Predicting user perceived satisfaction and reuse intentions toward Massive Open Online Courses (MOOCs) in the Covid-19 pandemic
Qing Wang, Muhammad Saqib Khan, Muhammad Kamran Khan
Abstract
The Covid-19 pandemic restricted the people around the world's boundaries. Therefore, online learning's importance increased in the current era, an essential topic for current research. Students are actively using Massive Open Online Courses (MOOCs) while restricted at their homes during the Covid-19 pandemic. This research investigates the Unified Theory of Acceptance and Use of Technology model (UTAUT) and quality factors to predict the users perceived satisfaction and reuse intentions toward MOOCs in the Covid-19 pandemic. We collected data from three public universities in Wuhan, China and 298 users who were actively using MOOCs participated in this research. The proposed hypotheses were tested by using PLS-SEM. The findings revealed that effort expectancy and social influence directly impacted users' reuse intentions while performance expectancy and perceived course quality positively impacted users' reuse intentions through perceived satisfaction toward MOOCs. This research found the critical role of perceived satisfaction in the current pandemic era. Finally, this research provides important theoretical implications for the researchers and practical implications for the developers, technologists, and policymakers for developing effective systems and strategies in online environments. In addition, this study revealed some limitations and future research guidelines for the researchers.