Configurational pathways to smart city AI adoption: Evidence from localgovernments in Australia, Hong Kong, Saudi Arabia, Spain, and theUnited States☆

Despite increasing policy attention and technological progress, AI adoption in smart city governance and local
governments remains uneven. While previous studies have identified individual drivers of adoption, limited
research has examined how multiple factors interact to enable or constrain implementation. Drawing on the
technology-community-policy framework, this study employs fuzzy-set qualitative comparative analysis to
investigate configurational pathways leading to AI-enabled smart city adoption across eleven local governments
in five countries, Australia, Hong Kong, Saudi Arabia, Spain, and the United States. The findings reveal three
different equifinal configurations leading to high AI adoption. First, the technology-driven pathway shows that
robust smart city infrastructure and data capability can offset limited regulatory preparedness. Second, the
balanced pathway integrates technological readiness, policy awareness, and organisational attention to com
munity considerations to support adoption holistically. Third, the policy-driven pathway demonstrates that
strong institutional mandates can compensate for weaker technical capacity. Across all pathways, perceived
implementation constraints emerge as a core enabling condition, suggesting that recognition of challenges can
stimulate proactive adoption strategies. The findings highlight substitutability between technological and policy
dimensions, offering strategic flexibility for municipalities with differing resource endowments. This study ad
vances configurational thinking in smart city and public sector innovation research and provides actionable
insights for context-sensitive, resource-appropriate AI governance in local governments