A uses and gratifications approach to examining users’ continuance intention towards smart mobile learning

Abstract

Smart mobile learning (SML), an online learning system built on artificial intelligence technology, signifies a key development trajectory for mobile learning. However, the current literature reveals a research deficit in introducing specific constructs that represent the categorical level of gratification towards SML, and a new gratification for the intelligent dimension of SML has yet to be identified. Utilising the uses and gratifications (U&G) framework, this study identifies five categories of user gratification. These are derived from five theoretical perspectives, including the incentive theory of motivation, learning theory, diffusion of innovation theory, self-determination theory, and flow theory. Hence, this research integrates aspects of technology, content, social, utilitarian, and hedonic gratification to examine their influence on users’ continuance intention towards SML. This study focuses on Liulishuo, an SML app, as a typical research object and incorporates data from 495 valid samples. The analysis via partial least-squares structural equation modelling (PLS-SEM) indicates the hierarchical significance of various gratifications influencing continuance intention. The empirical findings suggest that in the realm of SML, users’ expectations surpass basic intrinsic needs in importance. For the first time, this study introduces the intelligence construct to investigate users’ technology gratification concerning SML, thereby empirically establishing the validity of this construct. This study reveals that technology gratification, embodied in the notion of intelligence, is the most critical determinant of continuance intention towards SML, a relationship that has previously remained unexplored.