Performance risk assessment in public–private partnership projects based on adaptive fuzzy cognitive map

Abstract

High complexity exists underlying public–private partnership (PPP) projects due to their huge scale, large investment, and long-term relationships among various participants, leading to difficulty in managing PPP project performance risk. A robust model that integrates the structural equation model (SEM) and fuzzy cognitive map (FCM) is proposed to perceive and assess the performance risk in PPP projects. SEM is used to learn causal relationships among critical factors representing PPP project performance from the data given. Based on the well-verified SEM, an adaptive FCM model consisting of 14 observed variables and 5 latent variables is built. The proposed approach is capable of performing predictive, diagnostic, and hybrid analysis in various scenarios. Results indicate that variables, including project characteristics (A), project participants (B), project input (C), and project progress (D), all display positive correlations with the target performance (T). Particularly, variables C and D are identified to be more sensitive in ensuring the project satisfactory performance than variables A and B. The optimal risk mitigation strategy can be discovered when the project performance is under an unsatisfactory level. It is found that upgrading the variable with a higher priority would be more efficient to improve the target performance than the variable with a lower priority, which is helpful in both generic and specific situations. The novelty of this research lies in the development of an adaptive FCM model that is capable of learning casual relationships from observed data and assessing risk subjected to uncertainty, subjectivity, and interdependence. The developed model can be used to provide insights into a better understanding of risk mitigation strategies through what-if scenario analysis, enabling to enhance the likelihood of success in PPP projects.