Detection and Analysis of Cyberloafing Behaviors in The Workplace: A Multi-Method Literature Review

This study presents a systematic literature review on the growing concern of cyberloafing behaviors in workplaces. Cyberloafing, defined as non-work-related internet usage during work hours, has implications for productivity, employee motivation, and organizational dynamics. In this study, we examine detection methods, including machine learning, browser history analysis, and social media activity tracking, and critically evaluate their applicability. While each method has unique strengths, we argue that integrated, ethically responsible approaches are necessary. The study highlights gaps in current models and suggests future directions involving multi-source data fusion and adaptive monitoring algorithms.