Modelling the factors that affect individuals’ utilisation of online learning systems: An empirical study combining the task technology fit model with the theory of planned behaviour

Abstract

Understanding learners' behaviour, perceptions and influence in terms of learner performance is crucial to predict the use of electronic learning systems. By integrating the task-technology fit (TTF) model and the theory of planned behaviour (TPB), this paper investigates the online learning utilisation of Taiwanese students. This paper provides a better understanding of individual, technological and social factors regarding online learning system performance. A total of 870 students who were earlier introduced to e-learning were surveyed after a period of exposure to the system. The results of the research model were analysed using a structural equation modelling approach to verify 10 hypotheses; support was found for eight of them. This paper offers a new perspective on the mechanisms through the TTF and TPB model constructs, which facilitates e-learning learner performance and offers important implications for understanding learner performance in online learning environments. IntroductionWith the rapid expansion of the Internet, increased computer access and convenient electronic services, the electronic media learning environment is undergoing constant change. Advanced technology has redefined how we communicate and learn, especially in an online learning environment. An understanding of the task-technology relationship is important as this relationship has a significant impact on technology usage and its subsequent outcomes, whether these outcomes are in the form of improved group performance

Exploring Podcast Adoption Intention via Perceived Social Norms, Interpersonal Communication, and Theory of Planned Behavior

“…An early study that applied attitude, subjective norms, and perceived behavioral control to predict ICT adoption intention found that all three variables were significant predictors of college students' intention to utilize a shop-bot to purchase books online (Gentry & Calantone, 2002). Additional research reported that these three independent variables significantly contributed to the formation of behavioral intentions for utilizing an online learning system (Yu & Yu, 2010).…”

How does the smartphone usage of college students affect academic performance?

“…Park et al, 2012;Shin, 2009;Wang & Wang, 2010). There is previous literature illuminating the effects of online or virtual learning systems on students' learning outcome as well as engagement and satisfaction (Chen et al, 2010;Chou & Liu, 2005;Davis & Graff, 2005;Yu & Yu, 2010); however, there is very limited literature examining the effect of smartphone use on students' learning outcome or engagement. The mediator between technology self-efficacy and behavioural intention was indicated as perceived ease of use and perceived usefulness of a new technology (Gu et al, 2009;S.…”

“…The technologies in such cases were diverse types of digital devices or online systems including e-books, learning management systems, smartphones, and others (e.g., digital video tools; Davies & Graff, 2005;Lin & Wang, 2012;Shin, Shin, Choo, & Beom, 2011;Yi et al, 2016). Many studies that examined the effects of online or virtual learning systems illuminated their significant impacts on learning outcomes as well as student engagement or satisfaction, particularly in comparison with traditional learning systems (Chen, Lambert, & Guidry, 2010;Chou & Liu, 2005;Davies & Graff, 2005;Yu & Yu, 2010).…”

Motivations and Use Context in Mobile Tourism Shopping: Applying Contingency and Task-Technology Fit Theories

“…The task–technology fit theory directly affects electronic learning system utilization and indirectly affects electronic learning systems (Yu and Yu, ). As an extrinsic motivational factor, the task–technology fit is associated positively not only with perceived ease of use but also with the perceived usefulness of hotel information systems (Kim et al ., ).…”