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Value creation and appropriation from the use of machine learning: a study of start-ups using fuzzy-set qualitative comparative analysis

Author

Listed:
  • Ricardo Costa-Climent

    (Uppsala University
    University of Economics and Human Sciences)

  • Samuel Ribeiro Navarrete

    (University of Economics and Human Sciences
    Esic University Madrid)

  • Darek M. Haftor

    (Uppsala University)

  • Marcin W. Staniewski

    (University of Economics and Human Sciences)

Abstract

This study focuses on how start-ups use machine learning technology to create and appropriate value. A firm’s use of machine learning can activate data network effects. These data network effects can then create perceived value for users. This study examines the interaction between the activation of data network effects by start-ups and the value that they are able to create and appropriate based on their business model. A neo-configurational approach built on fuzzy-set qualitative comparative analysis (fsQCA) explores how the design of a firm’s business model interacts with various aspects to explain value creation and appropriation using machine learning. The study uses a sample of 122 European start-ups created between 2019 and 2022. It explores the system of interactions between business model value drivers and value creation factors under the theory of data network effects. The findings show that start-ups primarily activate the efficiency and novelty elements of value creation and value capture.

Suggested Citation

  • Ricardo Costa-Climent & Samuel Ribeiro Navarrete & Darek M. Haftor & Marcin W. Staniewski, 2024. "Value creation and appropriation from the use of machine learning: a study of start-ups using fuzzy-set qualitative comparative analysis," International Entrepreneurship and Management Journal, Springer, vol. 20(2), pages 935-967, June.
    • Handle: RePEc:spr:intemj:v:20:y:2024:i:2:d:10.1007_s11365-023-00922-w
    DOI: 10.1007/s11365-023-00922-w
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