Smart city rankings and startup ecosystems: An empirical analysis of inverse correlation across 77 smart cities

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As cities increasingly adopt smart technologies and seek to foster innovation-driven economies, it is vital to understand how smart city development relates to the strength of local startup ecosystems. This study investigates whether a statistically significant relationship exists between a city’s performance in the smart city ranking and the strength of its startup ecosystem. The study employed available data from the Global Startup Ecosystem Report (by Startup Genome) and the Smart City Index (SCI by the IMD World Competitiveness Center). A balanced panel regression analysis was conducted on a dataset comprising 77 cities across the years 2020, 2021, and 2023 (2022 is excluded as the SCI was not published). The findings reveal that the Random Effects model yielded statistically significant results, indicating a weak (R² = 25.63%) but significant inverse relationship between SCI and startup ecosystem development, which means cities that rank higher on smart city metrics tend to show lower levels of startup ecosystem performance. This counterintuitive result challenges the assumption that technologically advanced cities automatically provide fertile ground for entrepreneurial activity. One possible explanation is that smart cities, dominated by large tech players and rigid governance structures, may present entry barriers for emerging startups. High operational costs, regulatory constraints, and a focus on large-scale infrastructure projects may disincentivize startups from localizing their innovations within these environments. Although the R² suggests that other variables beyond the smart city ranking influence startup development. This study highlights the need for urban policies that actively integrate startup-supportive mechanisms into smart city strategies.

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    • Table 1. FE regression output for the relationship between City_startup (urban startup ecosystem development) and Smart_City_rank (Smart City Index)
    • Table 2. RE Generalized Least Squares (GLS) regression for the relationship between City_startup (urban startup ecosystem development) and Smart_City_rank (Smart City Index)
    • Table 3. Fixed-effects regression output for the relationship between Smart_City_rank (Smart City Index) and City_startup (urban startup ecosystem development)
    • Table 4. RE Generalized Least Squares (GLS) regression for the impact of Smart_City_rank (Smart City Index) on City_startup (urban startup ecosystem development)
    • Table A1. Panel data
    • Conceptualization
      Aleksandra Kuzior, Viktoriia Marhasova, Viera Zozuľakova, Maria Kočnerova, Vitaliia Koibichuk, Lyudmila Ryabushka, Tetiana Vasylieva
    • Data curation
      Aleksandra Kuzior, Vitaliia Koibichuk
    • Formal Analysis
      Aleksandra Kuzior, Viktoriia Marhasova, Vitaliia Koibichuk, Tetiana Vasylieva
    • Funding acquisition
      Aleksandra Kuzior
    • Investigation
      Aleksandra Kuzior, Viktoriia Marhasova, Tetiana Vasylieva
    • Methodology
      Aleksandra Kuzior, Viktoriia Marhasova, Vitaliia Koibichuk, Tetiana Vasylieva
    • Resources
      Aleksandra Kuzior
    • Writing – original draft
      Aleksandra Kuzior, Viktoriia Marhasova, Viera Zozuľakova, Maria Kočnerova, Vitaliia Koibichuk, Lyudmila Ryabushka, Tetiana Vasylieva
    • Writing – review & editing
      Aleksandra Kuzior, Viktoriia Marhasova, Viera Zozuľakova, Maria Kočnerova, Vitaliia Koibichuk, Lyudmila Ryabushka, Tetiana Vasylieva
    • Visualization
      Viera Zozuľakova
    • Software
      Maria Kočnerova, Lyudmila Ryabushka
    • Project administration
      Tetiana Vasylieva
    • Supervision
      Tetiana Vasylieva
    • Validation
      Tetiana Vasylieva