The 2019 Novel Coronavirus Pandemic has severely challenged the continuity of post-secondary education around the world. Online learning platforms have been put to the test, in a context where student engagement will not occur as a simple matter of course. To identify the factors supporting online learning under pandemic conditions, a questionnaire based on the Unified Theory of Acceptance and Use of Technology was adapted and administered to a sample of 704 Chinese university students. Structural equation modelling was applied to the resulting data, to identify the most relevant theoretical components. Effort expectancy, social influence, and information quality all significantly predicted both students’ performance expectancies and the overall adoption of their university’s Moodle-based system. Performance expectancy mediated the effects of effort expectancy, social influence, and information quality on symbolic adoption. Internet speed and reliability had no clear impact on adoption, and neither did gender. The direct impact of information quality on symbolic adoption represents a particularly robust and relatively novel result; one that is not usually examined by comparable research. As outlined, this is one of three key factors that have predicted online learning engagement, and the viability of educational continuity, during the Coronavirus pandemic.
|Number of pages||11|
|Journal||Australasian Journal of Disaster and Trauma Studies|
|Issue number||Special Issue|
|Publication status||Published - 2022|
Bibliographical noteFunding Information:
This research was funded in part by 2021 Guangdong Quality and Reform of University Teaching and Learning, and by BNU-HKBU United International College Research Grants No. R72021103, R201919, and UICR0400022-21. The funding bodies had no role in the design of the current study, nor in the study and collection, analysis and interpretation of data, nor in the writing of the current manuscript. The authors would like to thank Zhen Li, Jiayu Liu, Yuxuan Jia, Zhuoying Rong and Lindai Xie for helping pilot and improve data collection.
© The Author(s) 2022
All Science Journal Classification (ASJC) codes
- Applied Psychology