1. ZHAW digitalcollection
  2. Life Sciences und Facility Management
  3. Publikationen
  4. Publikationen Life Sciences und Facility Management
Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-26314
Full metadata record
DC FieldValueLanguage
dc.contributor.authorHanne, Thomas-
dc.contributor.authorGachnang, Phillip-
dc.contributor.authorGatziu Grivas, Stella-
dc.contributor.authorKirecci, Ilyas-
dc.contributor.authorSchmitter, Paul-
dc.date.accessioned2022-12-09T11:03:14Z-
dc.date.available2022-12-09T11:03:14Z-
dc.date.issued2022-
dc.identifier.isbn978-1-4503-9639-4de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/26314-
dc.description.abstractIn 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.de_CH
dc.language.isoende_CH
dc.publisherAssociation for Computing Machineryde_CH
dc.rightshttp://creativecommons.org/licenses/by/4.0/de_CH
dc.subjectComputing methodologyde_CH
dc.subjectModeling and simulationde_CH
dc.subjectMachine learningde_CH
dc.subjectMachine learning algorithmde_CH
dc.subjectModel development and analysisde_CH
dc.subjectDigital maturityde_CH
dc.subjectDigital transformationde_CH
dc.subjectMaturity evaluationde_CH
dc.subjectModel validationde_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.titleArtificial intelligence and machine learning for maturity evaluation and model validationde_CH
dc.typeKonferenz: Paperde_CH
dcterms.typeTextde_CH
zhaw.departementLife Sciences und Facility Managementde_CH
zhaw.organisationalunitInstitut für Facility Management (IFM)de_CH
dc.identifier.doi10.1145/3556089.3556102de_CH
dc.identifier.doi10.21256/zhaw-26314-
zhaw.conference.details13th International Conference on E-business, Management and Economics (ICEME), Beijing, China, 16-18 July 2022de_CH
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.end260de_CH
zhaw.pages.start256de_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.title.proceedingsICEME 2022 Proceedingde_CH
zhaw.webfeedDigital Health Labde_CH
zhaw.webfeedHealth Research Hub (LSFM)de_CH
zhaw.webfeedHospitality & Service Managementde_CH
zhaw.webfeedZHAW digitalde_CH
zhaw.funding.zhawDC4HC – Digital Competence for Healthcarede_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
Appears in collections:Publikationen Life Sciences und Facility Management

Files in This Item:
File Description SizeFormat 
2022_Hanne-etal_AI-and-ML-for-maturity-evaluation-model-validation.pdf129.04 kBAdobe PDFThumbnail
View/Open
Show simple item record
Hanne, T., Gachnang, P., Gatziu Grivas, S., Kirecci, I., & Schmitter, P. (2022). Artificial intelligence and machine learning for maturity evaluation and model validation [Conference paper]. ICEME 2022 Proceeding, 256–260. https://doi.org/10.1145/3556089.3556102
Hanne, T. et al. (2022) ‘Artificial intelligence and machine learning for maturity evaluation and model validation’, in ICEME 2022 Proceeding. Association for Computing Machinery, pp. 256–260. Available at: https://doi.org/10.1145/3556089.3556102.
T. Hanne, P. Gachnang, S. Gatziu Grivas, I. Kirecci, and P. Schmitter, “Artificial intelligence and machine learning for maturity evaluation and model validation,” in ICEME 2022 Proceeding, 2022, pp. 256–260. doi: 10.1145/3556089.3556102.
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 Proceeding. Conference paper. Association for Computing Machinery. 2022. S. 256–260. ISBN 978-1-4503-9639-4
Hanne, Thomas, Phillip Gachnang, Stella Gatziu Grivas, Ilyas Kirecci, and Paul Schmitter. 2022. “Artificial Intelligence and Machine Learning for Maturity Evaluation and Model Validation.” Conference paper. In ICEME 2022 Proceeding, 256–60. Association for Computing Machinery. https://doi.org/10.1145/3556089.3556102.
Hanne, Thomas, et al. “Artificial Intelligence and Machine Learning for Maturity Evaluation and Model Validation.” ICEME 2022 Proceeding, Association for Computing Machinery, 2022, pp. 256–60, https://doi.org/10.1145/3556089.3556102.


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.