Factors affecting m-learning continuance – From the perspectives of flow theory and stimulus-organism-response theory

Authors

  • Chen-Cheng Chang National Chin-Yi University of Technology

DOI:

https://doi.org/10.34190/eckm.23.2.710

Abstract

With the continual impact and deterioration of the pandemic disease of COVID-19 around the world, teaching classes in person that was a key part of learning style now was unavailable to be utilised by universities’ lecturers and it seemed that such situation would remain unavailable for some time to come. Fortunately, the good news is that there has been a trend over the past decades that the increasing number of universities has established and moved partly to online courses and most of the students have learned how information and communication technologies (ICTs) can help them study effectively. Previous research on E-learning has proven that the challenges of studying online can be even more daunting for both lecturers and students who have to suddenly change their learning patterns from the classrooms to the virtual ones. This is mainly because the suddenness of this change makes it difficult for lecturers to fully prepare to lecture in the virtual learning environment. In light of the above-mentioned facts, this research proposes a novel model and integrates flow theory into the theory of technology acceptance model (TAM), based on stimulus–organism–response (S-O-R) theory, the SOR model has been widely used in previous studies of online customer behaviour, and the model theory includes three components: stimulus, organism, and response. Assuming that stimuli contained in the external environment cause people to change, which in turn affects their behaviour. This research explores deeply what factors stimulate and affect learners mobile learning (M-learning) continuance (individual responses). Consequently, our research model provides a new lens for M-learning through the S-O-R theory and suggests that the TAM model affects students’ flow experiences and satisfaction, which in turn, influences engagement and M-learning continuance.

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Published

2022-08-25