Development and Application of Performance Index for Comparative Assessment of Public Capital Projects
Publication: Journal of Construction Engineering and Management
Volume 147, Issue 2
Abstract
Studies have estimated that infrastructure investment in the US will fall short by USD over the next decade due to growing urbanization. Public entities should seek to maximize the use of their available funds by increasing efficiency, supporting collaborative environments, and promoting value over initial low cost. This comprehensive study provides public entities with factual evidence of the performance of their projects under various delivery methods and bidding systems, as well as guidance toward practical implementations to increase their projects’ success using a unique data-driven mathematical model, the Project Performance Index (PPI), introduced in this paper. This index is a multivariate performance assessment metric that incorporates schedule, communication, change management, and spending metrics and is validated using confirmatory factor analysis. The results concluded that design–build (DB) projects outperformed each of hybrid single prime (HSP) and multiple prime (MP) projects (by 39% on average), construction manager at risk (CMAR) outperformed each of HSP and MP (by 32% on average), and also HSP outperformed MP (by 7.8% on average). Additionally, the study found that a 40%–45% enhancement in performance can be achieved by employing alternative delivery methods like DB and CMAR.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
Some or all data, models, or code generated or used during the study are available from the corresponding author by request (analysis R code and raw data).
References
Beauducel, A., and P. Y. Herzberg. 2006. “On the performance of maximum likelihood versus means and variance adjusted weighted least squares estimation in CFA.” Struct. Equation Model. 13 (2): 186–203. https://doi.org/10.1207/s15328007sem1302_2.
Bryant, F. B., and P. R. Yarnold. 1995. “Principal-components analysis and confirmatory factor analysis.” In Reading and understanding multivariate statistics, edited by L. G. Grimm and P. R. Yarnold, 99–136. Washington, DC: American Psychological Association.
Carpenter, N., and C. D. Bausman. 2016. “Project delivery method performance for public school construction: Design-bid-build versus CM at risk.” J. Constr. Eng. Manage. 142 (10): 05016009. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001155.
Changali, S., A. Mohammad, and M. V. Nieuwland. 2015. The construction productivity imperative. New York: McKinsey & Company.
CII (Construction Industry Institute). 2006. “Performance metric formulas and definitions.” Accessed June 10, 2018. https://www2.construction-institute.org/nextgen/publications/pas/general/GeneralLargeMetricsDefinition.pdf.
CMAA (Construction Management Institute of America). 2012. “An owner’s guide to project delivery methods.” Accessed September 16, 2018. http://cmaanet.org/files/Owners%20Guide%20to%.
DBIA (Design-Build Institute of America). 2014. “What is design-build?” Accessed August 12, 2018. https://dbia.org/what-is-design-build/.
El Asmar, M., A. Hanna, and W. Loh. 2015. “Evaluating integrated project delivery using the project quarterback rating.” J. Constr. Eng. Manage. 142 (1): 04015046. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001015.
Franz, B., R. Leicht, K. Molenaar, and J. Messner. 2017. “Impact of team integration and group cohesion on project delivery performance.” J. Constr. Eng. Manage. 143 (1): 04016088. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001219.
Griffith, A. F., E. Gibson, M. R. Hamilton, A. L. Tortora, and C. T. Wilson. 1999. “Project success index for capital facility construction projects.” J. Perform. Constr. Facil. 13 (1): 39–45. https://doi.org/10.1061/(ASCE)0887-3828(1999)13:1(39).
Hooper, D., J. Coughlan, and M. R. Mullen. 2008. “Structural equation modeling: Guidelines for determining model fit.” Electron. J. Bus. Res. Methods 6 (1): 58.
Horn, R. A., and C. Johnson. 2005. Matrix analysis. Cambridge, UK: Cambridge University Press.
Hughes, N., M. Wells, C. L. Nutter, and J. G. Zack. 2013. Impact & control of RFIs on construction projects. Chicago: Navigant Construction Forum.
Ibbs, W., Y. Kwak, T. Ng, and A. Odabasi. 2003. “Project delivery systems and project change: Quantitative analysis.” J. Constr. Eng. Manage. 129 (4): 382–387. https://doi.org/10.1061/(ASCE)0733-9364(2003)129:4(382).
Ibrahim, M., A. Hanna, and D. Kievet. 2018. “Comparative analysis between project delivery systems through quantitative assessment of project performance.” In Proc., Construction Research Congress 2018, 670–681. Reston, VA: ASCE.
