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

Sterk, Felix 1; Stocker, Alexander; Heinz, Daniel ORCID iD icon 2; Weinhardt, Christof ORCID iD icon 1
1 Institut für Wirtschaftsinformatik und Marketing (IISM), Karlsruher Institut für Technologie (KIT)
2 Karlsruhe Service Research Institute (KSRI), Karlsruher Institut für Technologie (KIT)

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.


Verlagsausgabe §
DOI: 10.5445/IR/1000168853
Veröffentlicht am 04.03.2024
Originalveröffentlichung
DOI: 10.1007/s12525-024-00692-5
Scopus
Zitationen: 3
Web of Science
Zitationen: 1
Dimensions
Zitationen: 4
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Wirtschaftsinformatik und Marketing (IISM)
Karlsruhe Service Research Institute (KSRI)
Publikationstyp Zeitschriftenaufsatz
Publikationsmonat/-jahr 12.2024
Sprache Englisch
Identifikator ISSN: 1019-6781, 1422-8890
KITopen-ID: 1000168853
Erschienen in Electronic Markets
Verlag Springer
Band 34
Heft 1
Seiten 13
Projektinformation bi.smart; TP (BMBF, 02J19B041)
Vorab online veröffentlicht am 13.02.2024
Schlagwörter Business models, Connected cars, Data monetization, Taxonomy, Archetypes, Cluster analysis
Nachgewiesen in Scopus
Dimensions
Web of Science
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