Predicting Optimum Project by Using Structural Equation Modeling

Developing a tool to predict the best project according to approved criteria is essential for the success of a project. Traditional assessment models often fall short in capturing the interdependencies among multiple variables. This study fills that gap by offering a predictive model-based framework throw create a simplified and practical interactive model for multi-criteria decision-making. The following steps were taken to overcome the above problems: 1) 33 factors were extracted from open sources and interviews with decision makers. 2) 16 factors achieved a relative importance higher than 80%. 3) Only eight factors were chosen from the Structural Equation Modeling (SEM) to determine weights and relationships having t-values for regression weights were smaller than 1.96, and the R-square was more significant than 0.05, and 4) a mathematical model was built based on the most influential factors, regardless of the lowest bid. To verify the validity of the developed model, the case study was applied to three alternatives (public sector projects under implementation). Four main criteria were consistent with the study's hypothesis, while the POLI criteria were excluded as a major determinant because it had a regression weight greater than 1.96. The results showed the third alternative, was not the best choice, however, the second project is considered the best among them with (medium) degree, while the first and third projects obtained a (weak) degree with a result of 60.44%, 48.01%, and 42.84%, respectively, with goodness of PLS modelwith an R-square of 0.943.