The rapid emergence of metaverse technologies presents transformative potential for digital integration in construction supply chains (CSCs). However, in emerging economies, the adoption of such technologies remains limited due to a range of structural and contextual barriers. This study examines the critical challenges hindering the integration of the metaverse within CSCs and justifies the need to address them to facilitate sustainable digital transformation.
A hybrid three-phase methodology was employed. Eighteen key challenges were identified through an extensive literature review and categorized using the technology–organization–environment (TOE) framework. Interpretive structural modeling (ISM–MICMAC) was applied to map the interrelationships and driving-dependence power of these challenges. The structural outcomes were further validated and prioritized through machine learning (ML)-based predictive modeling, employing logistic regression (LR), random forest (RF) and stacking ensemble (SE) classifiers.
Cloud computing and data security (C1), traditional technologies (C6) and artificial intelligence readiness (C17) emerged as the most critical challenges. Among the ML models evaluated, the SE achieved the best performance, with an accuracy of 95.6% and the highest area under the ROC curve (AUC = 0.976), confirming the robustness of the ISM–MICMAC structure and underscoring the significance of technological and organizational integration.
This study extends the TOE framework by integrating the ISM–MICMAC model with ML-based predictive validation to assess metaverse adoption readiness in CSCs, a previously underexplored domain. The findings contribute theoretically by presenting a validated structural model and offering practical insights for policymakers, construction leaders and digital solution providers in emerging economies.
