New publications: ECIS 2020
This year we are represented with three contributions at the European Conference on Information Systems (ECIS), one of the most important international conferences in business informatics. Congratulations to all authors.
You can access the contributions via the image links.
Leveraging Industrial IoT Platform Ecosystems: Insights from the Complementors’ Perspective
Industrial IoT platforms are currently a major focus of industrial firms, as they shift more towards service and platform-based business models. Value creation on these platforms relies heavily on an ecosystem of partners and complementors. In a recent study, Tobias Pauli, Emanuel Marx, and Martin Matzner shed light on how complementors leverage IIoT platform ecosystems in different ways and provide guidance on how to design successful IIoT platform business models.
The results of this study are available in a paper recently accepted for publication at the European Conference on Information Systems.
Detecting Workarounds in Business Processes — A Deep Learning Method for Analyzing Event Logs
Routines or processes are often not executed by employees in companies as originally defined. Instead, they often use workarounds to perform their tasks more effectively and efficiently. Since workarounds can have both positive and negative consequences, they need to be identified and addressed. While workarounds have so far primarily been detected using methods such as interviews or observations, Sven Weinzierl, Verena Wolf, Tobias Pauli, Daniel Beverungen, and Martin Matzner have developed a method for detecting workarounds in event logs using Deep Learning in their article. In doing so, they provide organizational researchers with an important tool from the field of business process management and build a bridge between the two disciplines.
Design Principles for Comprehensible Process Discovery in Process Mining
“Spaghetti-like” process models, which are uncovered by process mining, contain a lot of information for the decision-maker, but on the other hand, are often difficult to understand. Developing techniques for process discovery that take this trade-off into account is an existing problem in process mining. Therefore, in the conference paper the authors Matthias Stierle, Sandra Zilker, Sebastian Dunzer, Johannes Tenschert, and Gergana Karagegova derive different design principles based on metrics of different research strands, introduce an entropy-based metric as a boundary condition and demonstrate its applicability with an experimental evaluation.