Artificial intelligence and machine learning for maturity evaluation and model validation
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Autor:innen
Autor:in (Körperschaft)
Publikationsdatum
2022
Typ der Arbeit
Studiengang
Typ
04B - Beitrag Konferenzschrift
Herausgeber:innen
Herausgeber:in (Körperschaft)
Betreuer:in
Übergeordnetes Werk
ICEME 2022. The 2022 13th International Conference on E-business, Management and Economics (ICEME 2022). Beijing, China (vurtual conference), July 16-18, 2022
Themenheft
DOI der Originalpublikation
Link
Reihe / Serie
Reihennummer
Jahrgang / Band
Ausgabe / Nummer
Seiten / Dauer
256-260
Patentnummer
Verlag / Herausgebende Institution
Verlagsort / Veranstaltungsort
Beijing
Auflage
Version
Programmiersprache
Abtretungsempfänger:in
Praxispartner:in/Auftraggeber:in
Zusammenfassung
In this paper, we discuss the possibility of using machine learning (ML) to specify and validate maturity models, in particular maturity models related to the assessment of digital capabilities of an organization. Over the last decade, a rather large number of maturity models have been suggested for different aspects (such as type of technology or considered processes) and in relation to different industries. Usually, these models are based on a number of assumptions such as the data used for the assessment, the mathematical formulation of the model and various parameters such as weights or importance indicators. Empirical evidence for such assumptions is usually lacking. We investigate the potential of using data from assessments over time and for similar institutions for the ML of respective models. Related concepts are worked out in some details and for some types of maturity assessment models, a possible application of the concept is discussed.
Schlagwörter
Fachgebiet (DDC)
330 - Wirtschaft
Veranstaltung
2022 13th International Conference on E-business, Management and Economics (ICEME 2022)
Startdatum der Ausstellung
Enddatum der Ausstellung
Startdatum der Konferenz
16.07.2022
Enddatum der Konferenz
18.07.2022
Datum der letzten Prüfung
ISBN
978-1-4503-9639-4
ISSN
Sprache
Englisch
Während FHNW Zugehörigkeit erstellt
Ja
Zukunftsfelder FHNW
Publikationsstatus
Veröffentlicht
Begutachtung
Peer-Review der ganzen Publikation
Open Access-Status
Closed
Lizenz
Zitation
HANNE, Thomas, Phillip GACHNANG, Stella GATZIU GRIVAS, Ilyas KIRECCI und Paul SCHMITTER, 2022. Artificial intelligence and machine learning for maturity evaluation and model validation. In: ICEME 2022. The 2022 13th International Conference on E-business, Management and Economics (ICEME 2022). Beijing, China (vurtual conference), July 16-18, 2022. Beijing. 2022. S. 256–260. ISBN 978-1-4503-9639-4. Verfügbar unter: https://irf.fhnw.ch/handle/11654/43411