DEVELOPING ROBUST PROJECT SUCCESS MODELS FOR TRANSPORTATION PROJECTS IN TURKEY: INTEGRATING CSFS AND CONTRACT SIGNING PARAMETERS

Date

2023-9-07

Author

MUTLU, UGUR

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In Turkey, the transportation sector attracts a significant share of public investments, primarily directed towards transportation projects to ensure their success. However, not all projects within this sector achieve success, and a comprehensive analysis of the factors impacting project success in Turkey is lacking. This thesis aims to develop project success models for transportation projects in Turkey, focusing on overall success, budget success, and schedule success. Through data collection and linear regression analysis, the study identifies critical success factors (CSFs) specific to the Turkish transportation context. Real project data from 118 selected projects is utilized to develop and evaluate the accuracy of the models, employing metrics such as adjusted R2 values, Mean Squared Error (MSE), and Mean Absolute Percent Error (MAPE). The study discusses the significance and influence of selected variables on project success and provides insights into the dynamics and complexities of transportation projects in Turkey. By incorporating parameters obtained during the contract signing phase, the research enhances the practical relevance of its findings, bridging the gap between theory and application in transportation project management. Moreover, the study highlights the negative impact of negotiation method as tender procedure on project success, recommending improvements in public procurement practices. These findings have important implications for policy and decision-making, encouraging competitive procurement processes for better project outcomes. Implementing the suggested changes based on the study's findings can lead to improved project success rates, reduced risks, and more effective utilization of public investments in the Turkish transportation sector.

Subject Keywords

Critical success factors, PPP, Infrastructure, Procurement strategies, Regression analysis

URI

https://hdl.handle.net/11511/105543

Collections

Graduate School of Natural and Applied Sciences, Thesis