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|Publication type:||Conference paper|
|Type of review:||Peer review (publication)|
|Title:||Artificial intelligence and machine learning for maturity evaluation and model validation|
Gatziu Grivas, Stella
|Proceedings:||ICEME 2022 Proceeding|
|Conference details:||13th International Conference on E-business, Management and Economics (ICEME), Beijing, China, 16-18 July 2022|
|Publisher / Ed. Institution:||Association for Computing Machinery|
|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.|
|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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|2022_Hanne-etal_AI-and-ML-for-maturity-evaluation-model-validation.pdf||129.04 kB||Adobe PDF|
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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, 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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