Gatziu Grivas, Stella

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Gatziu Grivas
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Stella
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Gatziu Grivas, Stella

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2022 - 20232
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Autor:in: Gachnang, Phillip × Institut: Institut für Wirtschaftsinformatik ×

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Gerade angezeigt 1 - 2 von 2
  • Vorschaubild nicht verfügbar
    Publikation
    Position paper - Hybrid artificial intelligence for realizing a leadership assistant for platform-based leadership consulting
    (Springer, 2023) Gatziu Grivas, Stella; Imhof, Denis; Gachnang, Phillip; Soffer, Pnina; Ruiz, Marcela [in: Advanced information systems engineering workshops: CAiSE 2023 international workshops, Zaragoza, Spain, June 12-16, 2023, proceedings]
    Digital technologies enable new forms of value creation, value proposition, and value capturing for all kinds of organizations in all kinds of industries. Often, companies strive to digital transform and obtain consultancy services due to missing expert knowledge on how to approach the transformation. Interestingly, research shows that the consulting industry itself shows a high potential for a digital transformation, but platform-based consulting models and self-service consulting models are still underdeveloped. With this position paper, the authors propose an own approach on how to integrate human expert knowledge and machine learning in a novel hybrid artificial intelligence and platform-based consulting model, which not only offers the potential to transform the consultancy industry but also supports organizations in their transformation efforts. The authors take the area of digital leadership consulting to illustrate this.
    04A - Beitrag Sammelband
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    Publikation
    Artificial intelligence and machine learning for maturity evaluation and model validation
    (2022) Hanne, Thomas; Gachnang, Phillip; Gatziu Grivas, Stella; Kirecci, Ilyas; Schmitter, Paul [in: ICEME 2022. The 2022 13th International Conference on E-business, Management and Economics (ICEME 2022). Beijing, China (vurtual conference), July 16-18, 2022]
    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.
    04B - Beitrag Konferenzschrift