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Competency-Based Model for Predicting Construction Project Managers’ Performance
Using behavioral competencies to influence human resource management decisions is gaining popularity in business organizations. This study identifies the core competencies associated with the construction management role and, further, develops a predictive model to inform human resource selection an...
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Published in: | Journal of management in engineering 2005-01, Vol.21 (1), p.2-9 |
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creator | Dainty, Andrew R. J Cheng, Mei-I Moore, David R |
description | Using behavioral competencies to influence human resource management decisions is gaining popularity in business organizations. This study identifies the core competencies associated with the construction management role and, further, develops a predictive model to inform human resource selection and development decisions within large construction organizations. A range of construction managers took part in behavioral event interviews where staff were asked to recount critical management incidents, decisions, and actions from which their key competencies could be identified. By delineating the sample according to their levels of performance measured against a range of role-specific performance criteria, the competencies defining superior management performance could be determined. These were then used to construct a logistic regression model from which a project manager’s performance can be predicated. The validated results reveal that “self-control” and “team leadership” are the most predictive behaviors of effective project management performance within the framework of the model. The paper explores the potential role and application of the framework to underpin human resource management decision making with regards to recruitment, performance management, succession planning, and resource allocation. |
doi_str_mv | 10.1061/(ASCE)0742-597X(2005)21:1(2) |
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source | ASCE Civil Engineering Database; Business Source Ultimate |
subjects | TECHNICAL PAPERS |
title | Competency-Based Model for Predicting Construction Project Managers’ Performance |
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