IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i13p7830-d848977.html
   My bibliography  Save this article

Understanding Students’ Acceptance and Usage Behaviors of Online Learning in Mandatory Contexts: A Three-Wave Longitudinal Study during the COVID-19 Pandemic

Author

Listed:
  • Da Tao

    (Institute of Human Factors and Ergonomics, College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, China)

  • Wenkai Li

    (Institute of Human Factors and Ergonomics, College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, China)

  • Mingfu Qin

    (Department of Music Education, School of Primary Education, Hunan Vocational College for Nationalities, Yueyang 414000, China)

  • Miaoting Cheng

    (Department of Educational Technology, Faculty of Education and Institute of KEEP Collaborative Innovation, Shenzhen University, Shenzhen 518060, China)

Abstract

Online learning has been mandatorily adopted in many countries due to the closure of educational institutions caused by the COVID-19 pandemic. However, antecedents of the acceptance and continuance of online learning in such a situation and their changing role over time have not been well understood. This study proposed and empirically tested a longitudinal acceptance model of online learning by integrating the technology acceptance model (TAM) with the task–technology fit (TTF). Data were collected using a three-wave longitudinal survey from 251 Chinese college students after the outbreak of the COVID-19 pandemic. The results showed that most hypothesized relationships in the proposed model were supported and remained across the three-time stages, while the effects of perceived ease of use on perceived usefulness and behavioral intention changed over time. In addition, students’ perceptions at previous stages had little impact on perceptions at subsequent stages, except for perceived usefulness and behavioral intention. Our study demonstrates that the integrated model of TAM and TTF could be an effective tool to understand students’ acceptance of online learning across different time stages in a mandatory setting and that longitudinal design could be applicable to examine the changing mechanism of the acceptance and continuance use of online learning over time.

