Learner characteristics and interface characteristics as factors in the study of continued usage intention on learning management systems

Learning management systems are deemed to be of importance given that information communication technologies such as this would be needed for the growth of a knowledge-based economy (Rashida et al., 2018). In line with this, numerous studies involving the continued usage intention of students on learning management systems have been done in the past. However, past frameworks were not able to integrate a series of variables dedicated for the developers of learning management systems. In addition to that, it was discovered through further research for the current study that there are a series of learner characteristics, one of which have not been used in the context of continued usage intention studies on learning management systems, determination. As a solution to this gap in academic studies, the researchers included the interface characteristics found in the study of Cyr (2008), the two learner characteristics found in the study of Liaw and Huang (20 l 1 ), determination from the study of Aparicio et al. (2017), and the technology acceptance model applying the post-adoption context such as what was used in the study of Lee (2010). Further research provided that there may be a connection between interface characteristics towards the learner characteristics, which has not been done in past studies. Lastly, variables from the interface characteristics were integrated to the technology acceptance model, seeing that the introduction of new variables would always be connected towards the mentioned model in past studies of the same context. With this, 30 hypotheses were tested, all of which were regarding the direct connections between variables. In order to test the hypotheses used for the study, a three-phase experiment design was done wherein respondents would be observed how they use the learning management system, then have them answer a survey wherein the questions have been validated in past studies, and lastly, have them answer a series of interview questions. Structural Equation Modeling was the main statistical method employed in the study. Results from the current study showed that among the 30 hypotheses formulated, nine were deemed insignificant. The final framework yielded from the study could be used by developers of learning management systems in order to develop better iterations of designs of learning management systems in the future