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Yongshun Xu

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Open access Aug 2026

How Artificial Intelligence Enhances Construction Supply Chain Resilience Through Supply Chain Integration: A Mixed-Methods Study

Construction supply chains (CSCs) are increasingly exposed to material shortages, demand fluctuations, logistics disruptions, and inter-organizational coordination failures. Artificial intelligence (AI) offers new opportunities to improve construction supply chain resilience (CSCR) by strengthening prediction, information processing, and collaborative decision-making. However, the mechanisms through which AI capabilities enhance CSCR remain insufficiently understood. Drawing on organizational information processing theory (OIPT) and dynamic capabilities theory (DCT), this study examines whether AI capabilities affect proactive and reactive CSCR directly or indirectly through three dimensions of supply chain integration (SCI): operational, information, and relational integration. It further compares the relative strengths of these pathways. This research adopts an explanatory sequential mixed-methods design. In the quantitative phase, 353 valid questionnaires from construction professionals in China were analyzed using partial least squares structural equation modeling (PLS-SEM). In the qualitative phase, semi-structured interviews with 15 experts, alongside three real-world cases, were utilized to interpret the quantitative findings and identify contextual boundary conditions. The results demonstrate that AI capabilities have significant positive effects on both proactive CSCR (β = 0.140, p < 0.01) and reactive CSCR (β = 0.116, p < 0.05). Furthermore, AI capabilities significantly promote operational integration (β = 0.299, p < 0.001), information integration (β = 0.361, p < 0.001), and relational integration (β = 0.227, p < 0.001), which in turn enhance both resilience dimensions. Notably, information integration is an important aspect of proactive resilience (β = 0.290, p < 0.001), while operational integration is crucial for reactive resilience (β = 0.274, p < 0.001). The qualitative findings further indicate that environmental uncertainty, technical readiness, and top management support condition the effectiveness of AI-enabled SCI. Theoretically, grounded in OIPT and DCT, this study clarifies the pathways through which AI affects CSCR and the contextual conditions shaping these effects, thereby advancing the analytical framework for AI-driven resilience. Practically, it delivers tiered implementation guidance for construction stakeholders to deploy AI tools for layered integration, thereby specifically enhancing both pre-disruption proactive risk prevention and post-shock reactive recovery capacities.

Qiang Xu, Haitao Chen, Xinyu Yang et al. · Buildings · 0 citations

Review Open access Jul 2026

Artificial Intelligence in Construction Supply Chains: A Scientometric Review and Future Research Agenda

Despite growing interest in artificial intelligence (AI) applications in the construction industry, the literature still lacks a consolidated understanding of how AI functions across the full spectrum of construction supply chain processes. Existing studies are dispersed across different technologies, project stages, and application contexts, making it difficult to identify the intellectual structure of this field, the main areas of AI application, and the barriers that continue to constrain practical implementation. To address this gap, this study conducts a systematic review of AI applications in construction supply chains by combining scientometric analysis with qualitative content synthesis. A total of 212 journal articles retrieved from Scopus were analyzed using VOSviewer-based scientometric analysis and qualitative content synthesis. The scientometric analysis maps annual publication trends, keyword co-occurrence patterns, co-cited sources, influential documents, and collaboration networks. The qualitative synthesis further examines how AI supports construction supply chain management across three broad themes: procurement and production optimization, logistics and material management, and collaborative decision-making for resilience and sustainability. The findings show that AI has been primarily applied to demand forecasting, resource optimization, logistics coordination, contract and document processing, computer vision-based monitoring, and multi-agent decision support. However, its practical diffusion remains constrained by fragmented and low-quality data, limited empirical validation, high implementation costs, algorithmic opacity, cybersecurity risks, and unresolved governance and liability issues. Based on these findings, this study proposes a data-centric and phased research agenda that emphasizes benchmark datasets, human–AI collaboration, lifecycle economic evaluation, explainable AI, and multi-stakeholder governance. The study contributes to the literature by integrating fragmented AI-related research into a structured knowledge map and by clarifying future pathways for developing intelligent, transparent, and resilient construction supply chains.

Qiang Xu, Haitao Chen, Li Xu et al. · Buildings · 0 citations