Business processes performed in organizations often deviate from the abstract process models issued by designers. Workarounds that are carried out by process participants to increase the effectiveness or efficiency of their tasks are often viewed as negative deviations from prescribed business processes, interfering with their efficiency and quality requirements. But workarounds might also play an important role in identifying and re-structuring inefficient, dysfunctional, or obsolete processes. While ethnography or critical incident techniques can serve to identify how and why workarounds emerge, we need automated methods to detect workarounds in large data sets. We set out to design a method that implements a deep learning- based approach for detecting workarounds in event logs. An evaluation with three public real-life event logs exhibits that the method can identify workarounds best in standardized business processes that contain fewer variations and a higher number of different activities. Our method is one of the first IT artifacts to bridge boundaries between the complementing research disciplines of organizational routines and business processes management.
Weinzierl, Sven; Wolf, Verena; Pauli, Tobias; Beverungen, Daniel; and Matzner, Martin, "DETECTING WORKAROUNDS IN BUSINESS PROCESSES—A DEEP LEARNING METHOD FOR ANALYZING EVENT LOGS" (2020). In Proceedings of the 28th European Conference on Information Systems (ECIS), An Online AIS Conference, June 15-17, 2020.
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