Learning from project monitoring feedback : a case of optimizing behavior of contractors
By: WONG, Peter Shek Pui
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Contributor(s): CHEUNG, Sai On | WU, Ray Tsuen Hoi.
Material type: 
ArticlePublisher: Oxford : Elsevier, july 2010Subject(s): Gestão de Projetos | Retroalimentação | Avaliação de DesempenhoInternational Journal of Project Management 28, 5, p. 469-481Abstract: Monitoring is an essential part of construction project management and has typically been devised to guard against non-compliance. When contractors are subjected to regular performance feedback, instead of just taking remedial action, they may take the opportunities to enhance their operations in the light of the feedback. Thus, it has been advocated that responding to performance feedback has a learning element. This study takes on this notion and examined the performance change pattern of contractors that have been subjected to regular performance feedback. Longitudinal performance scores were fitted against five well established learning curve models using the Least Square Curve Fitting Analysis (LSCFA). It was found that the 3-parameter hyperbolic model is the best-fit model in terms of effectiveness and stability. The LSCFA results also suggested that the contractors in the sample can be arranged in four clusters: Competent Learners, Incidental Learners, Indifferent Learners and Optimizers. The majority of them fall into the optimizing cluster. This suggests that these contractors may adjust their resources to maintain their performance at an acceptable level which do not deprive their future opportunities. With due caveat to the constraints and limitations of working sample, this finding is thought provoking for project managers in exercising project monitoring
Monitoring is an essential part of construction project management and has typically been devised to guard against non-compliance. When contractors are subjected to regular performance feedback, instead of just taking remedial action, they may take the opportunities to enhance their operations in the light of the feedback. Thus, it has been advocated that responding to performance feedback has a learning element. This study takes on this notion and examined the performance change pattern of contractors that have been subjected to regular performance feedback. Longitudinal performance scores were fitted against five well established learning curve models using the Least Square Curve Fitting Analysis (LSCFA). It was found that the 3-parameter hyperbolic model is the best-fit model in terms of effectiveness and stability. The LSCFA results also suggested that the contractors in the sample can be arranged in four clusters: Competent Learners, Incidental Learners, Indifferent Learners and Optimizers. The majority of them fall into the optimizing cluster. This suggests that these contractors may adjust their resources to maintain their performance at an acceptable level which do not deprive their future opportunities. With due caveat to the constraints and limitations of working sample, this finding is thought provoking for project managers in exercising project monitoring
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