Workarounds as a Cause of Mismatches in Business Processes—

Christian Bartelheimer (), Bernd Löhr, Malte Reineke, Agnes Aßbrock and Daniel Beverungen
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Christian Bartelheimer: University of Göttingen, Faculty of Business and Economics
Bernd Löhr: Paderborn University, Department of Information Systems
Malte Reineke: Paderborn University, Department of Information Systems
Agnes Aßbrock: Paderborn University, Department of Information Systems
Daniel Beverungen: Paderborn University, Department of Information Systems

Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, 2025, vol. 67, issue 3, No 3, 339-356

Abstract: Abstract Process mining has been established as a data-driven approach to analyze and improve business processes based on event data documented in event logs. A core assumption for meaningful analyses is that event data accurately represent the real-world execution of business processes in an organization. However, anecdotal evidence and recent case studies show that these aspects do not always align, and the business process management community is only beginning to investigate the mechanisms generating mismatches between process execution and event data. This study aims to identify the role of workarounds—goal-directed deviations from standard processes performed by process participants to overcome obstacles—in this context. Through an inductive multiple case study of 13 workarounds in four organizations, three mismatch categories between event logs and real-world process execution related to workarounds are identified and explored. This study contributes to the literature by describing how workarounds can act as mechanisms that cause mismatches between process execution and event data, adding to the discussion on process drift and workaround mining. Furthermore, exploring the mismatch categories offers insights for practitioners and researchers on how to handle and interpret data quality issues in event data.

Keywords: Mismatches; Business process management; Process mining; Workarounds; Case study research (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s12599-025-00943-5

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