Employee well-being research is receiving growing attention as organizations are increasingly turning to well-being improvement to promote employee health and reduce health-related expenditures. Traditional organizational and occupational health studies often examine relationships between employee well-being and its antecedents and outcomes from a variable-centered perspective. The current study adopted a holistic and person-centered approach to well-being assessment, and (1) identified clusters of employees who shared common configurations with regard to multiple dimensions of psychosocial well-being (i.e., purpose, social, financial, and community). A profile-based perspective is a more intuitive way for employers/managers to understand employee well-being. The current study also (2) examined physical, work-related, and demographic predictors of profile membership, (3) investigated how profile membership distinguished employees on physical well-being and work-related productivity outcomes, and (4) determined the stability and transition patterns of well-being profiles over time. Study hypotheses and research questions were tested using latent mixture modeling, specifically Latent Profile Analysis (LPA) and Latent Transition Analysis (LTA). A large U.S. population-based dataset containing a representative employee sample was first used to conduct exploratory LPAs and determine the best-fitting profile solution. Two additional two-wave longitudinal employee samples were used to cross-validate the final profile solution, and test the hypotheses regarding profile antecedents, outcomes, and stability. Six distinct psychosocial well-being profiles emerged – discontented, contented, highly contented, financial-dominant, financially insecure, and lack of community well-being. Physical, work-related, and demographic factors were significant predictors of profile membership. Well-being profiles also distinguished employees on physical well-being and job performance. LTAs revealed that well-being profiles were largely stable over time, and some of the profile predictors and outcomes explained the transition probabilities. Results of the current study provide meaningful information and feedback for employer-sponsored well-being improvement programs. A profile-based understanding of employee well-being allows employers/managers to tailor intervention programs based on the needs of specific employee groups, as well as encourage (prevent) movement toward profiles associated with positive (negative) outcomes. Additional implications and directions for future research are discussed
