The application of Bayesian network analysis in demystifying construction project subcontracting complexities for developing countries

Abstract

Purpose This study aimed to address a gap in subcontractor management by focusing on previously unexplored complexities surrounding subcontractor management in developing countries. While past studies concentrated on selection and relationships, this study delved into how effective subcontractor management impacts project success. Design/methodology/approach This study used the Bayesian Network analysis approach, through a meticulously developed questionnaire survey refined through a piloting stage involving experienced industry professionals. The survey was ultimately distributed among participants based in Accra, Ghana, resulting in a response rate of approximately 63%. Findings The research identified diverse components contributing to subcontractor disruptions, highlighted the necessity of a clear regulatory framework, emphasized the impact of financial and leadership assessments on performance, and underscored the crucial role of main contractors in Integrated Project and Labour Cost Management with Subcontractor Oversight and Coordination. Originality/value Previous studies have not considered the challenges subcontractors face in projects. This investigation bridges this gap from multiple perspectives, using Bayesian network analysis to enhance subcontractor management, thereby contributing to the successful completion of construction projects.