Understanding the Sequential Pathways of Punitive Supervision and Employee Outcomes: Applying Hayes’ PROCESS Macro With Supervised Machine Learning

Nonwork-related internet usage is catastrophic for organizations since cyberloafing violates work ethics. The time and effort directed toward cyberloafing were meant to be invested in work-related obligations. Cyberloafing magnifies in the presence of punitive supervision, as it is a potential threat to employees’ psychological well-being. This study investigated the sequential mediation of stress and cyberloafing between punitive supervision and turnover intention. A cross-sectional design was utilized to obtain empirical data from 2008 working individuals from diverse sectors. A nonprobability purposive sampling technique was used to select the respondents. Hayes’ PROCESS Macro Model 6 was used to test the sequential mediation model. For supervised machine learning, the Python programming language and Google Colaboratory were employed as critical tools for conducting experiments to validate the research findings. This study highlighted cyberloafing as counterproductive work behavior catalyzed by punitive supervision. The diverse negative constructs with argumentation from the COR theory enriched the theoretical frameworks for understanding the psychological orientations of employees at work. The study findings facilitate fostering a supportive organizational culture for reducing turnover and enhancing well-being. This study highlights the role of workplace stability and efficiency for sustainable economic growth because a socially sustainable organization can make employees feel valued and reduce turnover. Both integrated methodologies demonstrate the hypothesized sequential mediation model. The theoretical and practical implications and directions for further studies are also discussed.