Abstract

This paper focuses on the impact of fear emotion upon technology adoption by educators and students during Covid-19 pandemic. Mobile learning (m-learning) has been applied as the educational social platform within higher education institutes, public as well as private. The research hypotheses were associated with the Covid-19 influence on m-learning adoption with the rise of the coronavirus increasing types of fear. Such fears include fear caused by the education failure, family lockdown, and loss of social relationships. Teachers and students are mostly fearful of these aspects of the situation. An integrated model was established within the research, using theoretical models; the Planned Behavior theory, the Technology Acceptance Model, and the Expectation-Confirmation Model. The proposed integrated model (using PLS-SEM software) was analyzed using an online survey data, with 420 respondents from Zayed University, UAE. The findings indicated that attitude was the best predictor for using the m-learning system, followed by continuous intention, expectation confirmation, perceived usefulness, ease-of-use, perceived fear, behavioral control, and satisfaction. According to the research, during the coronavirus pandemic, if the m-learning system is adopted for educational reasons, the learning and teaching outcome proves quite promising. Yet there is a fear of the family being stressed, or of loss of friends, and also a fear of the results of future schooling. It is therefore necessary to assess the students efficiently during this pandemic so that the situation can be managed emotionally.

Highlights

  • Several efforts have been made by universities and colleges to establish a virtual teaching environment using appropriate platforms and resources (Salloum & Shaalan, 2021; Alhashmi, Salloum, & Abdallah, 2020; Alshurideh, Salloum, Al Kurdi, Monem, et al, 2019; Hantoobi et al, 2021; Salloum, Mhamdi, et al, 2018; Sultan et al 2021)

  • The findings indicated that attitude was the best predictor for using the m-learning system, followed by continuous intention, expectation confirmation, perceived usefulness, ease-of-use, perceived fear, behavioral control, and satisfaction

  • The literature suggests that the Technology Acceptance Model (TAM), Expectation-Confirmation Model (ECM) and Theory of Planned Behavior (TPB) are successfully applied as the technology adoption model to measure the motivation of users to understand (Liu, Geertshuis, & Grainger, 2020; Sami Alkalha et al, 2012; Tsai et al, 2020)

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Summary

Background

Several efforts have been made by universities and colleges to establish a virtual teaching environment using appropriate platforms and resources (Salloum & Shaalan, 2021; Alhashmi, Salloum, & Abdallah, 2020; Alshurideh, Salloum, Al Kurdi, Monem, et al, 2019; Hantoobi et al, 2021; Salloum, Mhamdi, et al, 2018; Sultan et al 2021) These educational institutions are constantly struggling to attain specific outcomes. The literature suggests that the Technology Acceptance Model (TAM), Expectation-Confirmation Model (ECM) and Theory of Planned Behavior (TPB) are successfully applied as the technology adoption model to measure the motivation of users to understand (Liu, Geertshuis, & Grainger, 2020; Sami Alkalha et al, 2012; Tsai et al, 2020) This current research is based on the TAM, TPB and ECM and adding two external elements: subjective norms and fear. The technology adoption field would be contributed, practically and theoretically

Literature review
Alhumaid / International Journal of Data and Network Science 5 (2021)
Knowledge gap
Research model
Perceived Fear (PF)
The TAM
Satisfaction (SAT)
Attitude (ATD)
Perceived Behavioral Control (PBC)
Subjective norm (SBJ)
The Continuous intention (CON)
Data collection
Demographic data
The instrument
Pre-testing the questionnaire
Common method bias (CMB)
Findings
Convergent validity
Discriminant validity
Model fit
Testing the Hypotheses
H2 H3 H4 H5 H6 H7 H8 H9 H10 H11 H12 H13 tests
Conclusion
Research implications
Limitations and future research
Recommendations
Full Text
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