Suchergebnisse

The success of Public-Private Partnerships (PPP) lies in the exploitation and expertise of the participants in managing the resources and opportunities available. For the Jordanian government, meeting the growing and continuous demand for goods/services on its own in light of the increasing budgetary constraints and lack of expertise is very difficult, thus the dependency on other sectors of society is increasing. Various researchers associated the enhancement of success rates for projects by classifying the Critical Success Factors (CSFs) of Public-Private Partnership projects all around the world. Critical Success Factors identify the specific weaknesses associated to project implementation and classify the primary factors that ensure project success. This research will draw on previous literature to understand the Critical Success Factors (CSF's), the risk factors influencing PPP construction projects in Jordan, and questionnaire surveys to establish a practical framework for the implementation of risk evaluations. In this paper the Statistical Package for Social Science (SPSS) and the Analytical Hierarchy Process (AHP) were used as risk assessment models to improve the assessment accuracy. The results were gathered from questionnaires administered to government and private organization employees involved in the implementation of PPP projects. It was determined that the most important Critical Success Factor for any local PPP project is proper risk distribution and risk contribution, which was found to have a relative importance of 79.6%. The results showed that the risk factors with the highest ranks (respectively) are transfer phase (48.28%); organizational risks (28.3%), financing phase (12.26%), project management risks (8.29%), and finally, the feasibility study phase (3.64%). The research is ultimately aimed at developing a framework for the risk evaluation of PPPs within the construction industry in Jordan.

Build-Operate-Transfer (BOT) contracts have been widely implemented in developing countries facing budget constraints. Analysing the expected variability in project viability requires extensive risk analysis. An objective analysis of various risk variables and their influence on a BOT project evaluation requires study and integration of many sce­narios into the concession terms, which is complicated and time-consuming. If the process of negotiating the financial parameters and uncertainties of a BOT project could be automated, this would be a milestone in objective decision-mak­ing from various stakeholders' points of view. A soft computing model would let the user incorporate as many scenarios as could be provided. Extensive risk analysis could then be easily performed, leading to more accurate and dependable results. In this research, an artificial neural network model with correlation coefficient of 0.9064 has been used to model the relationship between important project parameters and risk variables. This information was extracted from sensitiv­ity analysis and Monte Carlo simulation results obtained from conventional spreadsheet data. The resulting consensus would yield to fair contractual agreements for both the government and the concession company. First published online:01 Jul 2016