Suggested Citation

  • Da Tao & Wenkai Li & Mingfu Qin & Miaoting Cheng, 2022. "Understanding Students’ Acceptance and Usage Behaviors of Online Learning in Mandatory Contexts: A Three-Wave Longitudinal Study during the COVID-19 Pandemic," Sustainability, MDPI, vol. 14(13), pages 1-17, June.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:13:p:7830-:d:848977
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/13/7830/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/13/7830/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Nancy Paule Melone, 1990. "A Theoretical Assessment of the User-Satisfaction Construct in Information Systems Research," Management Science, INFORMS, vol. 36(1), pages 76-91, January.
    2. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Hailiang Wang & Jiaxin Zhang & Yan Luximon & Mingfu Qin & Ping Geng & Da Tao, 2022. "The Determinants of User Acceptance of Mobile Medical Platforms: An Investigation Integrating the TPB, TAM, and Patient-Centered Factors," IJERPH, MDPI, vol. 19(17), pages 1-17, August.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Savaya, Riki & Monnickendam, Menachem & Waysman, Mark, 2006. "Extent and type of worker utilization of an integrated information system in a human services agency," Evaluation and Program Planning, Elsevier, vol. 29(3), pages 209-216, August.
    2. Claudio Vitari & Roxana Ologeanu-Taddei, 2018. "The intention to use an electronic health record and its antecedents among three different categories of clinical staff," Post-Print halshs-01923238, HAL.
    3. Brown, Susan A. & Venkatesh, Viswanath & Kuruzovich, Jason & Massey, Anne P., 2008. "Expectation confirmation: An examination of three competing models," Organizational Behavior and Human Decision Processes, Elsevier, vol. 105(1), pages 52-66, January.
    4. Sung S. Kim & Naresh K. Malhotra, 2005. "A Longitudinal Model of Continued IS Use: An Integrative View of Four Mechanisms Underlying Postadoption Phenomena," Management Science, INFORMS, vol. 51(5), pages 741-755, May.
    5. Kwahk, Kee-Young & Ahn, Hyunchul & Ryu, Young U., 2018. "Understanding mandatory IS use behavior: How outcome expectations affect conative IS use," International Journal of Information Management, Elsevier, vol. 38(1), pages 64-76.
    6. Rajiv D. Banker & Robert J. Kauffman, 2004. "50th Anniversary Article: The Evolution of Research on Information Systems: A Fiftieth-Year Survey of the Literature in Management Science," Management Science, INFORMS, vol. 50(3), pages 281-298, March.
    7. Andrew Burton-Jones & Detmar W. Straub, 2006. "Reconceptualizing System Usage: An Approach and Empirical Test," Information Systems Research, INFORMS, vol. 17(3), pages 228-246, September.
    8. Zhen Wang & John Lim & Xiaojia Guo, 2010. "Negotiator Satisfaction in NSS-Facilitated Negotiation," Group Decision and Negotiation, Springer, vol. 19(3), pages 279-300, May.
    9. Claudio Vitari & Roxana Ologeanu-Taddei, 2018. "The intention to use an electronic health record and its antecedents among three different categories of clinical staff," Grenoble Ecole de Management (Post-Print) halshs-01923238, HAL.
    10. Saeideh Sharifi fard & Ezhar Tamam & Md Salleh Hj Hassan & Moniza Waheed & Zeinab Zaremohzzabieh, 2016. "Factors affecting Malaysian university students’ purchase intention in social networking sites," Cogent Business & Management, Taylor & Francis Journals, vol. 3(1), pages 1182612-118, December.
    11. Chou, Jui-Sheng & Gusti Ayu Novi Yutami, I, 2014. "Smart meter adoption and deployment strategy for residential buildings in Indonesia," Applied Energy, Elsevier, vol. 128(C), pages 336-349.
    12. Philippe Cohard, 2020. "Information Systems Values: A Study of the Intranet in Three French Higher Education Institutions," Post-Print hal-02987225, HAL.
    13. Melih Engin & Fatih Gürses, 2019. "Adoption of Hospital Information Systems in Public Hospitals in Turkey: An Analysis with the Unified Theory of Acceptance and Use of Technology Model," International Journal of Innovation and Technology Management (IJITM), World Scientific Publishing Co. Pte. Ltd., vol. 16(06), pages 1-19, October.
    14. Morosan, Cristian, 2016. "An empirical examination of U.S. travelers’ intentions to use biometric e-gates in airports," Journal of Air Transport Management, Elsevier, vol. 55(C), pages 120-128.
    15. Abdesamad Zouine & Pierre Fenies, 2014. "The Critical Success Factors Of The ERP System Project: A Meta-Analysis Methodology," Post-Print hal-01419785, HAL.
    16. Talukder, Md. Shamim & Sorwar, Golam & Bao, Yukun & Ahmed, Jashim Uddin & Palash, Md. Abu Saeed, 2020. "Predicting antecedents of wearable healthcare technology acceptance by elderly: A combined SEM-Neural Network approach," Technological Forecasting and Social Change, Elsevier, vol. 150(C).
    17. Sarv Devaraj & Robert F. Easley & J. Michael Crant, 2008. "Research Note ---How Does Personality Matter? Relating the Five-Factor Model to Technology Acceptance and Use," Information Systems Research, INFORMS, vol. 19(1), pages 93-105, March.
    18. Claudio Vitari & Elisabetta Raguseo, 2016. "Big data value and financial performance: an empirical investigation [Digital data, dynamic capability and financial performance: an empirical investigation in the era of Big Data]," Post-Print halshs-01923271, HAL.
    19. Chen-Yuan Chen & Bih-Yaw Shih & Shih-Hsien Yu, 2012. "Disaster prevention and reduction for exploring teachers’ technology acceptance using a virtual reality system and partial least squares techniques," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 62(3), pages 1217-1231, July.
    20. Mäntymäki, Matti & Salo, Jari, 2013. "Purchasing behavior in social virtual worlds: An examination of Habbo Hotel," International Journal of Information Management, Elsevier, vol. 33(2), pages 282-290.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:14:y:2022:i:13:p:7830-:d:848977. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.