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Title: Intelligent models for predicting levels of client satisfaction
Authors: Soetanto, Robby
Proverbs, David G.
Keywords: Artificial neural network
Client satisfaction
Contractor performance
Performance assessment
Project coalition
Issue Date: 2004
Publisher: © World Scientific Publishing
Citation: SOETANTO, R. and PROVERBS, D.G., 2004. Intelligent models for predicting levels of client satisfaction. Journal of Construction Research, 5 (2), pp. 233-253.
Abstract: Presents the development of artificial neural network models for predicting client satisfaction levels arising from the performance of contractors, based on data from a UK wide questionnaire survey of clients. Important independent variables identified by the models indicate that long-term relationships may encourage higher satisfaction levels. Moreover, the performance of contractors was found to only partly contribute to determining levels of client satisfaction. Attributes of the assessor (i.e. client) were also found to be of importance, confirming that subjectivity is to some extent prevalent in performance assessment. The models demonstrate accurate and consistent predictive performance for ‘unseen’ independent data. It is recommended that the models be used as a platform to develop an expert system aimed at advising project coalition (PC) participants on how to improve performance and enhance satisfaction levels. The use of this tool will ultimately help to create a performance-enhancing environment, leading to harmonious working relationships between PC participants.
Description: This is the electronic version of an article published in Journal of Construction Research, Volume 5, Issue 2, 2005, pp. 233-253, DOI: 10.1142/S1609945104000164 © World Scientific Publishing Company. The Journal is available at: http://www.worldscientific.com/worldscinet/jcr
Version: Accepted for publication
DOI: 10.1142/S1609945104000164
URI: https://dspace.lboro.ac.uk/2134/16604
Publisher Link: http://dx.doi.org/10.1142/S1609945104000164
ISSN: 1793-687X
Appears in Collections:Published Articles (Architecture, Building and Civil Engineering)

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