Developing a PLS-SEM Model to Identify Risk Management Strategies in Construction Contracts: A Case Study in Public Sector Construction of Pakistan

Political unpredictability, environmental hazards, technological constraints, economic volatility, and regulatory barriers are a few difficulties facing Pakistan's public sector construction industry. These elements, together with inadequate infrastructure, have made it extremely difficult to guarantee the success of projects. The complex nature of hazards in this industry is frequently overlooked by existing models and tactics, despite the crucial role that efficient risk management plays in reducing these difficulties. Traditional methods do not capture The complexity involved well, increasing the likelihood of project failure. A structural equation modelling technique that enables the estimate of intricate cause-and-effect linkages in route models with latent variables is partial least squares path modelling, also known as partial least squares structural equation modelling (PLS-PM, PLS-SEM). Data from an 82-person questionnaire survey with G Power taking effect size (0.3), alpha error (0.05), and beta error (0.88) were analyzed using PLS-SEM. The participants included customers, contractors, and consultants involved in public sector building projects in Pakistan. The findings indicated that the model's goodness of fit index is 0.405. Since the coefficient of determination test (R2) of the produced model yielded an analysis result of 0.713, indicating a considerable explanation of the link between the causes of risks and their impacts on project success, the developed model was considered to fit. Project management, feasibility study design, and resource material availability are the internal risk categories most affected. Security, economic, and political factors are the primary components of foreign risk. Based on expert and statistical validation testing, the developed risk factor model successfully explained how risk variables affect construction project success.