A Data‐Driven Multidimensional Framework for Modeling and Analyzing Construction Performance Determinants and Strategic Pathways in the Amhara Region of Ethiopia

Construction project performance in developing economies is consistently hindered by complex, interdependent challenges, often managed in isolation. This study addresses this systemic gap by developing and validating a novel Integrated Multidimensional Performance Optimization Framework (IMPOF). Applying multivariate regression models to empirical data from 50 projects in Ethiopia’s Amhara Region, the research quantifies synergies and trade‐offs among six core performance dimensions: cost, schedule, quality, safety, environmental sustainability, and logistics. Diagnostic evaluation confirms that all models meet key OLS assumptions, with no harmful multicollinearity, normally distributed residuals, linear relationships, and constant error variance. The analysis reveals a “Productivity–Quality–Cost Paradox,” where intense pressure for labor productivity ( β  = −3.068 for Quality; −5.402 for Cost) leads to corner‐cutting, rework, and waste, challenging foundational industry norms. Crucially, logistics emerges as a strategic linchpin, with metrics like Delivery Route Efficiency ( β  = 28.344 for Schedule) delivering cascading benefits across all performance domains. The findings advocate for firms to institutionalize preventive maintenance, implement GIS‐based logistics systems, and adopt adaptive scheduling. The framework redefines success as achieving “Constrained Optimization” across the entire project ecosystem, advocating for adaptive scheduling and positioning environmental sustainability as a core indicator of operational efficiency. For practitioners and policymakers, this research provides an actionable roadmap: investing in logistics digitization, reforming contracts with integrated incentives, redefining onsite productivity around quality and safety, and formalizing skills development. Ultimately, the study underscores the need for integrated leadership and a collective mindset shift toward holistic project management to enhance efficiency, resilience, and sustainability in developing regions. Future research should integrate machine learning and dynamic simulation to further refine predictive accuracy.