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Ahadzie, D K, Proverbs, D G and Olomolaiye, P O (2008) Model for Predicting the Performance of Project Managers at the Construction Phase of Mass House Building Projects. Journal of Construction Engineering and Management, 134(08), 618–29.

  • Type: Journal Article
  • Keywords: Construction management; Housing; Professional development; Managers; Regression models; Buildings, residential;
  • ISBN/ISSN: 0733-9364
  • URL: https://doi.org/10.1061/(ASCE)0733-9364(2008)134:8(618)
  • Abstract:
    The need to match project managers’ (PMs) performance measures onto projects of both unique and similar characteristics has long since been acknowledged by researchers. The need for these measures to reflect the various phases of the project life cycle has also been contended in the recent past. Here, a competency-based multidimensional conceptual model is proposed for mass house building projects (MHBPs). The model reflects both performance behaviors and outcome in predicting the PMs’ performances at the conceptual, planning, design, tender, construction, and operational phases of the project life cycle. Adopting a positivist approach, data elicited for the construction phase is analyzed using multiple regression techniques (stepwise selection). Out of a broad range of behavioral metrics identified as the independent variables, the findings suggest the best predictors of PMs’ performances in MHBPs at the construction phase are: job knowledge in site layout techniques for repetitive construction works; dedication in helping works contractors achieve works schedule; job knowledge of appropriate technology transfer for repetitive construction works; effective time management practices on house units; ability to provide effective solution to conflicts, simultaneously maintaining good relationships; ease with which works contractors are able to approach the PM and volunteering to help works contractors solve personal problems. ANOVA, multicollineriality, Durbin–Watson, and residual analysis, confirm the goodness of fit. Validation of the model also reflected reasonably high predictive accuracy suggesting the findings could be generalized. These results indicate that the model can be a reliable tool for predicting the performance of PMs in MHBPs.