Investigating students' behavioral intention to use mobile learning in higher education in UAE during Coronavirus-19 pandemic

Abstract

The study explores the impacts of fear emotions on technology adoption by teachers and students during the COVID-19 pandemic. Mobile learning (ML) has been considered an educational, social platform in private and public higher education institutes. Since several fears are connected with COVID-19, this study's key hypotheses are related to how COVID-19 influences Mobile Learning (ML) adoption. Educators, teachers, and students may face some common types of fear in the course of the coronavirus pandemic, such as fear of losing social relationships, fear of educational loss and failure, and fear because of the lockdown of the family in the prevailing circumstances. Different theoretical models, named Expectation-Confirmation Model (ECM) and Technology Acceptance Model (TAM), are combined to develop an integrated model for this study. The proposed model was analyzed with the development of a questionnaire survey. The survey served as a data collection instrument to collect data from students of the University of Sharjah in Sharjah city in the United Arab Emirates (UAE). Three hundred twenty undergraduate students participated in the study. The collected data was evaluated using the partial least squares-structural equation modeling (PLS-SEM). The significant predictors revealed by experimental results included perceived fear, perceived ease of use, expectation confirmation, satisfaction, and perceived usefulness, explaining the intention to use the mobile learning platform. According to our study, teaching and learning can be benefitted to a great extent by the adoption of mobile learning (ML) during this pandemic for educational purposes; however, this process may be negatively affected by the fear of future educational results, fear of losing social relations and fear of stressful family situations. Therefore, appropriate student evaluation may be conducted to overcome the emotional distress caused by the pandemic effectively.