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Title: The aetiology and progression of construction disputes between client and contractor in the UK
Author: Wang, Peipei
ISNI:       0000 0004 8504 9398
Awarding Body: University of Manchester
Current Institution: University of Manchester
Date of Award: 2019
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Construction disputes are recognised as unpleasant for diverting valuable and limited project resources from the overall aim of project management. A model predicting the occurrence of construction disputes is desirable in this case. Prediction dictates sufficient understanding of the mechanism of dispute formation, including causation, origination, and development. Therefore, this thesis aims to establish a predictive model based on a validated formation mechanism of construction disputes. A literature review was conducted to identify factors that were critical to the formation of construction disputes. An analogy was drawn between construction disputes and clinical diseases in order to understand the interactions among and the roles of these identified factors in the dispute formation process. The mechanism of dispute formation was proposed based on this analogy and later validated via Bradford Hill criteria and transaction cost theory. Based on the validated mechanism, the structure of the predictive model was established. The research then sought to quantify the structure to render it functional for prediction purposes. A questionnaire survey was conducted to collect the necessary data. 1287 respondents from top 20 construction companies in the UK were invited, and 233 completed questionnaires were returned, representing a response rate of 18.1%. Using these collected questionnaires as the input for data analysis, the structure quantification was realised by applying Bayesian network analysis. Thus, a predictive model was produced. The model's prediction accuracy was tested with both complete and incomplete input data. When all the required input information was available, the model showed an accuracy rate of 93.9%. Five scenarios were proposed to simulate the possible conditions of missing input data. The results showed that the predictive model accommodated these five conditions well, with accuracy rates ranging from 87.9% to 93.9%. Sensitivity analyses were conducted to understand the efficiency of project management effort in dispute prevention. The gap between the service required by the project and that acquired by the client and contractor was found to be critical for shaping management strategies. It was suggested that effort should be shifted from improving the client's and contractor's respective services to enhancing the interactive processes between client and contractor when this gap emerged. This predictive model can be used to assist decision making before and during the construction process. It not only predicts the probability of the occurrence of construction disputes but also simulates the results of the management measures about to be taken. If the predicted probability of the occurrence of disputes is rather high, a user may identify possible improvements and then iteratively simulate the results of the improvements in the model until a satisfactory probability is achieved.
Supervisor: Fenn, Francis ; Stewart, Ian Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Epidemiology ; causation ; Bayesian networks ; construction disputes ; prediction