AI Adoption Enablers and Institutional Governance for Sustainable Infrastructure Resilience in Smart Cities

Artificial intelligence is increasingly used to support infrastructure resilience in smart cities. However, its contribution to sustainable urban development depends on technological capabilities, institutional governance, and stakeholder readiness. Existing technology adoption studies rely mainly on linear models and offer limited insight into nonlinear relationships, governance interactions, and stakeholder heterogeneity. This study examines how AI adoption enablers and institutional governance jointly predict perceived Triple Bottom Line infrastructure resilience. The empirical analysis draws on survey responses from 170 infrastructure stakeholders involved in smart city projects. An explainable machine learning framework was used to compare Random Forest, XGBoost, and LightGBM, with LightGBM achieving the strongest performance among the three models (MAE = 0.437). SHAP analysis, partial dependence and individual conditional expectation plots, interaction analysis, and K-means clustering were then applied to interpret the results. Perceived Value and Performance Benefits emerged as the most influential predictor, while Institutional Capacity and Integration and Institutional Trust and Ethics were the leading governance contributors. Four of the five key predictors displayed nonlinear patterns, with resilience gains becoming more pronounced above construct scores of approximately 3.3 to 4.0. Governance interactions were modest and suggested a compensatory rather than an amplifying relationship. Stronger governance was associated with a higher resilience baseline when perceived value was lower. The analysis also identified four stakeholder profiles with significantly different resilience outcomes (p = 0.002). The findings extend technology adoption research by connecting smart governance with environmental, economic, and social resilience outcomes. They also highlight the conditional role of institutional governance and inform differentiated policy strategies for sustainable smart city development and responsible digital transformation.