Fear from COVID-19 and technology adoption: the impact of Google Meet during Coronavirus pandemic

Al-Maroof, Rana Saeed; Salloum, Said A.; Hassanien, Aboul Ella; Shaalan, Khaled

doi:10.1080/10494820.2020.1830121

Cited by 199 publications

(196 citation statements)

References 52 publications

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“…Our analysis strongly supported the proposed research model. The findings of other researchers [23,41,[43][44][45][47][48][49][50][56][57][58]63] and our results have similarities.…”

Section: Principal Findingssupporting

confidence: 86%

“…Our hypotheses (ie, H1, H5 and H6) significantly and positively supported the relationships between the SN and PEOU (P=.001), the SN and PU (P=.03), and the SN and PF (P=.04). Numerous research studies have assessed the relationship between the SN and PEOU, the SN and PU, and the SN and PF [23,41,[43][44][45]. Moreover, our analysis provided empirical evidence for the effect of the PEOU on PU, as proposed in hypothesis H2.…”

Section: Principal Findingssupporting

confidence: 58%

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Using Machine Learning Algorithms to Predict People’s Intention to Use Mobile Learning Platforms During the COVID-19 Pandemic: Machine Learning Approach (Preprint)

Akour¹,

Alshurideh²,

Kurdi³

et al. 2020

Preprint

Self Cite

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BACKGROUND This paper investigates the use of mobile learning platforms for learning purposes among university students in UAE. An extended Technology Acceptance Model (TAM) and theory of planned behavior (TPB) are proposed to analyze the adoption of mobile learning platforms by university students for accessing course materials, searching the web for information related to their discipline, sharing knowledge, conducting assignments during COVID-19 pandemic. The total number of questionnaires collected was 1880 form different universities. Partial least squares-structural equation modeling (PLS-SEM) and machine learning algorithms (ML) were utilized to investigate the research model based on the student’s data gathered through a survey. According to the results, each hypothesized relationship within the research model has been supported by the data analysis methods. It should also be noted that the J48 classifier mostly had the upper hand on other classifiers when it comes to the prediction of the dependent variable. As per the indication of our research, teaching and learning can greatly benefit from the adoption of machine learning as an educational tool at the time of this pandemic; nevertheless, its significance could be lowered because of the emotion of fear concerning poor grades, stressful family circumstances, and loss of friends. Accordingly, this issue can only be solved by evaluating the emotions of students during this pandemic. OBJECTIVE This study is one of the earliest attempt to: (1) theoretically integrate the notion of fear within a hybrid model of Technology Acceptance Model (TAM) & Theory of Planned Behavior (TPB) (2) empirically test the effect of COVID-19 on the users of mobile application, and (3) explore the impact of the Coronavirus pandemic on users' ability to use the mobile application easily and users' attitude towards the usefulness of mobile learning platform. METHODS The developed theoretical model has been evaluated using two different techniques in this research. The first one involves the usage of the partial least squares-structural equation modeling (PLS-SEM) alongside the SmartPLS tool. This research uses PLS-SEM mainly because both the structural and measurement model can be concurrently analyzed through PLS-SEM, which increases the preciseness of results. As for the second technique, the research predicts the dependent variables entailing the conceptual model with the help of machine learning algorithms via Weka. RESULTS The present research has implemented a model that would be useful for future studies to be conducted since it helps assess the COVID-19 influence at the time of the pandemic period. Keeping the research results in mind, and the fear factor present during the period, the ML is considered to be a significantly useful tool which helps reduce the fear present within the peers and instructors. Similarly, the perceived fear (PF) highly affects the PU and PEU. According to the responses, during the pandemic period, the PF is quite evident; however, the ML maintains a high PU and PEU degree, which reduces the fear factor and encourages the students to participate in their scheduled class. CONCLUSIONS The current research results are similar to the ones presented in earlier research studies related to the TAM and TPB variable’s importance (Ajzen, 1985; F. D Davis, 1989; Teo, 2012; V Venkatesh & Bala, 2008). It is observed that the students are much more acceptable towards technology is there is nothing but the ML technology available as the tool for learning during the COVID-19 pandemic. The PU and PEU related results are also similar to the ones of the earlier PU and PEU related results that influence the student acceptance of ML. Hence, it should be considered as an indicator for the students intention to make use of the ML when the environment is infected with COVID-19. Furthermore, PU is highly affected by PEU, which indicates that if it is easy to use the technology, then it would be considered useful.

