Modeling Project Manager Competency: An Integrated Mathematical Approach: Fid-Bau Portal

Modeling Project Manager Competency: An Integrated Mathematical Approach

Hanna, Awad S. / Ibrahim, Michael W. / Lotfallah, Wafik / Iskandar, Karim A. / Russell, Jeffrey S.

Although the construction industry is a major component of the U.S. economy, it has been suffering from declining productivity for decades. The human element, project managers (PMs) in particular, are key in solving these persisting problems. Measurement of a PM’s overall performance is important to identify training needs and enables executives to better match competent PMs with the appropriate projects. This paper provides the construction industry with a generic mathematical formulation to reliably weigh different PM competencies. The developed data-driven mathematical model reflects the relative importance that industry practitioners place on different PM competencies while distinguishing exceptional PMs from average ones. This developed model is applied to a data set of 124 PM assessments filled by 62 PM supervisors so that each PM supervisor selected and rated an exceptional PM and an average PM. The results presented in the paper suggest that PMs should focus on developing their cognitive side, rather than settling only for possessing adequate knowledge and experience, managerial skills, and leadership capabilities. Also, these quantitative results illustrate that having business and financial acumens, disciplinary understanding of all the phases of construction projects and their interrelationships, continuous monitoring of similar construction projects, and consistent awareness of the available information technologies are among the most distinguishing competencies between exceptional and average PMs. Such results can assist the construction industry in directing its efforts toward accurately identified leverage development areas through pinpointing actual training and educational needs. Additionally, this paper compares the results of the developed data-driven mathematical model to an existing expert evaluation, presenting a key step in revealing and, in turn, reducing experts’ subjectivity.