Socio-technical determinants of explainable artificial intelligence for infrastructure decision support

The growing use of artificial intelligence (AI) in infrastructure management has improved data-driven operational decision-making; however, concerns regarding algorithmic opacity and limited interpretability continue to restrict trust in AI-supported systems. Existing studies have largely focused on predictive performance and general technology adoption while providing limited attention to the socio-technical conditions required for trustworthy explainable artificial intelligence (XAI)-enabled infrastructure decision support. Accordingly, this study examines the socio-technical determinants influencing trust in explainable artificial intelligence and perceived infrastructure decision quality. A quantitative research design was employed using a structured questionnaire survey administered to 283 infrastructure professionals. The proposed framework was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings reveal that algorithmic transparency, perceived explainability, decision accountability, and trust in explainable artificial intelligence significantly improve perceived infrastructure decision quality. Furthermore, transparency, explainability, and accountability significantly strengthen trust in explainable artificial intelligence, whereas organizational digital readiness does not significantly influence trust. The study contributes to explainable artificial intelligence and infrastructure management literature by demonstrating that perceived effectiveness of AI-supported infrastructure decision-making depends on the alignment between technological explainability mechanisms and organizational governance structures, as evaluated by infrastructure professionals.