Modeling the determinants of circular procurement adoption for sustainable construction: a fuzzy logic-based evaluation approach

Purpose Traditional procurement approaches are no longer sufficient amidst dire environmental challenges caused by the construction sector. Yet, it is unclear from existing studies what factors could encourage alternatives such as circular procurement (CP). Thus, this study aims to investigate the determinants of CP adoption in the global construction industry. Design/methodology/approach The study employs a mixed-method approach involving focus group interviews with circular experts and engaging 194 international construction professionals using a questionnaire survey. Multiple statistical techniques, including exploratory factor analysis and fuzzy synthetic modelling, were employed to analyze the data. Findings The study identified fourteen key benefits that drive the adoption of CP practices in construction, with “Waste management throughout the construction lifecycle,” “Integration of supply chains” and “Opportunities from new business models” as the three most significant. However, a deeper analysis of the interconnections between these drivers revealed four clusters of principal benefit drivers (PBDs). The four principal groups were “lifecycle cost optimization,” “climate impact mitigation,” “circular capability and waste reduction” and “supply chain collaboration.” Through fuzzy synthetic modeling, it was predicted that three out of four PBDs exerted high levels of influence and should be prioritized for developed economies, while all four PBDs had significant levels of influence in developing regions. Practical implications The findings provide a useful reference point for policymakers, practitioners and researchers in formulating strategies to accelerate the adoption and implementation of CP practices in different construction industries. Originality/value The study provides a novel perspective on CP adoption through understanding and promotion of its benefits. This offers a valuable framework for decision-making in two regional contexts.