Kline, R. B. 2011. Principles and practice of structural equation modeling. New York: Guilford.
Konchar, M., and V. Sanvido. 1998. “Comparison of US project delivery systems.” J. Constr. Eng. Manage. 124 (6): 435–444. https://doi.org/10.1061/(ASCE)0733-9364(1998)124:6(435).
Lam, E. W., A. P. Chan, and D. W. Chan. 2008. “Determinants of successful design-build projects.” J. Constr. Eng. Manage. 5 (333): 333–341. https://doi.org/10.1061/(ASCE)0733-9364(2008)134:5(333).
Lam, E. W. M., A. P. C. Chan, and D. W. M. Chan. 2007. “Benchmarking the performance of design-build projects: Development of project success index.” Benchmarking Int. J. 14 (5): 624–638. https://doi.org/10.1108/14635770710819290.
Li, C. 2016. “Confirmatory factor analysis with ordinal data: Comparing robust maximum likelihood and diagonally weighted least squares.” Behav. Res. Methods 48 (3): 936–949. https://doi.org/10.3758/s13428-015-0619-7.
McCluskey, L. 2013. Using construction manager at risk to save time and money. Sioux Falls, SD: Univ. Center Sioux Falls.
Mindrila, D. 2010. “Maximum likelihood (ML) and diagonally weighted least squares (DWLS) estimation procedures: A comparison of estimation bias with ordinal and multivariate non-normal data.” Int. J. Digital Soc. 1 (1): 60–66.
Osei-Kyei, R., and A. P. C. Chan. 2017. “Developing a project success index for public–private partnership projects in developing countries.” J. Infrastruct. Syst. 23 (4): 04017028. https://doi.org/10.1061/(ASCE)IS.1943-555X.0000388.
Pishdad-Bozorgi, P., and J. M. de la Garza. 2012. “Comparative analysis of design-bid-build and design-build from the standpoint of claims.” In Proc., Construction Research Congress 2012, 21–30. Reston, VA: ASCE.
Riley, D. R., B. E. Diller, and D. Kerr. 2005. “Effects of delivery systems on change order size and frequency in mechanical construction.” J. Constr. Eng. Manage. 131 (9): 953–962. https://doi.org/10.1061/(ASCE)0733-9364(2005)131:9(953).
Shrestha, P. P., and J. D. Fernane. 2016. “Performance of design-build and design-bid-build projects for public universities.” J. Constr. Eng. Manage. 143 (3): 04016101. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001241.
Sullivan, J., M. El Asmar, J. Chalhoub, and H. Obeid. 2017. “Two decades of performance comparisons for design–build, construction manager-at-risk, and design–bid–build: Quantitative analysis of the state of knowledge on project cost, schedule, and quality.” J. Constr. Eng. Manage. 143 (6): 04017009. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001282.
Tabachnick, B. G., and L. S. Fidell. 2007. Using multivariate statistics. 5th ed. Boston: Pearson.
Teicholz, P. 2013. “Labor productivity declines in the construction industry: Causes and remedies (another look).” Accessed March 14, 2013. http://www.aecbytes.com/viewpoint/2013/issue_67.html.
Toor, R. S., and S. O. Ogunlana. 2010. “Beyond the ‘iron triangle’: Stakeholder perception of key performance indicators (KPIs) for large-scale public sector development projects.” Int. J. Project Manage. 28 (3): 228–236. https://doi.org/10.1016/j.ijproman.2009.05.005.
UNC (University of North Carolina). 2009. Construction management at risk within the University of North Carolina. Chapel Hill, NC: UNC.
US Bureau of Labor Statistics. 2017. Current employment statistics highlights. Washington, DC: Dept. of Labor.
US Census Bureau. 2017. Value of construction put in place at a glance. Suitland, MD: US Dept. of Commerce.
WEF (World Economic Forum) and Boston Consulting Group. 2016. Shaping the future of construction: A breakthrough in mindset and technology. Geneva: World Economic Forum.
Information & Authors
Information
Published In
Journal of Construction Engineering and Management
Volume 147 • Issue 2 • February 2021
Copyright
© 2020 American Society of Civil Engineers.
History
Received: Dec 28, 2019
Accepted: Sep 16, 2020
Published online: Dec 11, 2020
Published in print: Feb 1, 2021
Discussion open until: May 11, 2021
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.