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Artificial Intelligence (AI) is transforming public sector services by improving efficiency, accessibility, and decision-making. However, security and privacy remain key challenges to user adoption. Despite the global rise in AI-enabled public services, empirical research on public perceptions remains limited, particularly regarding the relationship between security, trust, and adoption intention. Additionally, research on the factors shaping security perception in AI-enabled public services remains scarce, leaving key antecedents unexplored. To bridge this gap, this study offers empirical insights into the determinants of security, trust, and adoption intention in AI-enabled public services. Informed by the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2), a theoretical model is developed and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) on survey data from 506 respondents in Saudi Arabia. The findings show that information security awareness, cybersecurity law, and security culture significantly influence security. Furthermore, security, privacy, recommendation quality, and anthropomorphism positively affect trust. Among adoption factors, trust emerges as the second most influential after performance expectancy. The results contribute to AI adoption research by highlighting the role of security-related factors and trust in shaping citizen adoption of AI-enabled public services, offering practical implications for policymakers to enhance public confidence in AI-driven services.
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Stored only when both papers are in the corpus — 5 citations exist upstream.
Papers that cite the same references — the relational fallback while embeddings are still backfilling.
Imported from OpenAlex (CC0) — nothing here is generated.