Integrating Risk, Stakeholder, and ERP-BPMS Approaches: A Cross-Sectoral Multi-Case Study in Project Management

Abstract

This study investigates how the integration of real-time risk monitoring, stakeholder engagement, and multicriteria decision analysis (MCDA) within an enterprise resource planning and business process management system (ERP-BPMS) enhances project governance and promotes stakeholder well-being across utilities, construction, water technology, and energy services. It addresses the shortcomings of fragmented risk management and sporadic stakeholder consultation. A convergent mixed-method, multiple-case study was conducted over 18 months using ERP-BPMS logs, standardized surveys (812 responses), and semi-structured interviews (8-12 participants per organization). One utility provider employed a quasi-experimental design to compare the pilot and control districts. The MCDA parameters were refined through Delphi workshops to accommodate for sector-specific priorities. Documented managerial overrides provide context-sensitive refinements for algorithmic recommendations. The integrated approach reduced response times by 25-35%, increased preventive actions by 14-23 percentage points, and increased stakeholder satisfaction by 8-12%. Continuous stakeholder dialogue enabled proactive risk mitigation, as evidenced by 29% fewer service callbacks in the water technology sector. Managerial overrides (13-20%) ensured that local knowledge shaped the final decisions. The framework improves resource allocation, minimizes operational disruptions, and supports sustainable and inclusive governance. By operationalizing dynamic capabilities – risk sensing, MCDA-driven seizing, and stakeholder-led reconfiguring – this study advances the project management and stakeholder theories. This demonstrates that sustained digitally enabled engagement yields measurable gains in community welfare and organizational resilience. The cross-sector model offers a scalable blueprint to balance technical efficiency and social responsibility under tight resource constraints.