E-government development: Artificial intelligence vibrancy and readiness as drivers of digital public administration

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Type of the article: Research Article

Abstract
Artificial intelligence is shaping digital governance, with global organizations emphasizing its opportunities and risks for public administration. The study aims to assess whether advancements in AI, measured by the AI Vibrancy Score (AIVS) and the Government AI Readiness Index (GAIRI), drive improvements in the E-Government Development Index (EGDI). Using panel data methods, the analysis draws on data from 36 countries for 2018–2022 (AIVS–EGDI) and 170 countries for 2020–2024 (GAIRI–EGDI), due to differing data availability and indicator coverage periods, applying fixed effects, random effects, and Mundlak specifications, combined with robust inference techniques. The results demonstrate that within-country improvements in AI readiness are positively and robustly associated with higher levels of e-government development, with the FE estimate for the Government AI Readiness Index equal to 0.17 (p < 0.001). RE models reveal stronger cross-country correlations, with coefficients of 2.55 (p < 0.001) for the AI Vibrancy Score and 0.35 (p < 0.001) for AI readiness. However, Mundlak (correlated RE) specifications indicate that the between-country components are statistically insignificant. Yet, the within-country effects remain significant, suggesting that dynamic national reforms and policy-driven progress outweigh inherited structural advantages. Time effects are pronounced, with positive and significant shifts in 2020 (+7.02) and 2022 (+8.10) relative to the baseline year, reflecting the acceleration of digital public administration during the post-pandemic period. Country-specific effects exhibit substantial heterogeneity, ranging from strongly positive deviations (e.g., Denmark, Estonia, Korea) to persistently negative ones (e.g., India, South Africa), underscoring the uneven national trajectories. Robustness checks using clustered standard errors confirm the stability of all key coefficients.

Acknowledgment
This paper was prepared based on the results of a study funded by the Ministry of Education and Science of Ukraine entitled “Digitalization of the public-private partnership system as a driver of the state’s economic security in the war and post-war periods” (registration number: 0126U000543).

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    • Table 1. Descriptive statistics of the dataset
    • Table 2. Results of FE and RE estimations
    • Table 3. RE estimation with cluster-robust standard errors
    • Table 4. Correlated RE (Mundlak specification)
    • Table 5. Country-specific effects (uᵢ) from the Mundlak correlated random effects model
    • Table 6. Mundlak correlated random effects model with time effects
    • Table 7. Country-specific intercepts (αᵢ) from the Mundlak correlated random effects model
    • Table 8. Descriptive statistics of the dataset GARI and EGDI
    • Table 9. FE and RE estimations with cluster-robust standard errors
    • Table 10. Mundlak specifications (FE and RE) with cluster-robust standard errors
    • Table 11. Countries with the highest and lowest country effects (uᵢ)
    • Table 12. Countries with the highest and lowest fixed effects (αᵢ)
    • Table B1. Country-specific effects (uᵢ) from the Mundlak correlated random effects model
    • Table B2. Country fixed effects (αᵢ) from the panel model
    • Conceptualization
      Sergiy Spivakovskyy, Narek M. Kesoyan, Olena Astapova-Vyazmina, Anatolii Melnychuk, Denys Babaiev, Oleksii Zakharkin, Yong Zhou
    • Data curation
      Sergiy Spivakovskyy, Olena Astapova-Vyazmina, Oleksii Zakharkin, Yong Zhou
    • Formal Analysis
      Sergiy Spivakovskyy, Anatolii Melnychuk, Yong Zhou
    • Investigation
      Sergiy Spivakovskyy, Yong Zhou
    • Project administration
      Sergiy Spivakovskyy, Denys Babaiev, Oleksii Zakharkin, Yong Zhou
    • Supervision
      Sergiy Spivakovskyy, Oleksii Zakharkin, Yong Zhou
    • Validation
      Sergiy Spivakovskyy, Narek M. Kesoyan, Yong Zhou
    • Visualization
      Sergiy Spivakovskyy, Yong Zhou
    • Writing – original draft
      Sergiy Spivakovskyy, Narek M. Kesoyan, Olena Astapova-Vyazmina, Anatolii Melnychuk, Denys Babaiev, Oleksii Zakharkin, Yong Zhou
    • Writing – review & editing
      Sergiy Spivakovskyy, Narek M. Kesoyan, Olena Astapova-Vyazmina, Anatolii Melnychuk, Denys Babaiev, Oleksii Zakharkin, Yong Zhou
    • Resources
      Narek M. Kesoyan, Anatolii Melnychuk, Denys Babaiev
    • Software
      Olena Astapova-Vyazmina, Yong Zhou
    • Funding acquisition
      Denys Babaiev
    • Methodology
      Yong Zhou
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