Weinzierl S, Wolf V, Pauli T, Beverungen D, Matzner M (2022)
Publication Language: English
Publication Type: Journal article, Original article
Publication year: 2022
Book Volume: 5
Pages Range: 76-100
Journal Issue: 1
URI: https://www.tandfonline.com/doi/full/10.1080/2573234X.2021.1978337
DOI: 10.1080/2573234X.2021.1978337
Open Access Link: https://www.tandfonline.com/doi/full/10.1080/2573234X.2021.1978337
Business process management distinguishes the actual “as-is” way work is accomplished and a prescribed or desired “to-be” version of a process. In practice, many different causes trigger a process’s drifting away from a once designed to-be state. For instance, employees may “workaround” the proposed systems or protocols in an attempt to increase their effectiveness or efficiency in day-to-day work. Workarounds are often viewed as negative deviations that interfere with security and quality requirements. This article emphasises their role in identifying and restructuring dysfunctional or obsolete processes. So far, ethnography or critical incident techniques are used to identify how and why workarounds emerge. We design a method that helps detect different types of workarounds in event logs using a deep-learning-based approach. Event logs are large data sets documenting the completion of process work based on information systems. Our method tracks indications of potential workarounds in the early stages of their emergence among deviating behaviour. Our evaluation based on four real-life event logs reveals that our method performs well and works best for business processes with fewer variations and a higher number of different activities. The proposed method is one of the first information technology artefacts to bridge the boundaries between the complementing research disciplines of organisational routines and business processes management.
APA:
Weinzierl, S., Wolf, V., Pauli, T., Beverungen, D., & Matzner, M. (2022). Detecting temporal workarounds in business processes – A deep-learning-based method for analysing event log data. Journal of Business Analytics, 5(1), 76-100. https://dx.doi.org/10.1080/2573234X.2021.1978337
MLA:
Weinzierl, Sven, et al. "Detecting temporal workarounds in business processes – A deep-learning-based method for analysing event log data." Journal of Business Analytics 5.1 (2022): 76-100.
BibTeX: Download