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Lehrstuhl für Digital Industrial Service Systems
Friedrich-Alexander-Universität Erlangen-Nürnberg
Rechts- und Wirtschaftswissenschaftliche Fakultät
Fachbereich Wirtschaftswissenschaften
Overview
Publications
(189)
Research Grants
(30)
Research Fields
(5)
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Journal article
Journal article
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Book chapter / Article in edited volumes
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Guiding text-to-text privatization by syntax (2023)
Arnold S, Yesilbas D, Weinzierl S
Conference contribution, Conference Contribution
Driving context into text-to-text privatization (2023)
Arnold S, Yesilbas D, Weinzierl S
Conference contribution, Conference Contribution
Kollaborative datengetriebene Geschäftsmodelle: Die Gestaltung datengetriebener Geschäftsmodelle in der Automobilindustrie (2023)
Bellin P, Homner N, Matzner M
Conference contribution, Abstract of a poster
Herausforderungen von KMU bei der Wertschöpfung auf B2B-Plattformen: Ein Scoping Review (2023)
Bellin P, Nowak A, Vu DA, Pham J, Matzner M
Conference contribution, Conference Contribution
On the Disappearance of the Metaverse: Three Scenarios for the Future (2023)
Brechtelsbauer B, Tang W
Conference contribution, Conference Contribution
Does IT at Work have to $#!& or How Can We Create Better IT Experiences?: A Call to Study Digital Employee Experiences (2023)
Tang W, Brechtelsbauer B
Conference contribution, Conference Contribution
Design Principles for Using Business Process Management Systems (2023)
Dunzer S, Tang W, Höchstädter N, Zilker S, Matzner M
Conference contribution, Conference Contribution
Predictive Recommining: Learning relations between event log characteristics and machine learning approaches for supporting predictive process monitoring (2023)
Drodt C, Weinzierl S, Matzner M, Delfmann P
Conference contribution, Conference Contribution
Best of both worlds: Combining predictive power with interpretable and explainable results for patient pathway prediction (2023)
Zilker S, Weinzierl S, Zschech P, Kraus M, Matzner M
Conference contribution, Conference Contribution
Detecting temporal workarounds in business processes – A deep-learning-based method for analysing event log data (2022)
Weinzierl S, Wolf V, Pauli T, Beverungen D, Matzner M
Journal article, Original article
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