An Integrated Framework to Quantify the Impact of Competency Factors on Project Performance
Publication: Construction Research Congress 2022
ABSTRACT
The scope of this research is to investigate project competencies as leading indicators of project performance using two robust data-driven weighting mathematical frameworks. The objectives of this research are to (1) create a comprehensive performance score named the Project Performance Assessment (PPA) score by combining eight performance metrics spanning over six performance areas: cost, schedule, productivity, safety, quality, and communication; (2) benchmark successful projects from less-than-successful projects by performing K-means clustering; (3) compute a customized competency category-specific score from various competency factors spanning nine categories: alignment and team integration, planning and design, procurement management, contract and risk management, health, safety and environmental, quality management, labor productivity, execution and commissioning, and change management and project control; (4) quantitatively assess the relative contribution of the different competency categories to the PPA score and provide one unique comprehensive score named the Competency Category Assessment (CCA) Score; and (5) utilize a developed regression model to examine the degree of association between the CCA score and the PPA score. The results of this study can assist the construction industry in accurately identifying areas that require improvements based on an individualized assessment of different competency factors, ultimately ensuring more effective and efficient execution of projects.
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REFERENCES
Anderson, S. D., Patil, S. S., Edward Gibson, G., Jr., and Sullivan, G. R. (2004). “Owner–Contractor Work Structures: Process Approach.” Journal of Construction Engineering and Management, Vol. 130, no. 5, doi:https://doi.org/10.1061/(ASCE)0733-9364(2004)130:5(680).
Assaad, R., El-Adaway, I. H., and Abotaleb, I. S. (2020). “Predicting project performance in the construction industry.” Journal of Construction Engineering and Management, 146(5): doi: https://doi.org/10.1061/(ASCE)CO.1943-7862.0001797.
Fayek, A. R. (2012). Advancing Fuzzy Hybrid Techniques for Competency Modeling of Construction Organizations, Natural Sciences and Engineering Research Council of Canada Discovery Grant Proposal, University of Alberta, Edmonton, AB.
Hanna, A., Russell, J., Ibrahim, M., El-Adaway, I., and Abotaleb, I. (2018). Best practices for preventing out-of-sequence construction activities and minimizing their impacts. CII, Austin, TX.
Hanna, A. S., and Iskandar, K. A. (2017). “Quantifying and modeling the cumulative impact of change orders.” Journal of Construction Engineering and Management, Vol. 143, no. 10, 1–10, doi:https://doi.org/10.1061/(ASCE)CO.1943-7862.0001385.
Isik, Z., Arditi, D., Dikmen, I., and Birgonul, M. T. (2009). “Impact of corporate strengths/weaknesses on project management competencies.” International Journal of Project Management, 27(6), 629–637.
Iskandar, K. A., Hanna, A. S., and Lotfallah, W. (2019). “Modeling the performance of healthcare construction projects.” Engineering, Construction, and Architectural Management, vol. 26, no. 9, 2023–2039., doi:https://doi.org/10.1108/ecam-08-2018-0323.
Labib, Y. N., Lotfallah, W. B., Hanna, A. S., and Boulos, N. W. (2020). “Development and Application of Performance Index for Comparative Assessment of Public Capital Projects.” Journal of Construction Engineering and Management, 147(2), doi:https://doi.org/10.1061/(ASCE)CO.1943-7862.0001992.
Leon, H., Osman, H., Georgy, M., and Elsaid, M. (2018). “System dynamics approach for forecasting performance of construction projects.” Journal of Management in Engineering 34 (1): 04017049. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000575.
Sanvido, V., Grobler, F., Parfitt, K., Guvenis, M., and Coyle, M. (1992). “Critical success factors for construction projects.” Journal of Construction Engineering and Management, 118(1), 94–111.
Spencer, L. M., and Spencer, S. M. (1993). Competence at Work: Models for Superior Performance. New York: John Wiley & Sons, Inc.
Traverso, A., Kazmierski, M., Zhovannik, I., Welch, M., Wee, L., Jaffray, D., Dekker, A., and Hope, A. (2020). Machine learning helps identifying volume-confounding effects in radiomics. Physica Medica, 71, 24–30.
Yaya, S., Uthman, O. A., Bishwajit, G., and Ekholuenetale, M. (2019). Maternal health care service utilization in post-war Liberia: analysis of nationally representative cross-sectional household surveys. BMC Public Health, 19(1), 28.
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Published online: Mar 7, 2022
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