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

This study seeks to explore the effect of fear emotion on students' and teachers' technology adoption during COVID-19 pandemic. The study has made use of Google Meet (c) as an educational social platform in private higher education institutes. The data obtained from the study were analyzed by using the partial least squares structural equation modeling (PLS-SEM) and machine learning algorithms. The main hypotheses of this study are related to the effect of COVID-19 on the adoption of Google Meet as COVID-19 rise s various types of fear. During the Coronavirus pandemic, fear due to family lockdown situation, fear of education failure and fear of losing social relationships are the most common types of threat that may face students and teachers/educators. These types of fears are connected with two important factors within TAM theory, which are: perceived ease of use (PEOU) and perceived usefulness (PU), and with another external factor of TAM, which is subjective norm (SN). The results revealed that both data analysis techniques have successfully provided support to all the hypothesized relationships of the research model. More interesting, the J48 classifier has performed better than the other classifiers in predicting the dependent variable in most cases.

Automated summary

This study examines the impact of fear emotion on the adoption of Google Meet by students and teachers during the COVID-19 pandemic. The findings demonstrate that fear, specifically related to family lockdown, education failure, and loss of social relationships, has a significant effect on the adoption of this educational platform. The study also compares different data analysis techniques and finds that the J48 classifier is the most effective in predicting the dependent variable in most cases.