Abstract
PurposeThe purpose of this research is to develop a project complexity index (PCI) model using the best and worst method (BWM) to quantitatively analyze the impact of project complexities on the performance of metro rail projects.Design/methodology/approachThis study employed a two-phase research methodology. The first phase identifies complexities through a literature review and expert discussions and categorizes different types of complexities in metro rail projects. In the second phase, BWM, a robust multi-criteria decision-making (MCDM) technique, was used to prioritize key complexities, and a PCI model was developed. Further, the developed PCI was validated through case studies, and sensitivity analysis was performed to check the accuracy and applicability of the developed PCI model.FindingsThe analysis revealed that location complexity exerted the most substantial influence on project performance, followed by environmental, organizational, technological and contractual complexities. Sensitivity analysis revealed the varying impacts of complexity indices on the overall project complexity.Practical implicationsThe study's findings offer a novel approach for measuring project complexity's impact on metro rail projects. This allows stakeholders to make informed decisions, allocate resources efficiently and plan strategically.Originality/valueThe existing studies on project complexity identification and quantification were limited to megaprojects other than metro rail projects. Efforts to quantitatively study and analyze the impact of project complexity on metro rail projects are left unattended. The developed PCI model and its validation contribute to the field by providing a definite method to measure and manage complexity in metro rail projects.
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