TY - JOUR
T1 - Understanding the Continuance Intention of College Students toward New E-Learning Spaces Based on an Integrated Model of the TAM and TTF
AU - Wang, Chengliang
AU - Dai, Jian
AU - Zhu, Keke
AU - Yu, Teng
AU - Gu, Xiaoqing
N1 - Publisher Copyright:
© 2023 Taylor & Francis Group, LLC.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - Continuance intention
KW - e-learning
KW - microlearning
KW - task-technology fit theory
KW - technology acceptance model
UR - https://www.scopus.com/pages/publications/85179670429
U2 - 10.1080/10447318.2023.2291609
DO - 10.1080/10447318.2023.2291609
M3 - 文章
AN - SCOPUS:85179670429
SN - 1044-7318
VL - 40
SP - 8419
EP - 8432
JO - International Journal of Human-Computer Interaction
JF - International Journal of Human-Computer Interaction
IS - 24
ER -