Early understanding of project conditions is crucial so as to proactively respond to the variable situations of a project. Particularly, international construction projects are affected by more complex and dynamic factors than domestic projects; frequently being exposed to serious external uncertainties such as political, economical, social, and cultural risks, as well as internal risks from within the project itself. This study develops a structural equation model (SEM) to predict the project success of uncertain international construction projects. Through a comparative analysis of SEM with a multiple regression analysis and artificial neural network, SEM shows a more accurate prediction of performance because of its intrinsic ability to consider various risk variables in a systematic and realistic way. In addition, the use of SEM allows for visually depicting the paths of how those complicated variables are interrelated so as to promote the clear understanding of the complex system and its underpinned causes that critically affect the project success.
All Science Journal Classification (ASJC) codes
- Computer Science Applications
- Artificial Intelligence