A data-driven hybrid approach towards developing a circular economy diffusion model for the building construction industry

Although there is a growing body of knowledge about circular economy (CE) practices in the building construction industry (BCI), the prediction of CE diffusion within the BCI remains insufficiently explored. This paper aims to explore a hybrid approach towards predicting the diffusion of CE practices in the BCI of a developing economy. This study utilized the technology-organization-environment (TOE) framework to identify the essential factors influencing CE diffusion in the BCI. Survey data collected from 303 experts were analyzed using partial least squares structural equation modelling (PLS-SEM) to test the hypothesis regarding these influencing factors. Subsequently, machine learning (ML) algorithms were employed to develop a predictive model for CE diffusion in the BCI. SHapley Additive exPlanation (SHAP) was then applied to interpret the contributions of each essential factor to the predictive model. The PLS-SEM results advocated that four major factors, namely technological compatibility, relative technological advantages, top management support, and organizational readiness, significantly and positively influence CE diffusion in the BCI. Furthermore, random forest algorithm was identified as the optimal ML model for predicting CE diffusion, achieving an accuracy of 80.33% and ROC AUC (area under the Curve receiver operating characteristics) of 80.27%. According to the SHAP results, the three most essential features contributing to the random forest model prediction are organizational readiness, top management support, and the relative technological advantages of CE adoption. This study advances the existing literature on CE diffusion by offering a comprehensive, data-driven approach that stakeholders can leverage to forecast trends and patterns in CE practices. It also equips decision makers with strategic insights and pragmatic plans to foster CE diffusion in the BCI, particularly within the context of developing countries.