Determinants of Artificial Intelligence Adoption in Public Sector Human Resource Management: Empirical Evidence from Kazakhstan
Determinants of Artificial Intelligence Adoption in Public Sector Human Resource Management: Empirical Evidence from Kazakhstan

Summary/Abstract: As governments worldwide seek to modernise public administration through digital technologies, understanding the drivers and barriers of Artificial Intelligence (AI) adoption in Human Resource Management (HRM) becomes critically important. This paper investigates determinants of AI adoption among civil servants in Kazakhstan using a largescale empirical survey of 12,562 public servants conducted in June 2025. We construct and validate composite indices of internal and external HR quality factors (Cronbach's α = 0.924and 0.959, respectively) and estimate three complementary econometric models: an OLS regression explaining HR effectiveness (R² = 0.446), a binary logistic regression modelling AI adoption (McFadden R² = 0.032), and a path analysis tracing structural relationships between HR quality, effectiveness perceptions, and AI readiness. Internal HR factors exert a stronger influence on perceived HR effectiveness (β = 0.463) than external factors(β = 0.227). Managerial position is the strongest predictor of active AI adoption(OR = 1.609), while tenure negatively relates to AI use (OR = 0.846 per category). Access to modern digital tools positively moderates AI uptake. The paper concludes with policy recommendations for accelerating human-centred AI integration in public-sector HRM.

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