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TRUVACE RECORD VERSION
record: TRV-2026-0185
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-07-13T09:18:05.786311Z
status: published
lens: g_space
sector: labor
headline: Determinants of Artificial Intelligence Adoption in Public Sector Human Resource Management: Empirical Evidence from Kazakhstan
dek: 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 =…
gain_title: Among 12,562 Kazakhstan civil servants, access to modern digital tools and managerial position increased active AI adoption in public-sector HRM.
problem_title: (none)
trace_subject: (none)
gain_reading: Among 12,562 Kazakhstan civil servants, access to modern digital tools and managerial position increased active AI adoption in public-sector HRM.
gain_evidence: Access to modern digital tools positively moderates AI uptake | Managerial position is the strongest predictor of active AI adoption (OR = 1.609)
problem_reading: (none)
problem_evidence: (none)
quick_read: In June 2025 researchers surveyed 12,562 civil servants in Kazakhstan to examine determinants of AI adoption in public-sector HRM. They validated internal and external HR quality indices and estimated OLS, logistic, and path models to link HR quality, perceived effectiveness, and AI readiness.

The findings matter for workforce modernization because they show adoption is concentrated among managers and those with modern digital tools, while longer tenure predicts lower use, suggesting targeted training and tool provision are needed. Uncertainty remains because the AI adoption model explains only a small share of variance, so other organizational and individual barriers are not yet captured.
limitation: Binary logistic model for AI adoption explains very little variance, indicating most determinants of adoption remain unmeasured.
tag: Evidence-backed gain
key_points: Large-scale empirical survey of 12,562 public servants conducted in June 2025 in Kazakhstan | Constructed and validated composite indices of internal and external HR quality factors with Cronbach's α = 0.924 and 0.959 | OLS regression explaining HR effectiveness with R² = 0.446 found internal factors β = 0.463 stronger than external β = 0.227 | Binary logistic regression modelling AI adoption reported McFadden R² = 0.032 with managerial position OR = 1.609 as strongest predictor | Tenure negatively relates to AI use with OR = 0.846 per category
rundown: The study surveyed 12,562 public servants in June 2025 and built composite indices of internal and external HR quality factors, reporting high reliability with Cronbach's α = 0.924 and 0.959 respectively.

Three models were estimated: OLS for HR effectiveness with R² = 0.446, binary logistic for AI adoption with McFadden R² = 0.032, and a path analysis linking HR quality, effectiveness perceptions, and AI readiness.

Results show internal HR factors exert a stronger influence on perceived HR effectiveness than external factors, managerial position is the strongest predictor of active AI adoption, tenure negatively relates to AI use, and access to modern digital tools positively moderates uptake.
sources:
- peer_reviewed | Administratie si Management Public | https://doi.org/10.24818/amp/2026.46-05 | 2026-05-22
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