What constitutes a machine-learning-driven business model? A taxonomy of B2B start-ups with machine learning at their core

Oliver A. Vetter, Felix Hoffmann, Luisa Pumplun and Peter Buxmann

Publications of Darmstadt Technical University, Institute for Business Studies (BWL) from Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL)

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.

Date: 2022-06
New Economics Papers: this item is included in nep-big and nep-cmp
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