TY - JOUR
T1 - Identifying and Measuring Project Complexity
AU - Dao, Bac
AU - Kermanshachi, Sharareh
AU - Shane, Jennifer
AU - Anderson, Stuart
AU - Hare, Eric
N1 - Funding Information:
The study described in this paper was supported by the Construction Industry Institute (CII RT 305 Research Project). This paper forms a part of the research project titled “Measuring Project Complexity and Its Impact”, from which other deliverables have been produced with common background and methodology. The authors also acknowledge the contributions of other CII RT 305 Research Team members for providing significant inputs to complete this study.
Publisher Copyright:
© 2016 The Authors.
PY - 2016
Y1 - 2016
N2 - 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.
AB - 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.
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U2 - 10.1016/j.proeng.2016.04.024
DO - 10.1016/j.proeng.2016.04.024
M3 - Conference article
AN - SCOPUS:84999693652
SN - 1877-7058
VL - 145
SP - 476
EP - 482
JO - Procedia Engineering
JF - Procedia Engineering
T2 - International Conference on Sustainable Design, Engineering and Construction, ICSDEC 2016
Y2 - 18 May 2016 through 20 May 2016
ER -