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Open or proprietary ? Choosing the right crowdsourcing platform for innovation

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  • Eric Schenk

    (BETA - Bureau d'Économie Théorique et Appliquée - INRA - Institut National de la Recherche Agronomique - UNISTRA - Université de Strasbourg - UL - Université de Lorraine - CNRS - Centre National de la Recherche Scientifique, INSA - Institut National des Sciences Appliquées)

  • Claude Guittard

    (BETA - Bureau d'Économie Théorique et Appliquée - INRA - Institut National de la Recherche Agronomique - UNISTRA - Université de Strasbourg - UL - Université de Lorraine - CNRS - Centre National de la Recherche Scientifique)

  • Julien Pénin

    (BETA - Bureau d'Économie Théorique et Appliquée - INRA - Institut National de la Recherche Agronomique - UNISTRA - Université de Strasbourg - UL - Université de Lorraine - CNRS - Centre National de la Recherche Scientifique)

Abstract

Numerous reports have been published concerning the conditions under which a firm should rely on crowdsourcing for its innovation processes. This choice is the first order problem of crowdsourcing. Yet, once the decision to crowdsource has been made, the seeker must still decide whether to proceed internally with a proprietary platform, or to rely on the use of an open, external platform. This is the second order problem of crowdsourcing for which a theoretical analysis is offered in the present study. The conditions under which the use of an open, intermediary platform can be considered more appropriate than an exclusive, proprietary platform are analyzed. In particular, the role of transaction costs, network externalities and internal competencies are examined. Following this analysis, a decision-making model is proposed.

Suggested Citation

  • Eric Schenk & Claude Guittard & Julien Pénin, 2019. "Open or proprietary ? Choosing the right crowdsourcing platform for innovation," Post-Print hal-02286165, HAL.
  • Handle: RePEc:hal:journl:hal-02286165
    DOI: 10.1016/j.techfore.2017.11.021
    Note: View the original document on HAL open archive server: https://hal.univ-lorraine.fr/hal-02286165
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    3. Feng, Yuanyue & Yi, Zihui & Yang, Congcong & Chen, Ruoyi & Feng, Ye, 2022. "How do gamification mechanics drive solvers’ Knowledge contribution? A study of collaborative knowledge crowdsourcing," Technological Forecasting and Social Change, Elsevier, vol. 177(C).
    4. Troise, Ciro & Tani, Mario & Dinsmore, John & Schiuma, Giovanni, 2021. "Understanding the implications of equity crowdfunding on sustainability-oriented innovation and changes in agri-food systems: Insights into an open innovation approach," Technological Forecasting and Social Change, Elsevier, vol. 171(C).
    5. Lai, Xiaobing & Yue, Shujing & Guo, Chong & Zhang, Xinhe, 2023. "Does FinTech reduce corporate excess leverage? Evidence from China," Economic Analysis and Policy, Elsevier, vol. 77(C), pages 281-299.
    6. Albats, Ekaterina & Alexander, Allen T. & Cunningham, James A., 2022. "Traditional, virtual, and digital intermediaries in university-industry collaboration: exploring institutional logics and bounded rationality," Technological Forecasting and Social Change, Elsevier, vol. 177(C).

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