{"title":"Students’ Online Learning Adoption during an Emergency Situation: Integrating the Self-Determination and Perceived Risk Theories","authors":"Sheng‐Ju Chan, Thi Xuan Nong, T. Nguyen","doi":"10.1155/2023/6128584","DOIUrl":null,"url":null,"abstract":"By integrating self-determination theory and perceived risk theory, the current research proposes a new model to predict students’ online learning adoption during an emergency situation such as the COVID-19 pandemic. More specifically, it is aimed at exploring how online communication self-efficacy, online learning belonging, and perceived risk predict students’ online learning adoption. A printed questionnaire was developed to collect data from 487 Vietnamese students using a quota sampling method. After missing data and outliers were removed, 450 questionnaires were found to be usable for data analysis. SMARTPLS version 3.2.2 was employed to analyze PLS-SEM and test the proposed hypotheses. The study found that online communication self-efficacy and perceived risk both have direct effects on students’ online learning adoption as well as indirect effects through the partial mediating role of online learning belonging. Our study also explored that perceived risk does not play a moderation in the association between online learning belonging and students’ online learning adoption. These findings fill important gaps in the literature and provide some implications for academicians, governments, educators, and parents in fostering students’ adoption of online learning.","PeriodicalId":36408,"journal":{"name":"Human Behavior and Emerging Technologies","volume":null,"pages":null},"PeriodicalIF":4.3000,"publicationDate":"2023-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Human Behavior and Emerging Technologies","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1155/2023/6128584","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PSYCHOLOGY, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
By integrating self-determination theory and perceived risk theory, the current research proposes a new model to predict students’ online learning adoption during an emergency situation such as the COVID-19 pandemic. More specifically, it is aimed at exploring how online communication self-efficacy, online learning belonging, and perceived risk predict students’ online learning adoption. A printed questionnaire was developed to collect data from 487 Vietnamese students using a quota sampling method. After missing data and outliers were removed, 450 questionnaires were found to be usable for data analysis. SMARTPLS version 3.2.2 was employed to analyze PLS-SEM and test the proposed hypotheses. The study found that online communication self-efficacy and perceived risk both have direct effects on students’ online learning adoption as well as indirect effects through the partial mediating role of online learning belonging. Our study also explored that perceived risk does not play a moderation in the association between online learning belonging and students’ online learning adoption. These findings fill important gaps in the literature and provide some implications for academicians, governments, educators, and parents in fostering students’ adoption of online learning.
期刊介绍:
Human Behavior and Emerging Technologies is an interdisciplinary journal dedicated to publishing high-impact research that enhances understanding of the complex interactions between diverse human behavior and emerging digital technologies.