Vetter, Oliver A. ; Hoffmann, Felix ; Pumplun, Luisa ; Buxmann, Peter (2022)
What constitutes a machine-learning-driven business model? A taxonomy of B2B start-ups with machine learning at their core.
30th European Conference on Information Systems. Timisoara, Romania (18.06.2022-24.06.2022)
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
Artificial intelligence, specifically machine learning (ML), technologies are powerfully driving business model innovation in organizations against the backdrop of increasing digitalization. The resulting novel business models are profoundly shaped by ML, a technology that brings about unique opportunities and challenges. However, to date, little research examines what exactly constitutes these business models that use ML at their core and how they can be distinguished. Therefore, this study aims to contribute to an increased understanding of the anatomy of ML-driven business models in the business-to-business segment. To this end, we develop a taxonomy that allows researchers and practitioners to differentiate these ML-driven business models according to their characteristics along ten dimensions. Additionally, we derive archetypes of ML-driven business models through a cluster analysis based on the characteristics of 102 start-ups from the database Crunchbase. Our results are cross-industry, providing fertile soil for expansion through future investigations.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2022 |
| Autor(en): | Vetter, Oliver A. ; Hoffmann, Felix ; Pumplun, Luisa ; Buxmann, Peter |
| Art des Eintrags: | Bibliographie |
| Titel: | What constitutes a machine-learning-driven business model? A taxonomy of B2B start-ups with machine learning at their core |
| Sprache: | Englisch |
| Publikationsjahr: | Juni 2022 |
| Ort: | Timisoara, Romania |
| Buchtitel: | ECIS 2022 Research Papers |
| Veranstaltungstitel: | 30th European Conference on Information Systems |
| Veranstaltungsort: | Timisoara, Romania |
| Veranstaltungsdatum: | 18.06.2022-24.06.2022 |
| URL / URN: | https://aisel.aisnet.org/ecis2022_rp/29/ |
| Zugehörige Links: | |
| Kurzbeschreibung (Abstract): | Artificial intelligence, specifically machine learning (ML), technologies are powerfully driving business model innovation in organizations against the backdrop of increasing digitalization. The resulting novel business models are profoundly shaped by ML, a technology that brings about unique opportunities and challenges. However, to date, little research examines what exactly constitutes these business models that use ML at their core and how they can be distinguished. Therefore, this study aims to contribute to an increased understanding of the anatomy of ML-driven business models in the business-to-business segment. To this end, we develop a taxonomy that allows researchers and practitioners to differentiate these ML-driven business models according to their characteristics along ten dimensions. Additionally, we derive archetypes of ML-driven business models through a cluster analysis based on the characteristics of 102 start-ups from the database Crunchbase. Our results are cross-industry, providing fertile soil for expansion through future investigations. |
| Fachbereich(e)/-gebiet(e): | 01 Fachbereich Rechts- und Wirtschaftswissenschaften 16 Fachbereich Maschinenbau 16 Fachbereich Maschinenbau > Institut für Produktionsmanagement und Werkzeugmaschinen (PTW) 16 Fachbereich Maschinenbau > Institut für Produktionsmanagement und Werkzeugmaschinen (PTW) > Management industrieller Produktion 01 Fachbereich Rechts- und Wirtschaftswissenschaften > Betriebswirtschaftliche Fachgebiete 01 Fachbereich Rechts- und Wirtschaftswissenschaften > Betriebswirtschaftliche Fachgebiete > Wirtschaftsinformatik 01 Fachbereich Rechts- und Wirtschaftswissenschaften > Betriebswirtschaftliche Fachgebiete > Fachgebiet Software Business & Information Management |
| Hinterlegungsdatum: | 30 Jun 2022 10:14 |
| Letzte Änderung: | 07 Jun 2024 10:11 |
| PPN: | |
| Export: | |
| Suche nach Titel in: | TUfind oder in Google |
 |
Frage zum Eintrag |
Optionen (nur für Redakteure)
 |
Redaktionelle Details anzeigen |
Drucken
Impressum