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 (18-24 June)
Conference or Workshop Item, Bibliographie
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.
| Item Type: | Conference or Workshop Item |
|---|---|
| Erschienen: | 2022 |
| Creators: | Vetter, Oliver A. ; Hoffmann, Felix ; Pumplun, Luisa ; Buxmann, Peter |
| Type of entry: | Bibliographie |
| Title: | What constitutes a machine-learning-driven business model? A taxonomy of B2B start-ups with machine learning at their core |
| Language: | English |
| Date: | June 2022 |
| Place of Publication: | Timisoara, Romania |
| Book Title: | ECIS 2022 Research Papers |
| Event Title: | 30th European Conference on Information Systems |
| Event Location: | Timisoara, Romania |
| Event Dates: | 18-24 June |
| URL / URN: | https://aisel.aisnet.org/ecis2022_rp/29/ |
| Corresponding Links: | |
| 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. |
| Divisions: | 01 Department of Law and Economics 16 Department of Mechanical Engineering 16 Department of Mechanical Engineering > Institute of Production Technology and Machine Tools (PTW) 16 Department of Mechanical Engineering > Institute of Production Technology and Machine Tools (PTW) > Management of Industrial Production 01 Department of Law and Economics > Betriebswirtschaftliche Fachgebiete 01 Department of Law and Economics > Betriebswirtschaftliche Fachgebiete > Information Systems 01 Department of Law and Economics > Betriebswirtschaftliche Fachgebiete > Fachgebiet Software Business & Information Management |
| Date Deposited: | 30 Jun 2022 10:14 |
| Last Modified: | 07 Jun 2024 10:11 |
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