S-Space College of Engineering/Engineering Practice School (공과대학/대학원) Dept. of Civil & Environmental Engineering (건설환경공학부) Theses (Master's Degree_건설환경공학부)
Quantification Model of Smart City Development Dynamics Using Structural Equation Modeling
- Clément NICOLAS
- Issue Date
- 서울대학교 대학원
- Smart City; Project Management; Urban Development; Urban Regeneration; Development Enablers; Performance Objectives; Structural Equation Modeling
- 학위논문(석사)--서울대학교 대학원 :공과대학 건설환경공학부,2019. 8. 지석호.
- In recent years, smart city projects have drawn significant attention as initiatives for enhancing urban development and regeneration. Many studies have incorporated technical and non-technical enablers to better control the design, planning, and progress management of smart cities. However, despite considerable efforts and achievements, the direct and indirect effects of smart city enablers on urban performances have not been quantified comprehensively. Thus, due to this lack of in-depth quantification and understanding, urban leaders encounter difficulties in establishing proper strategies and policies for the successful development of smart cities. To address this issue, the present study has used Structural Equation Modeling (SEM) to identify the critical enablers of smart cities and to quantify their dynamic effects (i.e., direct and indirect effects) on the performances of such cities. More specifically, the authors applied SEM to test and estimate the relationships between four enabler clusters (i.e., technological infrastructure, open governance, intelligent community, and innovative economy) and four performance objectives (i.e., efficiency, sustainability, livability, and competitiveness) using the actual data of 50 smart cities. The statistical results demonstrated that non-technical enabler clusters (i.e., open governance, intelligent community, and innovative economy), as well as the technical drivers (i.e., technological infrastructure), have significant impacts on the performances of smart cities with their highly interrelated, synergetic dynamics. The high percentage of variance explained for performance objectives, which varied from about 71% to 91%, was indicative of good explanatory power. Based on those mathematical findings, urban leaders can enhance strategic planning for smart city transitions through proper policy management.