Mining for Well-Being: The Potential of Process Mining for Evaluating Employee Well-Being

Abstract

Monitoring work-related well-being is crucial for organizational success and part of good employment practices. This paper explores how process mining can evaluate employee well-being by conceptualizing variables of various work characteristics using the Job Demands-Resources model (JD-R), which explains how work characteristics influence employee well-being. We explored how the process mining variables compare to validated survey measures. Data was collected in two ways: first, a survey was conducted to measure the work characteristics of monotonous work, time pressure, workload, social support, and autonomy and the well-being outcomes of burnout, boredom, and work engagement. Second, process mining was used to calculate scores for the same work characteristics so that the scores could be compared with the survey variables. No strong correlations were found between corresponding survey variables and process mining variables. However, results reveal strong correlations between process mining variables of workload, social support, and autonomy with the survey variable of work engagement. These findings suggest that process mining variables can be valuable for predicting work-related well-being, especially work engagement. The combination of process mining and survey research has the potential to increase our comprehension of work-related well-being, make data collection more efficient, and monitor work engagement continuously.