross-sectional survey design with 278 MNO employees (response rate: 88.0%). Three hierarchical regression models are estimated: Model 1 tests IRM as a sole predictor (R² = .365, β = .344, p < .001); Model 2 introduces four peer digital leadership predictors AI strategy, digital literacy, forensic investigation, and board governance—demonstrating that IRM retains a significant independent contribution (β = .151, p = .007) within a competitive multi-predictor environment; and Model 3 adds the regulatory environment as a contextual control, which attenuates IRM's direct coefficient to borderline significance (β = .108, p = .062) while a companion moderation analysis confirms regulatory environment strongly activates IRM (moderated β = .402, p < .001, ΔR² = .320). Sub-scale decomposition reveals that risk identification (β = .154, p = .008) and risk assessment (β = .152, p = .013) drive IRM's security contribution, while risk treatment does not independently predict cybercrime prevention when the other dimensions are controlled. These findings advance the digital risk management literature by demonstrating that IRM's security value is dimension-specific, context-conditional, and contingent on the regulatory environment through which it is activated. Practical implications for risk management strategy, regulatory design, and digital leadership in MNOs are discussed.