Abstract

The increasing volume of available data and the advances in analytics and artificial intelligence hold the potential for new business models also in offline-established organizations. To successfully implement a data-driven business model, it is crucial to understand the environment and the roles that need to be fulfilled by actors in the business model. This partner perspective is overlooked by current research on datadriven business models. In this paper, we present a structured literature review in which we identified 33 relevant publications. Based on this literature, we developed a framework consisting of eight roles and two attributes that can be assigned to actors as well as three classes of exchanged values between actors. Finally, we evaluated our framework through three cases from one automotive company collected via interviews in which we applied the framework to analyze data-driven business models for which our interviewees are responsible.

Highlights

  • The increasing volume of available data and the advances in analytics and artificial intelligence hold the potential for competitive advantage, business growth, and new business models

  • data-driven business models (DDBMs) rely on data as a key resource and apply data analytics techniques as key activities to discover insights from data and that are transformed into a data-based value proposition that supports customers in their decision-making process (Hartmann et al, 2016; Kühne and Böhmann, 2019; Schüritz et al, 2019)

  • We developed a framework with roles and attributes that can be assigned to actors and classes of values exchanged between actors in DDBMs

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Summary

Introduction

The increasing volume of available data and the advances in analytics and artificial intelligence hold the potential for competitive advantage, business growth, and new business models. Offline-established organizations are seeking new socalled data-driven business models (DDBMs) This innovation and transformation process is often challenging, as it requires new skills and capabilities (e.g., data science or IT infrastructure), deep relationships, and partner information ecosystems (Schüritz et al, 2017). DDBMs rely on data as a key resource and apply data analytics techniques as key activities to discover insights from data and that are transformed into a data-based value proposition that supports customers in their decision-making process (Hartmann et al, 2016; Kühne and Böhmann, 2019; Schüritz et al, 2019) Other researchers denote such models as »data-infused business models« (Schüritz and Satzger, 2016) or »data-driven services« (Azkan et al, 2020). Little attention has been paid to that in contemporary research on DDBMs

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