مؤسسة الشرق الأوسط للنشر العلمي
عادةً ما يتم الرد في غضون خمس دقائق
This paper examines workforce readiness and barriers to Artificial Intelligence (AI) adoption for construction scheduling in post-disaster reconstruction, focusing on Derna, Libya, where effective scheduling is critical to managing systemic reconstruction risks. A single-case embedded study was conducted on a prime contractor active in Derna, surveying the complete site workforce (n = 40) across seven organizational units. Data were analyzed using descriptive statistics, ANOVA, and Spearman correlation. Results indicate low, uniform AI awareness across all units (Awareness Index M = .38; p = .151). Perceived benefits for operational resilience were moderate-to-high (M = 3.90), led by real-time subcontractor monitoring and proactive delay detection. Perceived barriers were high (M = 3.96), dominated by poor connectivity and infrastructure rather than attitudinal resistance. Significant inter-unit differences existed for perceived benefits (p = .008) and barriers (p = .003). Education positively predicted perceived benefits (R_s = .54), while experience negatively predicted perceived barriers (R_s = -.38). These findings inform targeted technology-integration strategies to mitigate scheduling risks and enhance management efficiency in disaster recovery environments. The study underscores that policy interventions focusing on infrastructure improvement are essential precursors to digital transformation. Furthermore, the proposed framework provides a replicable model for project managers seeking to stabilize construction timelines in volatile, post-disaster recovery zones. PRACTICAL APPLICATIONS: This study provides construction managers and policymakers with a roadmap for identifying readiness gaps before implementing AI-based scheduling tools in volatile environments. By addressing the identified barriers—specifically connectivity and infrastructure limitations- contractors can allocate resources more effectively to prioritize digital foundations. The findings offer a diagnostic framework that enables site managers in post-disaster zones to assess workforce receptivity and infrastructure capacity, ultimately reducing scheduling delays and improving reconstruction efficiency.