Contextual Evaluation of Risk Identification Techniques for Construction Projects: Comparative Insights and a Decision-Support Model

Risk identification is a foundational process in construction project management, yet the selection of appropriate identification techniques often lacks empirical guidance. To address this gap, this study adopts a case study design and conducts a comparative evaluation of four established but underutilized methods—Delphi, Nominal Group Technique (NGT), Hazard and Operability Study (HAZOP), and Preliminary Hazard Analysis (PHA)—within the context of a large-scale infrastructure project in Türkiye. The Delphi panel consisted of five senior experts. The NGT session involved six site-level practitioners, and the HAZOP team was composed of four multidisciplinary professionals. Two project-level managers conducted the PHA. Each technique was assessed against seven evaluative criteria: methodological structure, stakeholder engagement, analytical depth, resource intensity, flexibility, decision-support value, and contextual fit. The findings reveal that HAZOP achieved the highest analytical depth and decision-support capacity, while NGT demonstrated the strongest stakeholder engagement and contextual adaptability. Delphi provided robust systemic insights but required substantial time and expert availability, whereas PHA offered rapid screening capacity with limited depth. Drawing on these findings, the study proposes a Contextual Decision Support Model that helps practitioners select the most suitable technique based on project complexity, available resources, and stakeholder conditions. This practical framework enables construction professionals to balance methodological rigor with contextual feasibility, ensuring that risk identification processes are both systematic and adaptable to real-world constraints. Beyond its methodological contribution, the study advances risk management in construction by providing a structured and transparent decision-support approach that bridges academic rigor with on-site practice. By aligning method selection with project-specific attributes and stakeholder dynamics, the model strengthens the integration of analytical precision and practical decision-making across the project lifecycle, thereby contributing to more proactive, evidence-based, and resilient risk management in construction projects.