An Empirical Investigation of User Acceptance of Personalized Mobile Software for Sustainability Education

Abstract

Education for sustainability refers to educational policies that focus on shaping a sustainable future. Mobile learning software refers to the use of mobile applications through smart devices for promoting online learning. However, the effectiveness of such software in learning for sustainability depends on users’ intention to adopt it. While there is growing interest in using mobile technology for sustainability education, empirical evidence on how personalized mobile software can influence users’ attitudes and behaviors toward sustainable practices is limited. Thus, this study investigates the factors that affect users’ acceptance of mobile learning technology in sustainability education, by integrating environmental awareness and personalization strategies into a modified model based on the unified theory of acceptance and use of technology (UTAUT). A total of 120 users participated in the survey. An empirical analysis of data was conducted using Partial Least Squares Structural Equation Modeling (PLS-SEM) to test the relationship between the variables of the model. The results showed that both environmental awareness and personalization strategies had a significant effect on users’ behavioral intentions. In addition, hedonic motivation and habit exerted an effect on behavioral intention, contrary to effort expectancy and social influence. These findings confirm that a personalized mobile learning application related to environmental issues reveals as a powerful tool for supporting environmental education since such software appears to have high acceptance by the user. The significance of the study lies in its potential to inform the design and development of personalized mobile applications for sustainability education. The study contributes to the creation of more effective mobile applications that promote sustainable behavior and contribute to a more sustainable future by identifying the elements that influence user acceptance.