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<p>This study examines the determinants of artificial intelligence (AI) adoption in public administration, focusing on China’s unique sociopolitical context. Despite extensive research on AI, gaps remain in our understanding of how hierarchical structures, regulatory environments, and public trust influence AI integration in emerging economies. To address this, the Technology-Organization-Environment (TOE) and Diffusion of Innovation (DOI) frameworks have been extended with governance-specific factors. Survey data from 369 public administration officials were analyzed using partial least squares structural equation modeling (PLS-SEM). The results indicate that top management support, the regulatory environment, technological readiness, organizational capabilities, ethical considerations, and public trust influence AI adoption. Public trust also mediates the relationship between these factors and AI adoption, underscoring the importance of transparency and ethical governance. This study refines existing frameworks and offers actionable insights for policymakers and public administrators, highlighting the need for leadership development, regulatory reforms, infrastructure investment, and ethical AI practices. While focusing on China, these findings may have broader implications in similar contexts.</p>

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Bridging Governance and Technology: Key Determinants of AI Adoption in Public Administration

  • 期刊论文

  • 发表日期：

    2025年7月31日

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  • Bora Ly

摘要

This study examines the determinants of artificial intelligence (AI) adoption in public administration, focusing on China’s unique sociopolitical context. Despite extensive research on AI, gaps remain in our understanding of how hierarchical structures, regulatory environments, and public trust influence AI integration in emerging economies. To address this, the Technology-Organization-Environment (TOE) and Diffusion of Innovation (DOI) frameworks have been extended with governance-specific factors. Survey data from 369 public administration officials were analyzed using partial least squares structural equation modeling (PLS-SEM). The results indicate that top management support, the regulatory environment, technological readiness, organizational capabilities, ethical considerations, and public trust influence AI adoption. Public trust also mediates the relationship between these factors and AI adoption, underscoring the importance of transparency and ethical governance. This study refines existing frameworks and offers actionable insights for policymakers and public administrators, highlighting the need for leadership development, regulatory reforms, infrastructure investment, and ethical AI practices. While focusing on China, these findings may have broader implications in similar contexts.