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Are big data a radical innovation trigger or a problem-solving patch? The case of AI implementation by automotive incumbents

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  • Quentin Plantec

    (TSM - Toulouse School of Management Research - UT Capitole - Université Toulouse Capitole - UT - Université de Toulouse - CNRS - Centre National de la Recherche Scientifique - TSM - Toulouse School of Management - UT Capitole - Université Toulouse Capitole - UT - Université de Toulouse)

  • Marie-Alix Deval

    (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)

  • Sophie Hooge

    (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)

  • Benoît 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

Big data, supported by AI technologies, is mainly viewed as a trigger for radical innovation. The automotive industry appears as a key example: the most critical innovative challenges (e.g., autonomous driving, connected cars) imply drawing more extensively on big data. But the degree of innovativeness of the industrial purpose of incumbents, who are already embedding such technologies in their end-products, is worth investigating. To answer this research question, we relied on a mixed-method approach and used knowledge search as a theoretical framework. First, we conducted a quantitative analysis on 46,145 patents from the top-19 automotive incumbents. By comparing AI and non-AI patents, we showed that incumbents mainly rely on knowledge exploitation for data-driven innovation leading to incremental innovations. But, surprisingly, such innovation path foster more technologically original inventions with AI, which is not the case for non-AI patents. Second, we conducted a qualitative study to better understand this phenomenon. We showed that big data and AI technologies are integrated in the industrialization phase of new vehicles development process, following creative problem-solving logics. We also retrieved technical and organizational challenges limiting data-driven innovation. Those findings are discussed regarding the knowledge search and the new product development literature in the context of automotive industry.

Suggested Citation

  • Quentin Plantec & Marie-Alix Deval & Sophie Hooge & Benoît Weil, 2022. "Are big data a radical innovation trigger or a problem-solving patch? The case of AI implementation by automotive incumbents," Post-Print hal-03727359, HAL.
    • Handle: RePEc:hal:journl:hal-03727359
    Note: View the original document on HAL open archive server: https://minesparis-psl.hal.science/hal-03727359v1
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    References listed on IDEAS

    as
    1. Plantec, Quentin & Le Masson, Pascal & Weil, Benoît, 2021. "Impact of knowledge search practices on the originality of inventions: A study in the oil & gas industry through dynamic patent analysis," Technological Forecasting and Social Change, Elsevier, vol. 168(C).
    2. Quentin Plantec & Pascal Le Masson & Benoît Weil, 2021. "Impact of knowledge search practices on the originality of inventions: A study in the oil & gas industry through dynamic patent analysis," Post-Print halshs-03203124, HAL.
    3. Robert M. Grant, 1996. "Prospering in Dynamically-Competitive Environments: Organizational Capability as Knowledge Integration," Organization Science, INFORMS, vol. 7(4), pages 375-387, August.
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    5. Sai Yayavaram & Wei-Ru Chen, 2015. "Changes in firm knowledge couplings and firm innovation performance: The moderating role of technological complexity," Strategic Management Journal, Wiley Blackwell, vol. 36(3), pages 377-396, March.
    6. Lee Fleming, 2001. "Recombinant Uncertainty in Technological Search," Management Science, INFORMS, vol. 47(1), pages 117-132, January.
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    Keywords

    Big data; AI technologies; automotive industry; digital transformation;
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