Understanding the Continuance Intention of College Students toward New E-Learning Spaces Based on an Integrated Model of the TAM and TTF

Abstract

The emergence of educational video platforms has led to microlearning resources becoming increasingly mainstream. These platforms offer unique ecosystems and resource designs that better cater to the needs of learners. In this study, we examined the technology acceptance model (TAM) and task-technology fit (TTF) theory and conducted an empirical analysis of user satisfaction with new online learning spaces. We learned that perceived usefulness, perceived ease of use, and task-technology fit had significantly impacted user satisfaction, with these three factors collectively contributing to 78.2% of the variance in user satisfaction. Additionally, user satisfaction and task-technology fit significantly influenced the continuance intentions of users toward using these spaces, with both factors contributing to 66.7% of the variance in continuance intention. Overall, our findings revealed that the future development of new online learning spaces should consider the task requirements of learners and improve the platforms accordingly.

Keywords:

• Continuance intention
• technology acceptance model
• task-technology fit theory
• e-learning
• microlearning

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by Major Program of National Fund of Philosophy and Social Science of China (19ZDA364), the planning fund for humanities and social sciences projects of the Ministry of Education (Grant number: 22YJAZH001) and the 2022 Guangdong Education Science Planning Project (Special Project of Higher Education, Grant number: 2022GXJK369).

Notes on contributors

Chengliang Wang

Chengliang Wang is a postgraduate in the Department of Education Information Technology, Faculty of Education, East China Normal University. His research interests include social media learning, educational technology philosophy, and artificial intelligence in education.

Jian Dai

Jian Dai is a PhD student in the School of Management, at Zhejiang University of Technology. His research interests include computer-based education and quantitative analysis.

Keke Zhu

Keke Zhu is an undergraduate in the College of Foreign Languages, at Zhejiang University of Technology. Her research interests include language learning and technology.

Teng Yu

Teng Yu is a lecturer at GBA Digital Intelligence Business Research Center and a doctoral candidate at Universiti Sains Malaysia. His research focuses on technology and innovation management, and he is currently an exchange student at The Chinese University of Hong Kong.

Xiaoqing Gu

Xiaoqing Gu is a full professor in the Department of Education Information Technology, Faculty of Education, East China Normal University. Her main research interests include learning design, CSCL, and learning analytics.