Digitalization, Innovation, and Competitiveness: Insights from a Cross-Country Analysis of Labor Productivity Effects

Evgeny Kuzmin (), Viktoriia S. Bondareva, Akbar Shodiyev and Ilyos Ochilov ()
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Evgeny Kuzmin: Institute of Economics of the Ural Branch of the Russian Academy of Sciences
Viktoriia S. Bondareva: Kyrgyz-Russian Slavic University named after B.N. Yeltsin
Akbar Shodiyev: Termez University of Economics and Service
Ilyos Ochilov: Tashkent State University of Economics

A chapter in Artificial Intelligence and Digital Transformation, 2025, pp 307-329 from Springer

Abstract: Abstract The modern economy faces the necessity of continuously reassessing the factors that determine labor productivity growth. This study presents a cross-country analysis of the impact of digitalization, innovation potential, institutional development, and economic structural composition on labor productivity across 17 countries during the period from 2015 to 2024. The primary aim of the research is to identify robust determinants and evaluate the heterogeneity of digital transformation effects depending on the country profile. The analysis incorporates five explanatory variables: digital competitiveness index, artificial intelligence investment as a percentage of GDP, global innovation index, global competitiveness index, and the share of industry in GDP. Labor productivity per hour worked, expressed in USD adjusted for purchasing power parity at constant 2017 prices, serves as the dependent variable. The methodology involves the construction of an ordinary least squares (OLS) regression model, multicollinearity diagnostics using the variance inflation factor (VIF), dimensionality reduction via principal component analysis (PCA), and country stratification based on k-means cluster analysis. Separate regression models were constructed for each cluster group. The results indicate that the most significant predictors of labor productivity across the full sample are global competitiveness (positive effect), innovation index (negative effect), and digital competitiveness (negative effect). Investments in artificial intelligence and digital competitiveness yielded statistically unstable or paradoxical effects, possibly indicating the presence of time lags, institutional barriers, or structural heterogeneity. The cluster-based models reveal differentiated trajectories of digital transformation. In digitally mature countries, labor productivity is primarily driven by global competitiveness (positive) and a declining share of industry (negative). In contrast, for countries with transitional digital structures, digital and global competitiveness positively influence productivity, while innovation has a restraining effect. These findings underscore the heterogeneity of digitalization effects and highlight the necessity of differentiated digital policy approaches. The conclusions drawn from this study may serve as a basis for developing strategies aimed at enhancing economic performance in the context of a digital economy.

Keywords: Digitalization; Labor productivity; Artificial intelligence; Innovation; Competitiveness; Economic structure; Digital effects (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:spr:lnichp:978-3-032-00118-4_17

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DOI: 10.1007/978-3-032-00118-4_17

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