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Predicting performance of contractors is of interest to both academics and practitioners. The physical execution of a project is critical to the overall success of the development. Having a competent contractor that can deliver is most desirable. In this aspect, a significant number of performance prediction models have been developed. Multiple regression and neural networks are typically used as the analytical tools in these prediction models. This paper reports a study that employs a learning curve approach to perform the prediction task. It is suggested that this approach can accommodate the changes in performance as experience accumulates. Thus a performance pattern is projected in addition to the project final outcome. A two-step approach suggested by Everett and Farghal was adopted for this study. First, the learning curve model that best represents a contractors' performance was explored using the least-square curve fitting analysis. Second, prediction analysis was performed by comparing the actual performance data with their respective prediction results obtained from extrapolation on the selected learning curve. The three-parameter hyperbolic model was found to provide the most reliable prediction on performance in this study. DOI: 10.1061/(ASCE)0733-9364(2007) 133:6(474) CE Database subject headings: Predictions; Performance characteristics; Engineering education; Neural networks; Parameters.

Partnering has been advocated for use in construction as a project-delivery approach to curb dispute and enable a cooperative contracting environment. Successful construction partnering is perceived to derive benefits to both developers and contractors. In view of significant implications to successful project delivery, there has been a surge of research and studies on construction partnering. These studies typically focus on the partnering process as well as the identification of critical success factors. Among the various success factors that underpin partnering success, establishing trust among partners is considered the most important. Moreover, skeptics have been swift to point out that establishing trust in construction is daunting. A better understanding of the role of trust and its manifestation in partnering efforts is therefore of both academic and practical value. In this study, motivating trust in construction partnering is described within the classic framework of 'the prisoner's dilemma,' which suggests the trust cycle can be kick-started if construction partners put cooperation before competition and self-interest. This paper reports a study that is designed to suggest a suitable candidate for the trust initiator. As such, the critical trust factors for two groups of construction partners in Hong Kong--developers/consultants and contractors--are first identified. It is found that 'performance' and 'permeability' of partners are the two most critical trust factors. 'Performance' describes the partner's competence and problem-solving ability as perceived by their counterpart. 'Permeability' reflects the partner's openness in sharing information. The result of a multiple-regression analysis further suggests that the contractor is in a position to initiate trust through competent performance and maintaining effective communication with the client. In this manner, the trust cycle can expand with reciprocal trustworthiness from the client. DOI: 10.1061/(ASCE)0733-9364(2005) 131:10(1045) CE Database subject headings: Partnerships: Construction industry; Contract management; Contractors; Hong Kong.

Purpose – The purpose of this paper is to examine the use of bid information, including both price and non-price factors in predicting the bidder's performance. Design/methodology/approach – The practice of the industry was first reviewed. Data on bid evaluation and performance records of the successful bids were then obtained from the Hong Kong Housing Department, the largest housing provider in Hong Kong. This was followed by the development of a radial basis function (RBF) neural network based performance prediction model. Findings – It is found that public clients are more conscientious and include non-price factors in their bid evaluation equations. With the input variables used the information is available at the time of the bid and the output variable is the project performance score recorded during work in progress achieved by the successful bidder. It was found that past project performance score is the most sensitive input variable in predicting future performance. Research limitations/implications – The paper shows the inadequacy of using price alone for bid award criterion. The need for a systemic performance evaluation is also highlighted, as this information is highly instrumental for subsequent bid evaluations. The caveat for this study is that the prediction model was developed based on data obtained from one single source. Originality/value – The value of the paper is in the use of an RBF neural network as the prediction tool because it can model non-linear function. This capability avoids tedious "trial and error" in deciding the number of hidden layers to be used in the network model. [ABSTRACT FROM AUTHOR]