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Evaluation of tech ventures' evolving business models: rules for performance-related classification

Author

Listed:
  • Marc König

    (KIT - Karlsruhe Institute of Technology = Karlsruher Institut für Technologie)

  • Manon Enjolras

    (ERPI - Equipe de Recherche sur les Processus Innovatifs - UL - Université de Lorraine)

  • Christina Ungerer

    (Konstanz University of Applied Sciences, IST Institute for Strategic Innovation and Technology Management)

  • Mauricio Camargo

    (ERPI - Equipe de Recherche sur les Processus Innovatifs - UL - Université de Lorraine)

  • Guido Baltes

    (Konstanz University of Applied Sciences, IST Institute for Strategic Innovation and Technology Management)

Abstract

At the early stage of a successful tech venture's life cycle, it is assumed that the business model will evolve to higher quality over time. However, there are few empirical insights into business model evolution patterns for the performance-related classification of early-stage tech ventures. We created relevant variables evaluating the evolution of the venture-centric network and the technological proposition of both digital and non-digital ventures' business models using the text of submissions to the official business plan award in the German State of Baden-Württemberg between 2006 and 2012. Applying a principal component analysis/rough set theory mixed methodology, we explore performance-related business model classification rules in the heterogeneous sample of business plans. We find that ventures need to demonstrate real interactions with their customers' needs to survive. The distinguishing success rules are related to patent applications, risk capital, and scaling of the organisation. The rules help practitioners to classify business models in a way that allows them to prioritise action for performance.

Suggested Citation

  • Marc König & Manon Enjolras & Christina Ungerer & Mauricio Camargo & Guido Baltes, 2022. "Evaluation of tech ventures' evolving business models: rules for performance-related classification," Post-Print hal-03685241, HAL.
    • Handle: RePEc:hal:journl:hal-03685241
    DOI: 10.1504/IJEV.2022.122639
    Note: View the original document on HAL open archive server: https://hal.science/hal-03685241
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    References listed on IDEAS

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    Keywords

    network theory; business model; life cycle; RST; rough set theory; PCA; principal component analysis; tech ventures;
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