Mapping the Influence of Project Management Attributes on Project Cost

Mapping the Influence of Project Management Attributes on Project Cost

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Author(s): Jonathan Musonda

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DOI: 10.18483/ijSci.2296 66 213 1-15 Volume 9 - Mar 2020


This paper develops a framework to map the influence of project management (PM) attributes on project cost and then test these relationships between PM attributes and project cost on industrial construction projects. PM attributes are identified and classified into five areas: Human Resource Management (HRM), function of PM, partnering and supply chain, design efficiency, and quality. The framework is tested on survey data from construction companies and project management firms in China using the Structural Equation Modeling method. The results reveal that quality, function of PM, and HRM can have a significant positive impact on project cost. Quality is found to have the most direct and greatest impact on project cost efficiency among PM attributes. The goal of this statistical study is to demonstrate the paths and strengths of the effects of PM attributes on project cost.


Project Management Attributes, Project Cost, Influence Framework, Structural Equation Modeling


  1. Alwin, D. F., & Hauser, R. M. (1975). The decomposition of effects in path analysis. American Sociological Review, 40(1), 37-47.
  2. Coffman, D. L., & MacCallum, R. C. (2005). Using parcels to convert path analysis models into latent variable models. Multivariate Behavioral Research, 40(2), 235-259.
  3. Edwards, J. R., & Lambert, L. S. (2007). Methods for integrating moderation and mediation: A igeneral analytical framework using moderated path analysis. Psychological Methods, 12(1), 1-22.
  4. Bryant, F. B., & Yarnold, P. R. (1995). Principal components analysis and exploratory and confirmatory factor analysis. In L. G. Grimm & P. R. Yarnold (Eds.), Reading and understanding multivariate analysis. Washington, DC: American Psychological Association.
  5. Dunteman, G. H. (1989). Principal components analysis. Newbury Park, CA: Sage Publications.
  6. Fabrigar, L. R., Wegener, D. T., MacCallum, R. C., & Strahan, E. J. (1999). Evaluating the use of exploratory factor analysis in psychological research. Psychological Methods,4(3), 272-299.
  7. Gorsuch, R. L. (1983). Factor Analysis. Hillsdale, NJ: Lawrence Erlbaum Associates.
  8. Hair, J. F., Jr., Anderson, R. E., Tatham, R. L., & Black, W. C. (1995). Multivariate data analysis with readings (4th ed.). Upper Saddle River, NJ: Prentice-Hall.
  9. Hatcher, L. (1994). A step-by-step approach to using the SAS system for factor analysis and structural equation modeling. Cary, NC: SAS Institute.
  10. Hutcheson, G., & Sofroniou, N. (1999). The multivariate social scientist: Introductory statistics using generalized linear models. Thousand Oaks, CA: Sage Publications.
  11. Kim, J. -O., & Mueller, C. W. (1978a). Introduction to factor analysis: What it is and how to do it. Newbury Park, CA: Sage Publications.
  12. Kim, J. -O., & Mueller, C. W. (1978b). Factor Analysis: Statistical methods and practical issues. Newbury Park, CA: Sage Publications.
  13. Lawley, D. N., & Maxwell, A. E.(1962). Factor analysis as a statistical method. The Statistician, 12(3), 209-229.
  14. Levine, M. S. (1977). Canonical analysis and factor comparison. Newbury Park, CA:Sage Publications.
  15. Pett, M. A., Lackey, N. R., & Sullivan, J. J. (2003). Making sense of factor analysis: The use of factor analysis for instrument development in health care research. Thousand Oaks, CA: Sage Publications.
  16. Shapiro, S. E., Lasarev, M. R., & McCauley, L. (2002). Factor analysis of Gulf War illness: What does it add to our understanding of possible health effects of deployment, American Journal of Epidemiology, 156, 578-585.
  17. Velicer, W. F., Eaton, C. A., & Fava, J. L. (2000). Construct explication through factor or component analysis: A review and evaluation of alternative procedures for determining the number of factors or components. In R. D. Goffin & E. Helmes (Eds.), Problems and solutions in human assessment: Honoring Douglas Jackson at seventy. Boston, MA: Kluwer.
  18. Widaman, K. F. (1993). Common factor analysis versus principal component analysis: Differential bias in representing model parameters, Multivariate Behavioral Research, 28, 263-311.

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International Journal of Sciences is Open Access Journal.
This article is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) License.
Author(s) retain the copyrights of this article, though, publication rights are with Alkhaer Publications.

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