Navigating the Complexity of Dispute Management in Construction: A DEMATEL and Bayesian Network Approach

This study delves into the dynamics of dispute management within the construction industry, a topic that has been underexplored due to the complex interplay of factors and potential dispute scenarios. We employ a novel methodology that integrates the Decision Making Trial and Evaluation Laboratory (DEMATEL) approach with Bayesian network analysis, providing a more comprehensive understanding of these complexities. Our research utilizes simulations and empirical data analysis from the study region, shedding light on the relationships among factors contributing to claims in construction projects. We identify significant factors such as confusing client requirements, personnel changes, and client change mismanagement, and assess their likelihood of occurrence. Our approach allows for a detailed probability analysis of these factors, highlighting the importance of indirect relationships between seemingly unrelated factors. The primary contribution of our work is providing construction managers and stakeholders with valuable insights for informed decision-making, understanding dispute triggers, and facilitating proactive prevention and resolution measures.

This research holds significant implications for construction project stakeholders, offering practical insights that extend beyond academic circles. Project managers and practitioners can leverage the findings to enhance claims management strategies and improve project outcomes. The identification of influential factors, such as challenging economic conditions, poor contractor management, and personnel changes, enables proactive risk mitigation. The research methodology, combining Decision Making Trial and Evaluation Laboratory (DEMATEL) and Bayesian approaches, provides a structured framework for understanding the complex interplay of these factors. Practitioners can use the influential relationship map to prioritize and address key factors affecting claim occurrence. The Bayesian network model aids in assessing the probabilities of these factors, guiding resource allocation and risk management efforts. The practical application extends to decision-makers, empowering them to anticipate potential scenarios and make informed choices. By considering both internal and external factors, project teams can tailor their claims management approach to specific project contexts, fostering successful project outcomes.