How Flow Experience and Self-Efficacy Define Students’ Online Learning Intentions: View From Task Technology Fit (Framework)

Abstract

The ongoing pandemic has transformed communication modes globally. Especially in the case of higher education, where countermeasures against coronavirus disease 2019 (COVID-19) have affected students’ learning experience. This study emphasized the case of business simulation games, where critical factors were underlined to define learners’ intention to use an online learning environment through the lens of task technology fit (TTF) as a theoretical stance. This study considered the statistical analysis of 523 students who attended the business simulation module online at the tertiary level of education. Findings conclude that flow experience is the most critical factor to define learners’ perceived TTF in the case of an online learning experience. However, the learners’ self-efficacy is significant enough to map learners’ intentions to use an online environment for learning. The study discussed several theoretical and practical implications for learners’ educators and policymakers.

What is holding back business process virtualization in the post-COVID-19 era? Based on process virtualization theory (PVT)

“…Information overload refers to a situation where people receive more information than they can process and utilize the information, leading to anxiety or a negative sense of failure ( Granero-Gallegos et al, 2021 ). Information overload affects information-intensive industries across society, including government, education, family life, online consumption, and citizenship ( Bawden and Robinson, 2009 ; Huang and Wang, 2022 ). In the digital information age, individuals and organizations are overwhelmed by too much information.…”

“…These hurdles are not conducive for enterprises to business process virtualization. The existing literature has been extensively discussed through various theoretical perspectives to weigh the benefits and challenges of information system-based telecommuting, such as the Technology Acceptance Model ( Silva-C, 2019 ; Donati et al, 2021 ; Chai et al, 2022 ), Innovation Diffusion Theory ( Al-Rahmi et al, 2019 , 2021 ; Tsai and Hung, 2020 ), and Task-Technology Fit Theory ( Al-Rahmi et al, 2022 ; Huang and Wang, 2022 ). These theoretical lenses make significant contributions at the level of individual acceptance of information systems.…”

Assessment of the Contribution of Information Adversarial Technology to Educational Development in the Context of Neural Networks

“…(2) ere is no need to establish practically meaningful relationships between individual items. In the process of model prediction, there may be some connection between the predicted outcome and the predicted purpose, but since both the predicted outcome and the purpose are indicators of the predicted learning status of students, it is not possible to determine the relationship between them diagnostically [18]. erefore, it is necessary to build a prediction model for the neural network horizon and analyze the actual relationship between the prediction outcome, purpose, and the various stages of prediction [19,20].…”

Extending the Task-Technology Fit Model in an E-Collaborative English Learning Context

“…Flow experience played the most important role in fitting TTF in online learning contexts. learners' self-efficacy could also greatly influence learners' intention to use online learning platforms or technologies (Huang & Wang, 2022).…”