Computer-assisted content analysis in risk identification of public-private partnership projects

Abstract

Public-private partnership (PPP) is increasingly popular around the world. The success in implementation of PPP is affected by the identification of the wide range of risks which exist in the different stages of PPP life cycle. Content analysis is a qualitative data analysis method, which is adopted in this study, since it can assist in understanding of the meaning of the texts, classifying the texts, and reducing to more relevant and manageable bits of data. In this study, a computer-assisted content analysis system is developed based on the part-of-speech and syntax-tree algorithm to exact and annotate keywords/phrases, and build up coding framework to help users to identify PPP risks more efficiently and conveniently. The computer-assisted content analysis system is simulated by 20 secondary PPP cases. Finally, the results indicated that the computer-assisted content analysis system is having good potential in assisting users to identify PPP risks with great efficiency and convenience.