The 2019 Pandemic has forced students to take online classes, increasing their stress levels and negatively impacting their academic performance. This issue urges the development of a mechanism to make online learning more effective in this nerve-racking time. Therefore, the present study has integrated the task technology fit (TTF) model and the DeLone and McLean Model of Information Systems Success (DMISM) to address the stated issue. The data were collected from 330 and 326 students of top-ranked public and private universities of Punjab, respectively. The theoretical framework was analyzed with the help of structural equation modeling (SEM) using Analysis of Moment Structures (AMOS). The findings indicate that overall quality positively predicts performance through the mediating role of user satisfaction and TTF. The overall quality also positively elevates performance through the mediating role of user satisfaction and actual usage of the system. Moreover, perceived usefulness proved to be a moderator between overall quality and user satisfaction. Finally, the expected practical and theoretical implications have also been discussed.

With the rapid spread of COVID-19 worldwide, governments of all countries declared the closure of educational institutions to control its transmission. As a result, institutions were under pressure to offer online education opportunities so that students could continue their education without interruption. The unintended, hasty and unknown duration of the strategy encountered challenges at all pedagogical levels, especially for students who felt stressed out by this abrupt shift, resulting in the decline of their academic performance. Hence, it is necessary to comprehend the approach that might improve students’ involvement and performance in online learning. In this context, the current study used four models to understand the phenomenon: the Task Technology Fit (TTF), the DeLone and McLean Model of Information Systems Success (DMISM), the Technology-to-Performance Chain model (TPC) and the Technology Acceptance Model (TAM). The data for this study were obtained from 404 university students from the top ten universities of Pakistan. The results analyzed using structural equation modeling (SEM) show that learner characteristics positively predict performance through user satisfaction and task technology fit mediating function. Moreover, learner characteristics were also observed to have a significant positive influence on the academic performance of the students, with the mediating functions of user satisfaction and actual usage of the system. Likewise, perceived learning moderated the relationship between learner characteristics and user satisfaction. This research work provides policymakers with a profound framework that emphasizes how employing online learning technologies can strengthen the academic potential of students.