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Towards a Unified Approach to Identify Business Model Patterns: A Case of E-Mobility Services

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Exploring Services Science (IESS 2017)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 279))

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Abstract

The introduction of new technologies creates the pursuit for innovative business models. In order to compare and evaluate such models in new markets, business model patterns can support the provision of new insights. However, so far there is no agreed upon transparent approach that helps to identify these patterns. Based on a combination of established statistical methods we propose a systematical approach that allows to identify business model patterns of any given domain. In order to validate the approach, we apply it on a data set of 58 e-mobility projects and as a result identify five distinct and semantically meaningful business models types. This paper contributes on the one hand by suggesting a new approach to identify different patterns of business models and on the other hand provides a valuable insight of the current state of e-mobility service business models that can further drive the adoption.

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Authors and Affiliations

  1. Karlsruhe Service Research Institute (KSRI), Karlsruhe Institute of Technology (KIT), Kaiserstr. 89, 76133, Karlsruhe, Germany

    Fabian Hunke, Ronny Schüritz & Niklas Kuehl

Authors
  1. Fabian Hunke
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  2. Ronny Schüritz
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  3. Niklas Kuehl
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Corresponding author

Correspondence to Fabian Hunke .

Editor information

Editors and Affiliations

  1. Faculty of Economics, eCampus University, Rome, Italy

    Stefano Za

  2. Department of Automatic Control and System Engineering, University Politehnica of Bucharest, Bucharest, Romania

    Monica Drăgoicea

  3. Università Cattolica del Sacro Cuore, Milan, Italy

    Maurizio Cavallari

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Hunke, F., Schüritz, R., Kuehl, N. (2017). Towards a Unified Approach to Identify Business Model Patterns: A Case of E-Mobility Services. In: Za, S., Drăgoicea, M., Cavallari, M. (eds) Exploring Services Science. IESS 2017. Lecture Notes in Business Information Processing, vol 279. Springer, Cham. https://doi.org/10.1007/978-3-319-56925-3_15

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  • DOI: https://doi.org/10.1007/978-3-319-56925-3_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-56924-6

  • Online ISBN: 978-3-319-56925-3

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