A data-driven typology of individually perceived workplace environments and employee well-being

Abstract

This study adopts a data-driven, employee-centered approach to explore how individually perceived workplace environments shape well-being. Analyzing over 1.3 million observations from 2017 to 2021, we identify nine key job demand and resource factors—including workload, emotional burden, job autonomy, organizational integrity, and support—using factor analysis. Rather than examining these dimensions in isolation, we apply Gaussian mixture models (GMM) to uncover ten distinct workplace types that reflect co-occurring patterns of demands and resources as experienced by individual employees, ranging from Grade D (least favorable) to Grade A1 (most favorable). Our findings reveal substantial variation in employee well-being—including mental health, work engagement, job satisfaction, and workplace cohesiveness—across these clusters. We further examine how changes in workplace type over time relate to changes in individual well-being, highlighting the importance of dynamic and subjective workplace conditions. By centering the employee perspective and identifying lived patterns of workplace experience, this study offers novel insights into workplace dynamics and supports more responsive, employee-oriented organizational policies and practices.