Creating a measure to operationalize engaged well-being at work

Abstract

Abstract Background Mental well-being and work engagement are both desirable, positive states of mind that help employees to better function in the workplace. While occupational researchers have argued in favor of considering both states concurrently, it is less clear how this might be translated to provide an instrument characterizing the workforce accordingly. The present study describes empirical efforts to operationalize a construct called engaged well-being. Methods We used employee-level data (n = 13,538) from three waves of the German linked personnel panel (LPP; 2012–2017). Exploratory factor analysis and a combination of hierarchical and non-hierarchical cluster analyses linked with within-sum-of-squares statistics were used to identify distinct profiles describing mental well-being and work engagement concurrently. These profiles were then used as the basis to identify cut-offs to create replicable categories of engaged well-being. Using the longitudinal data from a subgroup providing data across more than one wave, we observed whether the newly constructed indicator changed over time. Results The exploratory factor analysis suggested that both states were two distinct factors. Cluster analysis linked with within-sum-of-squares statistics suggested a four-cluster solution: engaged well-being (46.9%), disengaged well-being (27.5%), engaged strain (8.8%), and disengaged strain (16.8%). One cut-off for each state was identified to replicate the cluster solution. Across observation periods, we could observe changes in engaged well-being. Conclusions Our measure of engaged well-being can be used to simultaneously characterize a workforce’s mental well-being and work engagement. Changes in this measure over time suggest its potential utility in organizational interventions. Future studies are needed to further explore both the antecedents, correlates, and potential effects of engaged well-being.

Keywords

• Mental well-being
• Work engagement
• Employees
• Cluster analysis
• Operationalization