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Al-Zahrani, J (2013) The impact of contractors' attributes on construction project success, Unpublished PhD Thesis, Department of Mechanical, Aerospace & Civil Engineering, University of Manchester.
- Type: Thesis
- Keywords: contractor; project success; evaluation
- URL: https://www.research.manchester.ac.uk/portal/files/54536844/FULL_TEXT.PDF
The construction industry is one of the most significant industrial contributors to the economy in terms of gross product and employment. As a result, the success of a construction project is a fundamental issue to most governments, users and communities. The thesis addresses the role of the contractor in construction project success as one of the main stakeholders in the project.The research aims to study the impact of contractors’ attributes on project success from a post-construction evaluation perspective to identify what goes right and to recognise the most critical success factors (CSFs) of contractors that greatly impact on project success and link those factors to project success objectives. Initially, a literature review on construction project success was conducted to investigate the success criteria and CSFs of contractors in project success. This was followed by a survey to establish construction professionals’ perceptions of the CSFs of contractors that greatly impact on the success of construction projects.One hundred and sixty-four (164) completed surveys were returned, representing a 32% response rate. The data gathered was analysed using quantitative analysis tools (SPSS). Factor analysis reveals nine underlying clusters perceived to greatly impact the success of projects, namely: (i) health, safety and quality; (ii) past performance; (iii) environment; (iv) management and technical aspects; (v) resource; (vi) organisation; (vii) experience; (viii) size/type of previous projects; and (ix) finance.Four logistic regression and artificial neural network models were developed to predict the most important contractor factors associated with project success. The predictive ability of neural network models outperforms that of logistic models by 47.5% for scheduling model; 34.8% for budget model; 46.2% for quality model; and 46.5% for contractors’ impact model. Assuming that project success is repeatable, these findings provide a clear understanding of contractors’ performance and could potentially enhance existing knowledge of construction project success.