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How the application of machine learning systems changes business processes: A multiple case study
Kunz, Pascal Christoph
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Jussupow, Ekaterina
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Spohrer, Kai
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Heinzl, Armin
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URL:
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https://aisel.aisnet.org/ecis2022_rip/30/
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Dokumenttyp:
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Konferenzveröffentlichung
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Erscheinungsjahr:
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2022
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Buchtitel:
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Proceedings of the 30th European Conference on Information Systems (ECIS): Timișoara, Romania, June 18-24, 2022
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Titel einer Zeitschrift oder einer Reihe:
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ECIS Research-in-Progress Papers
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Band/Volume:
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2022, 30
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Seitenbereich:
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1-11
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Veranstaltungstitel:
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ECIS 2022
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Veranstaltungsort:
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Timișoara, Romania
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Veranstaltungsdatum:
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18.-24.06.2022
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Herausgeber:
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Beck, Roman
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Petcu, Dana
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Fotache, Marin
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Ort der Veröffentlichung:
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Atlanta, GA
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Verlag:
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AISeL
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ISBN:
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978-1-958200-02-5
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Sprache der Veröffentlichung:
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Englisch
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Einrichtung:
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Fakultät für Betriebswirtschaftslehre > ABWL u. Wirtschaftsinformatik I (Heinzl 2002-)
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Fachgebiet:
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004 Informatik
330 Wirtschaft
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Freie Schlagwörter (Englisch):
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machine learning , artificial intelligence , process change , process performance , future of work , business value of IT , IS value , exploratory case study , multiple case study
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Abstract:
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Machine Learning (ML) systems are applied in organizations to substitute or complement human knowledge work. Although organizations invest heavily in ML, the resulting business benefits often remain unclear. To explain the impact of ML systems, it is necessary to understand how their application changes business processes and affects process performance. In our exploratory multiple case study, we analyze the application of multiple productive ML systems in one organization to (1.) describe how activity composition, allocation, and sequence change in ML-supported processes; (2.) distinguish how the applied ML system type and task characteristics influence process changes; and (3.) explain how process efficiency and quality are affected. As a result, we develop three preliminary change patterns: Lift & Shift, Divide & Conquer, and Expand & Intensify. Our research aims to contribute to the future of work and IS value literature by connecting the emerging knowledge on ML systems to their process-level implications.
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 | Dieser Eintrag ist Teil der Universitätsbibliographie. |
 Suche Autoren in
BASE:
Kunz, Pascal Christoph
;
Jussupow, Ekaterina
;
Spohrer, Kai
;
Heinzl, Armin
Google Scholar:
Kunz, Pascal Christoph
;
Jussupow, Ekaterina
;
Spohrer, Kai
;
Heinzl, Armin
ORCID:
Kunz, Pascal Christoph ; Jussupow, Ekaterina  ORCID: 0000-0002-3009-076X ; Spohrer, Kai ORCID: 0000-0001-8659-7554 ; Heinzl, Armin
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