Identifying and Measuring Project Complexity. (2016)

• Record Type: Journal Article
• Authors: Dao, Bac; Kermanshachi, Sharareh; Shane, Jennifer; Anderson, Stuart; Hare, Eric
• Is Part Of: Procedia engineering. Volume 145(2016)
• Page Start: 476
• Page End: 482
• Publication Date: 2016
• Subjects: Project Complexity -- Complexity Attribute -- Complexity Indicator -- Project Management; Engineering -- Congresses; Engineering -- Periodicals; Engineering; Conference proceedings; Periodicals; 620.005
• DOI: 10.1016/j.proeng.2016.04.024
• Languages: English
• ISSNs: 1877-7058

Abstract

Abstract: This study provides a constructive approach to identify and assess project complexity as a separate factor influencing projects. Project complexity was described in terms of managing projects rather than project physical features to ensure the research results can be generalized across different industries. The complexity attributes and indicators deemed to measure those associated attributes were developed. The data collected through a survey was analyzed using statistical methods to test the significance of complexity indicators in differentiating low complexity projects from high complexity projects. The data analysis resulted in 37 complexity indicators associated with 23 attributes statistically significant to project complexity. The research findings help scholars and practitioners in the project management field in developing an appropriate strategy to manage project complexity effectively.