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A pathway to bypassing market entry barriers from data network effects: A case study of a start-up’s use of machine learning

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  • Haftor, Darek M.
  • Costa-Climent, Ricardo
  • Ribeiro Navarrete, Samuel

Abstract

Highly valued firms exploit machine learning to activate data network effects. Data is gathered and analyzed to generate predictions and recommendations. This loop locks in existing service users and locks out potential competitors, thus creating a sizeable entry barrier, particularly for small and medium-sized (SME) enterprises. The literature does not describe the possible pathways to enter markets protected by incumbents’ data network effects. This study examines an SME that successfully entered such a market. A key finding is that, for successful market entry, an SME can focus on different stakeholders from those that are targeted by incumbents, provided such stakeholders can legitimize the SME’s use of user data generated by incumbents.

Suggested Citation

  • Haftor, Darek M. & Costa-Climent, Ricardo & Ribeiro Navarrete, Samuel, 2023. "A pathway to bypassing market entry barriers from data network effects: A case study of a start-up’s use of machine learning," Journal of Business Research, Elsevier, vol. 168(C).
    • Handle: RePEc:eee:jbrese:v:168:y:2023:i:c:s0148296323006033
    DOI: 10.1016/j.jbusres.2023.114244
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    References listed on IDEAS

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    1. Jean-Charles Rochet & Jean Tirole, 2003. "Platform Competition in Two-Sided Markets," Journal of the European Economic Association, MIT Press, vol. 1(4), pages 990-1029, June.
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    Cited by:

    1. Lin Wu & Jimmy Huang & Miao Wang & Ajay Kumar, 2024. "Unleashing supply chain agility : Leveraging data network effects for digital transformation," Post-Print hal-04850421, HAL.
    2. Wu, Lin & Huang, Jimmy & Wang, Miao & Kumar, Ajay, 2024. "Unleashing supply chain agility: Leveraging data network effects for digital transformation," International Journal of Production Economics, Elsevier, vol. 277(C).

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