Understanding cyberslacking intention during Covid-19 online classes: An fsQCA analysis

Abstract

Due to the Covid-19 pandemic, students have been restricted to their homes and forced to take online classes as an alternative for in-person classes. However, data shows that this enforced online education is resulting in a learning deficit for a generation of students and that many engage in cyberslacking behavior, which is the use of non-work-related Internet use during designated work time. Cyberslacking tendency among university students in particular is on the rise. A fuzzy-set qualitative comparative analysis (fsQCA) was applied. Sentiment analysis was subsequently performed using Natural Language Processing. Findings from the fsQCA analysis identified five core factors that underpin cyberslacking attention. Alternative paths have been identified based on gender and the students’ current education status. The study findings contain a number of contributions, illustrating different topologies of student intention towards cyberslacking and identifying causal factors that influence cyberslacking intention, as well as illustrating student sentiment regarding this behavior.