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Hayden, I (2019) An evaluation of the design and use of applied visual interactive resources for teaching building regulations in higher education built environment programmes. Architectural Engineering and Design Management, 15(03), 159–80.
Javed, S, Ørnes, I R, Myrup, M and Dokka, T H (2019) Design optimization of the borehole system for a plus-Energy kindergarten in Oslo, Norway. Architectural Engineering and Design Management, 15(03), 181–95.
Osei-Kyei, R and Chan, A P C (2019) Model for predicting the success of public–private partnership infrastructure projects in developing countries: a case of Ghana. Architectural Engineering and Design Management, 15(03), 213–32.
- Type: Journal Article
- Keywords: Public–private partnerships; projects success; critical success factors; success criteria; regression analysis; Ghana;
- ISBN/ISSN: 1745-2007
- URL: https://doi.org/10.1080/17452007.2018.1545632
This paper develops a practical tool for predicting public–private partnership (PPP) project success in developing countries using Ghana as example. The predictive model examines the causal relationship between CSFs and success criteria for PPP projects. First, a conceptual model for PPP projects success was proposed. Second, the theoretical model was tested by means of a questionnaire survey with experienced PPP experts. Using the regression analysis technique, a predictive model for PPP project success was developed. The regression model shows three best predictors of PPP project success in Ghana, these include; appropriate risk allocation and sharing, sound economic policy and right project identification. Various statistical tests including ANOVA, tolerance and variance inflation factor (VIF), homoscedasticity and Durbin–Watson tests confirmed the validity and goodness of fit for the model. The substantive model will enable PPP practitioners including designers, public clients and engineers in Ghana and other neighbouring developing countries particularly sub-Saharan Africa to predict the likely success of their PPP projects prior to their implementations.
Tang, B, Han, J, Guo, G, Chen, Y and Zhang, S (2019) Building material prices forecasting based on least square support vector machine and improved particle swarm optimization. Architectural Engineering and Design Management, 15(03), 196–212.