Research

2025

1. 

   TAPAS: A Pattern-Based Approach to Assessing Government Transparency

   Jos Zuijderwijk, Iris Beerepoot, Thomas Martens, and 3 more authors

   In Electronic Government, 2025

   Abs DOI arXiv Bib

   Government transparency, widely recognized as a cornerstone of open government, depends on robust information management practices. Yet effective assessment of information management remains challenging, as existing methods fail to consider the actual working behavior of civil servants and are resource-intensive. Using a design science research approach, we present the Transparency Anti-Pattern Assessment System (TAPAS) — a novel, data-driven methodology designed to evaluate government transparency through the identification of behavioral patterns that impede transparency. We demonstrate TAPAS’s realworld applicability at a Dutch ministry, analyzing their electronic document management system data from the past two decades. We identify eight transparency anti-patterns grouped into four categories: Incomplete Documentation, Limited Accessibility, Unclear Information, and Delayed Documentation. We show that TAPAS enables continuous monitoring and provides actionable insights without requiring significant resource investments.

   @inproceedings{zuijderwijk2025tapas,
     title = {TAPAS: A Pattern-Based Approach to Assessing Government Transparency},
     author = {Zuijderwijk, Jos and Beerepoot, Iris and Martens, Thomas and Knies, Eva and van der Lippe, Tanja and Reijers, Hajo A.},
     year = {2025},
     doi = {10.1007/978-3-032-01589-1_22},
     booktitle = {Electronic Government},
     publisher = {Springer Nature Switzerland},
     address = {Cham},
     pages = {351--367}
   }

2. 

   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 Bib 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.

   @inproceedings{braakman2025miningforwellbeing,
     title = {Mining for Well-Being: The Potential of Process Mining for Evaluating Employee Well-Being},
     author = {Braakman, Mari A. J. and Zuijderwijk, Jos and Beerepoot, Iris and Lugtigheid, Sven and Martens, Thomas and Peeters, Maria and Knies, Eva and Reijers, Hajo A.},
     year = {2025},
     booktitle = {Business Process Management Workshops},
     publisher = {Springer Nature Switzerland},
     address = {Cham},
     pages = {180--191},
     doi = {10.1007/978-3-031-78666-2_14},
     isbn = {978-3-031-78666-2},
     editor = {Gdowska, Katarzyna and G{\'o}mez-L{\'o}pez, Mar{\'i}a Teresa and Rehse, Jana-Rebecca},
   }

2026

1. Shadows of Transparency: Signaling Transparency-Impeding Behavior Using Public Data

   Jos Zuijderwijk, Iris Beerepoot, Thomas Martens, and 3 more authors

   In Electronic Government, 2026

   In press

   Abs arXiv Bib

   Freedom of Information (FOI) laws aim to increase government transparency, yet existing assessments focus mainly on legal compliance and procedural outcomes, leaving organizational behavior underexamined. As FOI processes are increasingly mediated through digital information systems, public response data offer traces of how organizations handle requests and disclosures in practice. This paper develops and evaluates a pattern-based approach that uses such data to signal transparency-impeding behavior across government agencies and over time. Drawing on prior literature, we identify nine recurring behavioral patterns that undermine transparency. Using a dataset of 15,277 Dutch FOI dossiers comprising 139,449 documents, we operationalize and evaluate measurable indicators for six of these patterns, demonstrating that they enable systematic comparison of disclosure practices across agencies and over time. Expert interviews with researchers, journalists, and civil servants confirm the interpretability and practical usefulness of the indicators for investigative prioritization, comparative research, and transparency oversight.

   @inproceedings{zuijderwijk2026sot,
     title = {Shadows of Transparency: Signaling Transparency-Impeding Behavior Using Public Data},
     author = {Zuijderwijk, Jos and Beerepoot, Iris and Martens, Thomas and Knies, Eva and van der Lippe, Tanja and Reijers, Hajo A.},
     year = {2026},
     booktitle = {Electronic Government},
     publisher = {Springer Nature Switzerland},
     address = {Cham},
     note = {In press},
   }