Weinzierl S, Bartelheimer C, Zilker S, Beverungen D, Matzner M (2022)
Publication Language: English
Publication Type: Conference contribution, Conference Contribution
Publication year: 2022
Publisher: AISeL
Pages Range: 1--9
Conference Proceedings Title: Proceedings of the 25th Pacific Asia Conference on Information Systems
Event location: Taipei-Sydney
Open Access Link: https://aisel.aisnet.org/pacis2022/108/
Workarounds are performed intentionally by employees to bypass obstacles constraining their day-to-day work. These obstacles manifest from latent misfits in the interplay of information systems, organizational structure, and human agency. While workarounds are often mandatory for employees to perform their work, they can yield positive and negative effects on an organization’s performance. Process managers are supposed to identify workarounds early, promoting their positive while reducing their negative consequences. While related research has touched upon detecting workarounds in event logs that include data on completed processes, little is known on how to predict workarounds in a running business process. We set out to design a workaround prediction method using a deep learning approach. The IT artifact enables process managers to proactively intervene if workarounds are about to emerge in a business process, reducing their adverse effects while supporting organizational learning and process innovation.
APA:
Weinzierl, S., Bartelheimer, C., Zilker, S., Beverungen, D., & Matzner, M. (2022). A method for predicting workarounds in business processes. In Proceedings of the 25th Pacific Asia Conference on Information Systems (pp. 1--9). Taipei-Sydney: AISeL.
MLA:
Weinzierl, Sven, et al. "A method for predicting workarounds in business processes." Proceedings of the Pacific Asia Conference on Information Systems, Taipei-Sydney AISeL, 2022. 1--9.
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