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A metric to characterize major innovation sequences and its application in three industrial sectors: from random emergence to waterfall phenomena

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  • Kenza El Qaoumi

    (CGS i3 - Centre de Gestion Scientifique i3 - Mines Paris - PSL (École nationale supérieure des mines de Paris) - PSL - Université Paris sciences et lettres - I3 - Institut interdisciplinaire de l’innovation - CNRS - Centre National de la Recherche Scientifique)

  • Pascal Le Masson

    (CGS i3 - Centre de Gestion Scientifique i3 - Mines Paris - PSL (École nationale supérieure des mines de Paris) - PSL - Université Paris sciences et lettres - I3 - Institut interdisciplinaire de l’innovation - CNRS - Centre National de la Recherche Scientifique)

  • Aytunç Ün

    (CGS i3 - Centre de Gestion Scientifique i3 - Mines Paris - PSL (École nationale supérieure des mines de Paris) - PSL - Université Paris sciences et lettres - I3 - Institut interdisciplinaire de l’innovation - CNRS - Centre National de la Recherche Scientifique)

  • Benoit Weil

    (CGS i3 - Centre de Gestion Scientifique i3 - Mines Paris - PSL (École nationale supérieure des mines de Paris) - PSL - Université Paris sciences et lettres - I3 - Institut interdisciplinaire de l’innovation - CNRS - Centre National de la Recherche Scientifique)

Abstract

Are Major innovations rare or frequent? Is there any relationship between major innovations? Do major innovations occur independently of the others? In order to answer these questions, we build a new tool of measuring major innovations sequences, based on Lancaster's approach to consumer theory. This new tool allows us to characterize major innovation sequences and its application in three industrial sectors (Mobile phone, Iron, Automobile). The main results of our empirical work show that Major Innovations (MI) are not rare and reveal the existence of a relationship - with a chain reaction effect- between successive major innovations. This article treats especially major innovations and it focuses on characterizing the sequences and the increasing rhythm of major innovations.

Suggested Citation

  • Kenza El Qaoumi & Pascal Le Masson & Aytunç Ün & Benoit Weil, 2013. "A metric to characterize major innovation sequences and its application in three industrial sectors: from random emergence to waterfall phenomena," Post-Print hal-00920984, HAL.
    • Handle: RePEc:hal:journl:hal-00920984
    Note: View the original document on HAL open archive server: https://minesparis-psl.hal.science/hal-00920984
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    1. Zvi Griliches, 1998. "Patent Statistics as Economic Indicators: A Survey," NBER Chapters, in: R&D and Productivity: The Econometric Evidence, pages 287-343, National Bureau of Economic Research, Inc.
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    Keywords

    Major Innovation; Metric; Learning Effect;
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