Artificial Intelligence Readiness and Maintenance Performance among Critical Infrastructure Engineers: The Mediating Role of Trust in Artificial Intelligence

The aim of this study was to fill the gap in the existing literature by investigating the mediating effect of Trust in Artificial Intelligence on the association between Artificial Intelligence Readiness and Maintenance Performance of Critical Infrastructure Engineers in Makkah, Saudi Arabia. This study utilized cross-sectional correlational research design and purposive sampling approach. Data was obtained from engineers working in the critical infrastructure industry, such as energy, transport, water, manufacturing, telecommunication, oil and gas, and public utility companies. Male and female engineers who have attained a minimum of a bachelor’s degree and at least one year of experience in their respective areas participated in this study. The standardized tools used were Artificial Intelligence Readiness Scale (Wang et al., 2026), Trust in Automated Systems Survey (Jian et al., 2000) and the Individual Work Performance Questionnaire (Koopmans et al., 2014) modified items. Ethical standards were adhered to throughout the study in line with APA 7th edition. Of 350 targeted participants, 300 filled the questionnaire. The data was analyzed using IBM SPSS Statistics Version 29 and Hayes PROCESS Macro (Model 4). It was found from the Pearson product-moment correlation that there are significant positive associations between AI Readiness, Trust in AI, and Maintenance Performance. Mediation analysis proved that Trust in AI is a partial mediator for the relationship between AI Readiness and Maintenance Performance. There were no significant gender differences regarding Artificial Intelligence Readiness, Trust in AI, and Maintenance Performance from the independent-samples t-tests.