Unlocking the value from car data: A taxonomy and archetypes of connected car business models

Felix Sterk (), Alexander Stocker, Daniel Heinz and Christof Weinhardt
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Felix Sterk: Institute of Information Systems and Marketing (IISM) at the Karlsruhe Institute of Technology (KIT)
Alexander Stocker: Virtual Vehicle Research GmbH
Daniel Heinz: Institute of Information Systems and Marketing (IISM) at the Karlsruhe Institute of Technology (KIT)
Christof Weinhardt: Institute of Information Systems and Marketing (IISM) at the Karlsruhe Institute of Technology (KIT)

Electronic Markets, 2024, vol. 34, issue 1, No 13, 24 pages

Abstract: Abstract The automotive industry is relocating from viewing cars as standalone products to an all-encompassing ecosystem built around connected cars enabling data-driven business models. The vastly increasing amount of data collected by connected cars grants a unique driving experience for its users while providing companies operating in the automotive industry access to valuable information and, ultimately, cost and revenue benefits. In this article, we develop an empirically and theoretically grounded taxonomy of data-driven business models in the connected car domain to explore the impact of car connectivity and data availability on business models. Building on this, we conduct a cluster analysis revealing seven business model archetypes for the connected car domain: data platforms, location-based services, fleet management, diagnostics and maintenance, driving analytics, cyber-physical protection, and connected infotainment. Our findings advance the theoretical knowledge of data-driven business models, provide researchers with a systematic analysis of connected car-enabled business models, and enable decision-makers to identify strategic opportunities for leveraging connected car technology to enrich their business portfolios.

Keywords: Business models; Connected cars; Data monetization; Taxonomy; Archetypes; Cluster analysis (search for similar items in EconPapers)
JEL-codes: L8 O3 (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s12525-024-00692-5

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