A model for predicting cost control practice in the Ghanaian construction industry
- Authors: Adjei, Kofi Owusu
- Date: 2020
- Subjects: Construction industry - Ghana , Construction industry - Cost control
- Language: English
- Type: Doctoral (Thesis)
- Identifier: http://hdl.handle.net/10210/437411 , uj:37979
- Description: Abstract: One of the key roles of construction project managers is to execute construction projects within the targeted project cost. In Africa, most construction projects suffer huge cost overruns. Project cost control practice is required by every construction firm to keep the project cost in line with the budgeted cost. A comprehension of the different parts of cost control philosophies is fundamental to empower project cost managers to adequately set up robust cost controls and to improve future strategies for active construction project cost delivery. Although there are efforts by project cost managers to control cost, there is a lack of understanding of the factors that determine cost control practice in Ghana, as a developing nation. The factors enhancing cost control practice and a formal model are needed for consideration by project cost managers to guide their operations. This study develops a model for predicting cost control practice in the Ghanaian construction industry. Mixed-method methodology was utilised for this study. The qualitative survey used the Delphi survey approach to investigate the primary factors and measurement-related factors. The study identifies project cost control as eight-factor constructs: project cost estimation, project cost budgeting, project cost reporting, project cost monitoring, project cost analysis, decision-making, change management and project cost communication. These had strong inter-quartile deviations. .. , D.Phil. (Engineering Management)
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An integrated total quality management model for the Ghanaian construction industry
- Authors: Ansah, Samuel Kwame
- Date: 2018
- Subjects: Construction industry - Ghana , Total quality management - Ghana
- Language: English
- Type: Doctoral (Thesis)
- Identifier: http://hdl.handle.net/10210/413268 , uj:34810
- Description: D.Phil. (Engineering Management) , Abstract: This research project investigated and modelled Total Quality Management (TQM) for the Ghanaian construction industry. The primary aim of the research was to model the extent to which Leadership/Top Management features, Company Supplier Quality Management features, Client Focus and Involvement features, Company Quality System Evaluation features, Company Vision and Plan Statement features, Product Design Management features, Product Selection Management features, Construction Process Management and Improvement features, and Construction Employees’ Involvement and Motivation features predict TQM for the construction industry, these factors being classified as the exogenous variables. Mixed-methods research which involved both Qualitative and Quantitative approaches was adopted for the study. Empirical data was collected through a Delphi study and a field questionnaire survey. Analysis of results from the Delphi study was done with Microsoft Excel to output descriptive statistics. A conceptual integrated TQM for the Ghanaian construction industry model was based on the theory developed from literature review findings and the Delphi study. A questionnaire survey was conducted among the top management working in the construction industry in Ghana. From the 641 sample questionnaires, 536 questionnaires were returned which represents 83.62 per cent. An exploratory factor analysis (EFA) was conducted on the initial eight-factor constructs and their variables to determine their reliability for their inclusion in the confirmatory factor analysis (CFA). Nine-factor constructs were realized after the EFA factor loading test. Further, CFA was conducted on these nine-factor constructs using structural equation modelling (SEM) software with Eqations (EQS) version 6.2 software programme to validate and determine their reliability and inclusion in the final model. Findings from the literature on TQM studies revealed the theory that TQM implementations and practices and the latent variables lead to TQM in the construction industry. Findings from the Delphi study revealed that several factors (Leadership/Top Management features, Company Supplier Quality Management features, Client Focus and Involvement features, Company Quality System Evaluation features, Company Vision and Plan Statement features, Product Selection and Design Management features, Construction Process Management and Improvement features, and Construction Employees’ Involvement and Motivation features) were considered to be the most important determinants of TQM in the Ghanaian construction industry. Both findings revealed that TQM could be considered as an eight-factor model defined by the influence of TQM practices and experts in construction...
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Development of a cost-predicting model for construction projects in Ghana
- Authors: Coffie, George Harrison
- Date: 2018
- Subjects: Construction industry - Costs , Construction industry - Ghana
- Language: English
- Type: Doctoral (Thesis)
- Identifier: http://hdl.handle.net/10210/493210 , uj:45074
- Description: Abstract: One of the foremost challenges faced by the construction industry is the issue of cost overruns. Cost overruns cut across construction projects of nations and continents as well. They vary in magnitude and occur irrespective of project size and location. Over the years numerous attempts have been made in the area of estimating cost of construction projects right and improving the efficacy or accuracy of cost estimating using different statistical methods. This research investigated the factors that contribute to cost overruns and developed a predicting cost-estimating model for public sector building projects. The aim primarily was to extract factors from historical data of completed projects and use these predictive factors to develop a predictive model. Two models were developed using the predictive variables from historical data by the use of multiple linear regression and extreme learning machine. These models were compared to see the accuracy of performance. Results from the study reveal findings that; predictive variables from historical data can be used to predict the cost of completion of construction projects at the contract award stage, the multiple linear regression model results as compared to extreme learning machine results shows that extreme learning machine performs better. The study brought to light the use of extreme learning machine for developing predicting cost-estimating models built on historical data from completed projects. This rarely exists in construction industry. It further substantiates the superior performance of extreme learning machine to multiple linear regressions using big data. The developed model can also be converted to desktop software for predicting completion cost by industry... , Ph.D. (Engineering Management)
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