García de Blanes Sebastián, MaríaSarmiento Guede, José RamónAzuara Grande, AlbertoFilipe, Antonio Ferrao2024-10-072024-10-072024-08-13de Blanes Sebastián, M., Sarmiento Guede, J.R., Azuara Grande, A. et al. UTAUT-2 predictors and satisfaction: implications for mobile-learning adoption among university students. Educ Inf Technol (2024). https://doi.org/10.1007/s10639-024-12927-11360-2357 (print)1573-7608 (online)https://hdl.handle.net/10115/40010During Covid-19 pandemic, Mobile learning (M-learning) was implemented in many universities to continue teaching. Consequently, its use has increased drastically, facing new challenges and benefits. The main aim of this research is to adopt the Unified Theory of Acceptance and Use of Extended Technology (UTAUT-2) to determine the intention of using M-learning among Spanish university students after the pandemic. Model was extended with additional variables, such as ubiquity, satisfaction, system quality, information quality and design quality. Data was obtained from a survey with 378 response units. For data analysis and the hypothesis testing of this study, Structural Equation Modelling (SEM) were used. Results show that social influence, hedonic motivation, price-value, habit, and satisfaction are significantly influencing intention to use M-learning. On the contrary, performance expectation, effort expectation, facilitating conditions, and ubiquity do not influence the intention to use M-learning. The strongest predictor was habit. It is confirmed that system quality, information quality, and design quality are factors that contribute to the satisfaction of using M-learning. The results establish a model predictive power of 76.5%. This study provides a background to the analysis for the intention and usage of M-learning in university students. Universities, as well as developers of educational platforms, can benefit from the results of this study implementing gamification tools, improving the quality of the platform’s interface design, providing quality and updated teaching materials, and, finally, developing updated and accessible platforms.engThis version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/s10639-024-12927-1Mobile-learningUTAUT-2Structural Equation Modelling (SEM)University studentsinfo:eu-repo/semantics/article10.1007/s10639-024-12927-1info:eu-repo/semantics/embargoedAccess