A model for sustainable mobile education beyond the COVID-19 pandemic

Mobile learning (ML) was widely adopted during the coronavirus disease 2019 (COVID-19) pandemic, but its sustained use post-pandemic is not guaranteed. This study identifies the factors influencing university students’ intention to continue using ML. Using the Unified Theory of Acceptance and Use of Technology (UTAUT-2) model, data from 445 students at King Faisal University were analyzed via structural equation modeling. The results showed that attitude toward ML was significantly influenced by effort expectancy ( β = 0.620, p < 0.001), performance expectancy ( β = 0.521, p < 0.001), and hedonic motivation ( β = 0.313, p < 0.001). For continuous intention, habit was the strongest predictor ( β = 0.445, p < 0.001), followed by hedonic motivation ( β = 0.471, p < 0.001) and attitude ( β = 0.175, p < 0.05). Performance expectancy, effort expectancy, social influence, and facilitating conditions had no significant direct effects on continuance intention. These findings confirm habit as the cornerstone of post-pandemic ML continuance, highlighting a shift from utilitarian factors to automated use and enjoyment. Post-pandemic ML integration must strategically foster habitual use and enhance enjoyment, moving beyond utility-focused approaches. This study provides evidence-based insights for educational leaders and platform developers to guide ML’s sustainable integration.