ECOSYSTEM AND HUMAN-CENTRIC HRM IN KAZAKHSTAN’S CIVIL SERVICE

Human-centric and ecosystem-based approaches to human resource management (HRM) have gained increasing attention in public administration reform, particularly in transitional states. However, large-scale empirical evidence remains limited, especially in Central Asia. This study investigates the individual and institutional determinants of perceived HRM quality in Kazakhstan’s civil service and examines why the adoption of artificial intelligence (AI) remains constrained. Using a nationally representative survey of 12,562 civil servants conducted in June 2025, the analysis employs ordinary least squares regression with heteroskedasticity-robust standard errors and binary logistic regression. The findings show that perceived HRM quality is driven primarily by access to education and training, public service motivation (patriotism), and participation in innovative projects, with the model explaining 39.8% of the variance. In contrast, AI adoption is influenced more by organisational position and institutional access than by individual motivation. Managers are substantially more likely to use AI tools, whereas women and longer-tenured civil servants exhibit lower adoption rates. Notably, 44% of respondents report wanting to use AI but lacking access, pointing to a significant implementation gap. The study makes three principal contributions. First, it provides the first large-scale empirical assessment of the ecosystem-based and human-centric HRM framework in a post-Soviet context. Second, it demonstrates that the AI adoption gap is primarily structural rather than motivational. Third, it translates these empirical findings into actionable policy recommendations. The results suggest that narrowing the AI aspiration gap and creating more transparent and accessible career pathways should constitute key priorities for civil service reform, requiring coordinated system-level interventions rather than isolated organisational initiatives.