Behavioral determinants towards enhancing construction waste management: A Bayesian Network analysis

•Quantification of relationships between behavior towards Construction Waste Management (CWM) and its respective factors is imperative to propose strategies for improving CWM practices.•Limitations of various statistical analysis techniques for quantification of relationships between causal factors were argued.•A Bayesian Network (BN) analysis was adopted to develop a model that quantifies the causal relationships based on probability distributions.•The survey questionnaire was administered to field workers. Single- and multi-factor strategies were proposed to improve behavior towards CWM.•The behavioral BN-based model can serve as a motivation and a decision support system for the adoption and implementation of CWM practices.

In this study, a Bayesian Network analysis is used to define causal behavioral determinants towards improving practices in construction waste management (CWM). For this purpose, a structured survey questionnaire was developed and administered to field workers at construction projects. The collected data was used to develop a probabilistic relational model with single- and multi-factor analysis to assess conditional probabilities underlying various determinants. The results indicate that behavior is highly influenced by attitude, past experience, and social pressure with 21, 20, and 10% higher chance of improvement by these factors, respectively. Behavior in CWM appears to be more sensitive to changes in personal factors such as attitude than corporate factors such as training. When simultaneously controlling all factors, the behavior is improved with personal factors by 9% more than with corporate factors. Additionally, it was found that the probability of having effective CWM practices on-site reaches 83% when workers have a positive attitude towards waste management, are well experienced in CWM practices, and are influenced by social pressure. Achieving this result also requires independency at work and availability of training sessions. The model raises the awareness of construction stakeholders about factors influencing workers' behavior towards CWM and presents quantified strategies that increase the chances of minimizing the generation of construction waste. It can serve as a motivation and a decision support tool for adopting and implementing sustainable CWM practices.