Paper Number
ECIS2026-1516
Paper Type
SP
Abstract
Workarounds recently gained traction in research in practice, as they signal underlying issues in processes, but also reveal potential for bottom-up innovation. Process managers can leverage these purposeful deviations from standard operating procedures, to identify and resolve the responsible organizational or technical misfit. However, those might be caused by other prior workarounds, resulting in workaround chains. Existing research predominantly classifies individual workarounds and their corresponding misfit, neglecting the broader organizational interactions. This Research-in-Progress paper presents an object-centric process mining approach to quantitatively identify potential workaround chains. Our approach builds on the state-of-the-art framework called Semi-automated WORkaround Detection (SWORD), which is now extended to leverage an object-centric perspective. Our new artifact, the ChainSWORD comprises five new object-centric patterns, leverages an advanced case notion and enables the identification of potential workaround chains. However, it remains to be fully implemented and validated as a software solution as part of future research.
Recommended Citation
Löhr, Bernd, "Drafting The ChainSWORD – Towards Identifying Workaround Chains With Object-Centric Process Mining" (2026). ECIS 2026 Proceedings. 5.
https://aisel.aisnet.org/ecis2026/bpm/bpm/5
Drafting The ChainSWORD – Towards Identifying Workaround Chains With Object-Centric Process Mining
Workarounds recently gained traction in research in practice, as they signal underlying issues in processes, but also reveal potential for bottom-up innovation. Process managers can leverage these purposeful deviations from standard operating procedures, to identify and resolve the responsible organizational or technical misfit. However, those might be caused by other prior workarounds, resulting in workaround chains. Existing research predominantly classifies individual workarounds and their corresponding misfit, neglecting the broader organizational interactions. This Research-in-Progress paper presents an object-centric process mining approach to quantitatively identify potential workaround chains. Our approach builds on the state-of-the-art framework called Semi-automated WORkaround Detection (SWORD), which is now extended to leverage an object-centric perspective. Our new artifact, the ChainSWORD comprises five new object-centric patterns, leverages an advanced case notion and enables the identification of potential workaround chains. However, it remains to be fully implemented and validated as a software solution as part of future research.
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