Disentangling the influence of mobile learning usability and its determinants–PLS-SEM and importance-performance investigation

Today, numerous mobile learning applications are used to enable learning during the working process or on-the-go. However, few insights that are available regarding mobile application usability (MAU) and its determinants in the context of mobile learning. More specifically, there is a critical need to disentangle the determinants of MAU and their overall impact on MAU while also acknowledging the possible motivational consequences. Therefore, we developed a theoretical model of MAU, its determinants, and its consequences. By utilizing a free simulation experiment, we investigated the role of MAU in the domain of mobile learning. We used structural equation modeling to analyze the theoretical model. The results show a significant influence of MAU on mobile learning compatibility, performance expectancy, and self-efficacy. The results also indicate that compatibility acts as a partial mediator of usability on performance expectancy. Finally, we conducted an importance-performance analysis that reveals key usability insights: UI output, the most critical factor, underperforms, highlighting a major improvement area. UI structure and application design also need enhancement. In contrast, UI input and application utility perform well despite lower importance, with UI graphics showing adequate performance despite being least crucial. The present paper contributes to the discussion concerning MAU and its impact on mobile learning, while delivering formative insights of MAU for mobile learning applications.