Daniel Schömer, M.Sc.
CV
Daniel Schömer studied Industrial Engineering and Management (International Business and Technology) at the Nuremberg University of Technology Georg Simon Ohm from 2013 to 2017 and graduated with a Bachelor of Engineering in 2017. He is currently studying for a Master’s degree in International Information Systems at the Friedrich-Alexander University Erlangen-Nuremberg, which he will graduate this summer.
Already during his training and work as an administrative assistant, as well as during his studies, he gained extensive practical experience in Germany and abroad at various renowned companies, including BMW, GfK and Apple. Most recently, he worked as a student assistant at the Schöller Endowed Chair for Information Systems.
From 01.04.2020, he will be responsible for the ADVICE project funded by the Volkswagen Foundation as a part-time research assistant.
2023
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Navigating AI innovation ecosystems in manufacturing: Shaping factors and their implications
34th Australasian Conference on Information Systems, ACIS 2023 (Wellington, NZL, 5. December 2023 - 8. December 2023)
In: Australasian Conference on Information Systems, ACIS 2023 2023
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Survival of the Fittest: A Business Model Perspective to explain Innovation Ecosystem Membership
URL: https://aisel.aisnet.org/amcis2023/sig_scuidt/sig_scuidt/1/
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2021
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How can artificial intelligence enhance car manufacturing? A Delphi study-based identification and assessment of general use cases
In: International Journal of Information Management 58 (2021), p. 1-14
ISSN: 0268-4012
DOI: 10.1016/j.ijinfomgt.2021.102317
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If you go for AI, be aware of the psychological hurdles around it—Practical and theoretical insights on the industrial application of artificial intelligence
In: Kai-Ingo Voigt; Julian M. Müller (ed.): Digital Business Models in Industrial Ecosystems - Lessons Learned from Industry 4.0 Across Europe, 2021, p. 173-185 (Future of Business and Finance)
DOI: 10.1007/978-3-030-82003-9_11
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Data-driven Student Advisory and Potential Direct Discrimination: A Literature Review on Machine Learning for Predicting Students' Academic Success
Americas Conference on Information Systems (Montreal)
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Give a Little, Take a Little? A Factorial Survey Experiment on Students’ Willingness to Use an AI-based Advisory System and to Share Data
In: T. Wolbring, H. Leitgöb, & F. Faulbaum (ed.): Sozialwissenschaftliche Datenerhebung im digitalen Zeitalter, Wiesbaden: Springer VS, 2021, p. 253-281
ISBN: 978-3-658-34396-5
DOI: 10.1007/978-3-658-34396-5_10
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