Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-26314
Publication type: Conference paper
Type of review: Peer review (publication)
Title: Artificial intelligence and machine learning for maturity evaluation and model validation
Authors: Hanne, Thomas
Gachnang, Phillip
Gatziu Grivas, Stella
Kirecci, Ilyas
Schmitter, Paul
et. al: No
DOI: 10.1145/3556089.3556102
10.21256/zhaw-26314
Proceedings: ICEME 2022 Proceeding
Page(s): 256
Pages to: 260
Conference details: 13th International Conference on E-business, Management and Economics (ICEME), Beijing, China, 16-18 July 2022
Issue Date: 2022
Publisher / Ed. Institution: Association for Computing Machinery
ISBN: 978-1-4503-9639-4
Language: English
Subjects: Computing methodology; Modeling and simulation; Machine learning; Machine learning algorithm; Model development and analysis; Digital maturity; Digital transformation; Maturity evaluation; Model validation
Subject (DDC): 006: Special computer methods
Abstract: 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.
URI: https://digitalcollection.zhaw.ch/handle/11475/26314
Fulltext version: Published version
License (according to publishing contract): CC BY 4.0: Attribution 4.0 International
Departement: Life Sciences and Facility Management
Organisational Unit: Institute of Facility Management (IFM)
Published as part of the ZHAW project: DC4HC – Digital Competence for Healthcare
Appears in collections:Publikationen Life Sciences und Facility Management

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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.


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