4.6 Article

Continuance Intention of University Students and Online Learning during the COVID-19 Pandemic: A Modified Expectation Confirmation Model Perspective

Journal

SUSTAINABILITY
Volume 13, Issue 8, Pages -

Publisher

MDPI
DOI: 10.3390/su13084586

Keywords

COVID-19; expectation confirmation model; online learning; task-technology fit

Funding

  1. K.C. Wong Magna Fund in Ningbo University [RC190015]
  2. Ningbo Education Science Planning [2020YQZX173]
  3. provincial Qinyuan innovation and entrepreneurship comprehensive experimental teaching center key construction project of the 13th five-year plan of Zhejiang Province (Zhejiang Education Office) [218]
  4. Ministry of Science and Technology, Taiwan [MOST 109-2511-H-019004-MY2, MOST 109-2511-H-019-001]

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The prevalence of COVID-19 has led to changes in traditional teaching modes, promoting the development of online learning. Educational institutions worldwide have launched large-scale online course modes in response to the pandemic. The study showed that students' continuance intention towards online learning during the pandemic was influenced by technical support.
The prevalence of COVID-19 has changed traditional teaching modes. For many teachers, online learning effectively compensated for the absence of traditional face-to-face instruction. Online learning can support students and schools and can create unique opportunities under emergency management. Educational institutions in various countries have launched large-scale online course modes in response to the pandemic. Additionally, online learning during a pandemic differs from traditional online learning modes. Through surveying students in higher education institutions, educational reform under emergency management can be explored. Therefore, university students were surveyed to investigate their continuance intention regarding online learning during the pandemic. Expectation confirmation theory was extended using the task-technology fit model to ascertain whether the technical support of promoting online learning helped student's complete course learning tasks during the pandemic and spawned a continuance intention to use online learning in the future. Data were collected through online questionnaires. A total of 854 valid responses were collected, and partial least squares structural equation modeling was employed to verify the research hypotheses. The results revealed that the overall research framework largely explained continuance intention. Concrete suggestions are proposed for higher education institutions to promote online learning modes and methods after the COVID-19 pandemic.

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