IDEAS home Printed from https://ideas.repec.org/p/hal/journl/hal-03727359.html
   My bibliography  Save this paper

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://minesparis-psl.hal.science/hal-03727359v1/document
    Download Restriction: no
    ---><---

    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.
    4. Theodoros A. Skouras & Panagiotis K. Gkonis & Charalampos N. Ilias & Panagiotis T. Trakadas & Eleftherios G. Tsampasis & Theodore V. Zahariadis, 2019. "Electrical Vehicles: Current State of the Art, Future Challenges, and Perspectives," Clean Technol., MDPI, vol. 2(1), pages 1-16, December.
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Plantec, Quentin & Deval, Marie-Alix & Hooge, Sophie & Weil, Benoit, 2023. "Big data as an exploration trigger or problem-solving patch: Design and integration of AI-embedded systems in the automotive industry," Technovation, Elsevier, vol. 124(C).
    2. Gnekpe, Christian & Plantec, Quentin, 2023. "Regulatory push-pull and technological knowledge dynamics of circular economy innovation," Technological Forecasting and Social Change, Elsevier, vol. 196(C).
    3. Xu, Luyun & Zeng, Deming, 2021. "When does the diverse partnership of R&D alliances promote new product development? The contingent effect of the knowledge base," Technology in Society, Elsevier, vol. 65(C).
    4. Naeini, Ali Bonyadi & Zamani, Mehdi & Daim, Tugrul U. & Sharma, Mahak & Yalcin, Haydar, 2022. "Conceptual structure and perspectives on “innovation management”: A bibliometric review," Technological Forecasting and Social Change, Elsevier, vol. 185(C).
    5. 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).
    6. Rifat Kamasak & Meltem Yavuz & Gultekin Altuntas, 2016. "Is the relationship between innovation performance and knowledge management contingent on environmental dynamism and learning capability? Evidence from a turbulent market," Business Research, Springer;German Academic Association for Business Research, vol. 9(2), pages 229-253, August.
    7. Giuliani, Elisa & Martinelli, Arianna & Rabellotti, Roberta, 2016. "Is Co-Invention Expediting Technological Catch Up? A Study of Collaboration between Emerging Country Firms and EU Inventors," World Development, Elsevier, vol. 77(C), pages 192-205.
    8. Rana, Mohammad B. & Elo, Maria, 2017. "Transnational Diaspora and Civil Society Actors Driving MNE Internationalisation: The Case of Grameenphone in Bangladesh," Journal of International Management, Elsevier, vol. 23(1), pages 87-106.
    9. Yildiz, H. Emre & Murtic, Adis & Zander, Udo, 2024. "Re-conceptualizing absorptive capacity: The importance of teams as a meso-level context," Technological Forecasting and Social Change, Elsevier, vol. 199(C).
    10. Robert P. Garrett Jr. & Jeffrey G. Covin, 2015. "Internal Corporate Venture Operations Independence and Performance: A Knowledge–Based Perspective," Entrepreneurship Theory and Practice, , vol. 39(4), pages 763-790, July.
    11. Sirkka L. Jarvenpaa & Ann Majchrzak, 2010. "Research Commentary ---Vigilant Interaction in Knowledge Collaboration: Challenges of Online User Participation Under Ambivalence," Information Systems Research, INFORMS, vol. 21(4), pages 773-784, December.
    12. Groza, Mark D. & Groza, Mya Pronschinske, 2018. "Salesperson regulatory knowledge and sales performance," Journal of Business Research, Elsevier, vol. 89(C), pages 37-46.
    13. Dominik M. Wielgos & Christian Homburg & Christina Kuehnl, 2021. "Digital business capability: its impact on firm and customer performance," Journal of the Academy of Marketing Science, Springer, vol. 49(4), pages 762-789, July.
    14. Ahammad, Mohammad Faisal & Tarba, Shlomo Yedidia & Liu, Yipeng & Glaister, Keith W., 2016. "Knowledge transfer and cross-border acquisition performance: The impact of cultural distance and employee retention," International Business Review, Elsevier, vol. 25(1), pages 66-75.
    15. Kazadi, Kande & Lievens, Annouk & Mahr, Dominik, 2016. "Stakeholder co-creation during the innovation process: Identifying capabilities for knowledge creation among multiple stakeholders," Journal of Business Research, Elsevier, vol. 69(2), pages 525-540.
    16. Izabela Kowalik & Lidia Danik & Petr Král & Hana Řezanková, 2017. "Antecedents of Accelerated Internationalisation of Polish and Czech Small and Medium-Sized Enterprises," Entrepreneurial Business and Economics Review, Centre for Strategic and International Entrepreneurship at the Cracow University of Economics., vol. 5(3), pages 31-48.
    17. Marte C.W. Solheim & Ron Boschma & Sverre Herstad, 2018. "Related variety, unrelated variety and the novelty content of firm innovation in urban and non-urban locations," Papers in Evolutionary Economic Geography (PEEG) 1836, Utrecht University, Department of Human Geography and Spatial Planning, Group Economic Geography, revised Oct 2018.
    18. Fei Li & Jin Chen & Ying Ying, 2019. "Innovation Search Scope, Technological Complexity, and Environmental Turbulence: A N-K Simulation," Sustainability, MDPI, vol. 11(16), pages 1-12, August.
    19. Alberto Cerezo-Narváez & Andrés Pastor-Fernández & Manuel Otero-Mateo & Pablo Ballesteros-Pérez, 2022. "The Influence of Knowledge on Managing Risk for the Success in Complex Construction Projects: The IPMA Approach," Sustainability, MDPI, vol. 14(15), pages 1-30, August.
    20. Henri Barki & Alain Pinsonneault, 2005. "A Model of Organizational Integration, Implementation Effort, and Performance," Organization Science, INFORMS, vol. 16(2), pages 165-179, April.

    More about this item

    Keywords

    Big data; AI technologies; automotive industry; digital transformation;
    All these keywords.

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hal:journl:hal-03727359. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: CCSD (email available below). General contact details of provider: https://hal.archives-ouvertes.fr/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.