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Investigating Lifelong Learners’ Continuing Learning Intention Moderated by Affective Support in Online Learning

Author

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  • Wen Tang

    (School of Education, China Central Normal University, Wuhan 430079, China
    School of Open Learning, Jiangsu Open University, Nanjing 212013, China)

  • Xiangyang Zhang

    (School of Open Learning, Jiangsu Open University, Nanjing 212013, China
    Research Institute for Open Education, School of Foreign Studies, Suqian University, Suqian 223805, China)

  • Youyi Tian

    (School of Education, China Central Normal University, Wuhan 430079, China)

Abstract

During the pandemic crises, online learning has moved from the margin to the centre of education, thus making a change in the educational paradigm. The degrees of technology acceptance, therefore, pose a major challenge to administrators, educators, and students, including lifelong learners from all fields of work. This paper aims to investigate how technology acceptance facilitates lifelong learners’ continuing learning intention. By constructing a structural equation model based on the Technology Acceptance Model (TAM) theory and verifying the mediating role of learning satisfaction and the moderating role of affective support empirically, this study addresses the online learning characteristics in terms of self-efficacy, satisfaction, and willingness to learn in the context of lifelong learners. Data were collected from the questionnaires embedded in the courses at a distance teaching university. A total of 513 questionnaires have been retrieved and 488 valid questionnaires have been processed and analysed via SPSS 26.0 and Amos 24.0. The findings indicate that positive relationships and positive correlations between learning satisfaction and continuing learning intention juxtapose, and satisfaction is positively correlated with continuing intention. Moreover, affective support moderates the three components significantly. The results of this research help expand the applicability of the TAM theory with more practical significance, conveying positive messages to the administrators, educators and lifelong learners in continuance of online learning, and thus maintaining retention, particularly in the post-pandemic new normal for the sustainability of higher education.

Suggested Citation

  • Wen Tang & Xiangyang Zhang & Youyi Tian, 2023. "Investigating Lifelong Learners’ Continuing Learning Intention Moderated by Affective Support in Online Learning," Sustainability, MDPI, vol. 15(3), pages 1-18, January.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:3:p:1901-:d:1040708
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    References listed on IDEAS

    as
    1. Wen Tang & Xiangyang Zhang & Youyi Tian, 2022. "Mitigation of Regional Disparities in Quality Education for Maintaining Sustainable Development at Local Study Centres: Diagnosis and Remedies for Open Universities in China," Sustainability, MDPI, vol. 14(22), pages 1-20, November.
    2. Mengfan Li & Ting Wang & Wei Lu & Mengke Wang, 2022. "Optimizing the Systematic Characteristics of Online Learning Systems to Enhance the Continuance Intention of Chinese College Students," Sustainability, MDPI, vol. 14(18), pages 1-17, September.
    3. Aleksandra Kobicheva & Elena Tokareva & Tatiana Baranova, 2022. "Students’ Affective Learning Outcomes and Academic Performance in the Blended Environment at University: Comparative Study," Sustainability, MDPI, vol. 14(18), pages 1-14, September.
    4. James Anderson & David Gerbing, 1984. "The effect of sampling error on convergence, improper solutions, and goodness-of-fit indices for maximum likelihood confirmatory factor analysis," Psychometrika, Springer;The Psychometric Society, vol. 49(2), pages 155-173, June.
    5. Viswanath Venkatesh & Fred D. Davis, 2000. "A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies," Management Science, INFORMS, vol. 46(2), pages 186-204, February.
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