Risk Identification and Project Performance in Libyan Construction Companies: Empirical Evidence from 240 Professionals

• Abdulnasir Abdulrraziq Mohammed Akhrees

  Faculty of Arts and Architecture, University of Derna, Derna, Libya.

  Author

• Saaed Awad Abrabba Saaed

  Faculty of Engineering Technologies, University of Derna, Al-Qubba, Libya

  Author

• Abdulbasit Abdulrhman Abdulraziq Abdulsamea

  Faculty of Engineering Technologies, University of Derna, Al-Qubba, Libya

  Author

• Fathi Alnagi Akreem Mahmoud

  Faculty of Engineering Technologies, University of Derna, Al-Qubba, Libya

  Author

This study examines whether systematic risk identification is associated with project performance in Libyan construction companies. A quantitative, cross-sectional survey was administered to 240 construction professionals selected through convenience sampling. Risk identification and project performance were measured using structured five-point Likert-scale items. Instrument reliability was acceptable for risk identification (Cronbach's alpha = 0.734) and strong for project performance (alpha = 0.888), while the full 27-item instrument reported alpha = 0.969. Pearson correlation and simple linear regression were used to test the proposed relationship. Risk identification was positively and significantly correlated with project performance (r = 0.352, p < 0.001). The regression model was statistically significant, F(1, 238) = 33.711, p < 0.001, and explained 12.4% of the variance in project performance (R-squared = 0.124). The unstandardized coefficient (B = 0.468) indicates that higher reported risk-identification practice predicts higher reported performance. The findings support institutionalizing early and recurring risk registers, cross-functional stakeholder workshops, lessons-learned reviews, and periodic risk updates linked to project decisions. However, the modest explanatory power shows that risk identification is only one component of a wider performance system. Because the design is cross-sectional, self-reported, and based on non-probability sampling, the results establish association and prediction rather than causality and should be interpreted within these methodological limits.

1. Saaed Awad Abrabba Saaed, Faculty of Engineering Technologies, University of Derna, Al-Qubba, Libya

   Risk Identification and Project Performance in Libyan Construction Companies: Empirical Evidence from 240 Professionals

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