Research | Jos Zuijderwijk

As a PhD candidate at Utrecht University, I research digital innovations that enhance government transparency. Under the supervision of Prof. Hajo Reijers, Prof. Tanja van der Lippe, and Dr. Iris Beerepoot, I investigate how government agencies can operate transparently in today’s digital information environment with its complex processes and massive information flows. Through a multidisciplinary approach combining computer science, sociology, and public administration, I analyze actual workplace behavior through document management data to identify patterns that impact transparency. Working together with the Ministry of Infrastructure and Water Management, I aim to develop scientific methods and practical tools that help civil servants effectively manage information.

Publications

An up-to-date list is available on my Google Scholar.

2025

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

Mari A. J. Braakman, Jos Zuijderwijk, Iris Beerepoot, and 5 more authors

In Business Process Management Workshops, 2025

Abs DOI PDF

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.

Teaching

I worked as a TA for the following courses

KI1V13001 Introduction to Logic (2017 — 2020) 📖 course material
INFOB1MNP Modelling & Programming (2016 — 2019)
KI1V13004 Introduction to Linguistics (2017, 2021)
TW3V19002 Language and Computation (2022)
INFO3BIT Interaction Technology (2023) 📖 course material
INFOB1PROM Process Modelling (2025)

Supervision

I was the (co-)supervisor of several students in their thesis projects at Utrecht University (sorted in reverse chronological order).

Bonne van Rijzingen (2024): “The Potential Impact of Intelligent Automation on the European Labor Market”, BSc thesis
Bastiaan van Gilst (2024): “Comparing Location-Specific Factors in Digital Adoption at Dutch Prisons”, MSc thesis