The application of process mining in determining employee well-being

Abstract

The monitoring of well-being has become increasingly popular in the last decade as higher employee well-being results in better performance and reduces the number of burnouts. Surveys and interviews are the most popular instruments for determining well-being. However, these instruments are cross-sectional, making it difficult to continuously monitor well-being. Process mining is a discipline that has the potential to measure well-being without this drawback. This project investigates to what degree work-related parts (job demands & resources) of well-being can be determined with process mining.

A literature study revealed that five job demands & resources can be measured based on human behaviour, these being: workload, time pressure, monotonous work, autonomy and social support. These five can measure burnout, boredom and work engagement to a great extent. We investigated which process mining techniques are related to the selected job demands & resources. We observed that workload, time pressure, monotonous work and autonomy can be measured in their entirety through process mining whilst social support can be measured partially.

Finally, a case study was conducted to analyse whether the proposed application of measuring the job demands & resources is correct. Four of the five job demands & resources have a medium correlation with the key strain or motivation that they theoretically should measure. Only time pressure has no significant relation with its theoretical strain, burnout.

To conclude, three out of the five job demands & resources, workload, monotonous work, and autonomy, can be measured entirely using process mining and one, social support, partly.