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“…Our analysis strongly supported the proposed research model. The findings of other researchers [23,41,[43][44][45][47][48][49][50][56][57][58]63] and our results have similarities.…”

Section: Principal Findingssupporting

confidence: 86%

Section: Principal Findingssupporting

confidence: 58%

Using Machine Learning Algorithms to Predict People’s Intention to Use Mobile Learning Platforms During the COVID-19 Pandemic: Machine Learning Approach (Preprint)

Akour¹,

Alshurideh²,

Kurdi³

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

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“…The fear factor was tackled in recent studies by [27] as an external factor that affects students' perception of technology during the pandemic. The fear from COVID-19 is manifested in forms such as threat, anxiety, feeling of uncertainty, the risk for loved ones [19,[28][29][30][31][32].…”

Section: Perceived Fearmentioning

confidence: 99%

Acceptance of Google Meet during the Spread of Coronavirus by Arab University Students

et al. 2021

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The COVID-19 pandemic not only affected our health and medical systems but also has created large disruption of education systems at school and universities levels. According to the United Nation’s report, COVID-19 has influenced more than 1.6 billion learners from all over the world (190 countries or more). To tackle this problem, universities and colleges have implemented various technologically based platforms to replace the physical classrooms during the spread of Coronavirus. The effectiveness of these technologies and their educational impact on the educational sector has been the concern of researchers during the spread of the pandemic. Consequently, the current study is an attempt to explore the effect of Google Meet acceptance among Arab students during the pandemic in Oman, UAE, and Jordan. The perceived fear factor is integrated into a hybrid model that combines crucial factors in TAM (Technology acceptance Model) and VAM (Value-based Adoption Model). The integration embraces perceived fear factor with other important factors in TAM perceived ease of use (PEOU) and perceived usefulness (PU) on the one hand and technically influential factor of VAM, which are perceived technicality (PTE) and perceived enjoyment (PE) on the other hand. The data, collected from 475 participants (49% males and 51% females students), were analyzed using the partial least squares-structural equation modelling (PLS-SEM). The results have shown that TAM hypotheses of usefulness and easy to use have been supported. Similarly, the results have supported the hypotheses related to VAM factors of being technically useful and enjoying, which helps in reducing the atmosphere of fear that is created due to the spread of Coronavirus.

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“…Previous studies have confirmed a relationship between health anxiety or fear emotion and using healthrelated technologies. Al-Maroof et al (2020) [4] found a significant effect of fear emotion related to Google Meet adoption during COVID-19. Wnuk et al (2020) [5] revealed that an adoption of COVID-19 tracking technologies was significantly predicted by users' perceived personal threat and lack of personal control.…”

Section: -Introductionmentioning

confidence: 99%

Explaining an Adoption and Continuance Intention to Use Contactless Payment Technologies: During the COVID-19 Pandemic

Puriwat

Tripopsakul

2021

Emerg Sci J

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The COVID-19 pandemic has affected the lives of people and services, pushing them toward new technologies that are in step with the development of a “New Normal” way of life. Contactless technologies have been realized as a mechanism to reduce the risks of infection, accelerating the move to touchless behaviors. The purpose of this study is to develop an Integrated Expectation-Confirmation and Health Belief Model (ECHBM) to explain an adoption and continuance intention to use contactless technologies during the COVID-19 pandemic in Thailand. Based on an empirical research survey of 142 samples, the proposed conceptual model was empirically validated using structural equation modelling (SEM). The study found that perceived usefulness, perceived susceptibility, perceived seriousness, and satisfaction significantly influenced continuance usage intention of contactless payment technologies, whereas perceived usefulness and confirmation were found to be significant determinants of consumer satisfaction. The effect of perceived susceptibility was found to be relatively higher than that of satisfaction, and confirmation was found to have an indirect effect on continuance usage intention through perceived usefulness and satisfaction. The integrated ECHBM model has strong explanatory power (56.8%) to predict customers’ continuance usage intention toward use of contactless payment technologies during the COVID-19 pandemic. The study proposes a novel challenge to explain an adoption and continuance intention to use contactless payment technologies as a protective health behavior to mitigate risks of being infected by COVID-19. Doi: 10.28991/esj-2021-01260 Full Text: PDF

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Fear from COVID-19 and technology adoption: the impact of Google Meet during Coronavirus pandemic

Cited by 199 publications

References 52 publications

Using Machine Learning Algorithms to Predict People’s Intention to Use Mobile Learning Platforms During the COVID-19 Pandemic: Machine Learning Approach (Preprint)

Using Machine Learning Algorithms to Predict People’s Intention to Use Mobile Learning Platforms During the COVID-19 Pandemic: Machine Learning Approach (Preprint)

Acceptance of Google Meet during the Spread of Coronavirus by Arab University Students

Explaining an Adoption and Continuance Intention to Use Contactless Payment Technologies: During the COVID-19 Pandemic

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