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Description
Data monetization has proven to be one of the most viable profit pools across industries. As vehicles become increasingly connected, leveraging their collected data through novel business models is the most promising value driver for automotive enterprises. Despite the increasing practical relevance, theoretical and conceptual insights on connected cars and their associated business models are still scarce. Thus, we develop a taxonomy of data-driven business models in theconnected car domain according to four perspectives—value proposition, value architecture, value network, and value finance. Further, we apply the taxonomy to analyze the business model of 70 companies acting under the realm of connected cars. A subsequent evaluation indicates both the robustness and general feasibility of our taxonomy. Our taxonomy contributes to descriptive knowledge in this emerging field and enables researchers and practitioners to analyze, design, andconfigure data-driven business models for connected cars.
Recommended Citation
Sterk, Felix; Peukert, Christian; Hunke, Fabian; and Weinhardt, Christof, "Understanding Car Data Monetization: A Taxonomy of Data-Driven Business Models in the Connected Car Domain" (2022). Wirtschaftsinformatik 2022 Proceedings. 7.
https://aisel.aisnet.org/wi2022/digital_business_models/digital_business_models/7
Understanding Car Data Monetization: A Taxonomy of Data-Driven Business Models in the Connected Car Domain
Data monetization has proven to be one of the most viable profit pools across industries. As vehicles become increasingly connected, leveraging their collected data through novel business models is the most promising value driver for automotive enterprises. Despite the increasing practical relevance, theoretical and conceptual insights on connected cars and their associated business models are still scarce. Thus, we develop a taxonomy of data-driven business models in theconnected car domain according to four perspectives—value proposition, value architecture, value network, and value finance. Further, we apply the taxonomy to analyze the business model of 70 companies acting under the realm of connected cars. A subsequent evaluation indicates both the robustness and general feasibility of our taxonomy. Our taxonomy contributes to descriptive knowledge in this emerging field and enables researchers and practitioners to analyze, design, andconfigure data-driven business models for connected cars.