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Knowledge characteristics and the dynamics of technological alliances in Pharmaceuticals: Empirical evidence from Europe, US and Japan

Author

Listed:
  • Jackie Krafft

    (GREDEG - Groupe de Recherche en Droit, Economie et Gestion - UNS - Université Nice Sophia Antipolis (1965 - 2019) - CNRS - Centre National de la Recherche Scientifique - UniCA - Université Côte d'Azur)

  • Francesco Quatraro

    (GREDEG - Groupe de Recherche en Droit, Economie et Gestion - UNS - Université Nice Sophia Antipolis (1965 - 2019) - CNRS - Centre National de la Recherche Scientifique - UniCA - Université Côte d'Azur)

  • Pier-Paolo Saviotti

    (GAEL - Laboratoire d'Economie Appliquée = Grenoble Applied Economics Laboratory - UPMF - Université Pierre Mendès France - Grenoble 2 - INRA - Institut National de la Recherche Agronomique)

Abstract

The paper investigates the co-evolutionary patterns of the dynamics of technological alliances and of the structure of the knowledge base in the pharmaceutical sector. The main hypothesis under scrutiny is that technological alliances represent a key resource for firms in knowledge intensive sectors to cope with dramatic changes in the knowledge base, marked by the introduction of discontinuities opening up new technological trajectories. Using patent information and data on technological alliances drawn from the CATI-MERIT database, we compare the evidence concerning the so-called triad regions, i.e. United States, Europe and Japan. The empirical results support the existence of a life cycle in biotechnology affecting the pharmaceutical industry. Furthermore, the dynamics of alliances is found to depend on (i) the phase of the biotechnology life cycle, (ii) the strength of the region in biotechnology and (iii) the general features of the economic environment of the region.

Suggested Citation

  • Jackie Krafft & Francesco Quatraro & Pier-Paolo Saviotti, 2014. "Knowledge characteristics and the dynamics of technological alliances in Pharmaceuticals: Empirical evidence from Europe, US and Japan," Post-Print hal-02632021, HAL.
    • Handle: RePEc:hal:journl:hal-02632021
    DOI: 10.1007/s00191-014-0338-8
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    2. Renato Garcia & Veneziano Araújo & Suelene Mascarini & Emerson Gomes Dos Santos & Ariana Ribeiro Costa, 2018. "An Analysis Of The Relation Between Geographical And Cognitive Proximity In University-Industry Linkages," Anais do XLIV Encontro Nacional de Economia [Proceedings of the 44th Brazilian Economics Meeting] 132, ANPEC - Associação Nacional dos Centros de Pós-Graduação em Economia [Brazilian Association of Graduate Programs in Economics].
    3. Alessandra Colombelli & Gianluca Orsatti & Francesco Quatraro, 2021. "Local knowledge composition and the emergence of entrepreneurial activities across industries: evidence from Italian NUTS-3 regions," Small Business Economics, Springer, vol. 56(2), pages 613-635, February.
    4. Martin Kalthaus, 2020. "Knowledge recombination along the technology life cycle," Journal of Evolutionary Economics, Springer, vol. 30(3), pages 643-704, July.
    5. Ad van den Oord & Arjen van Witteloostuijn, 2018. "A multi-level model of emerging technology: An empirical study of the evolution of biotechnology from 1976 to 2003," PLOS ONE, Public Library of Science, vol. 13(5), pages 1-27, May.
    6. Subramanian, Annapoornima M. & Bo, Wang & Kah-Hin, Chai, 2018. "The role of knowledge base homogeneity in learning from strategic alliances," Research Policy, Elsevier, vol. 47(1), pages 158-168.
    7. Chang, Shu-Hao & Fan, Chin-Yuan, 2016. "Identification of the technology life cycle of telematics: A patent-based analytical perspective," Technological Forecasting and Social Change, Elsevier, vol. 105(C), pages 1-10.
    8. Ouyang, Huimin & Park, Chansoo & Oh, Chang Hoon, 2024. "The impact of reverse knowledge transfers on exploitative and exploratory innovations in MNCs: The role of knowledge distance," Journal of Business Research, Elsevier, vol. 179(C).
    9. Boutillier, Sophie & Laperche, Blandine & Lebert, Didier & Elouaer-Mrizak, Sana, 2023. "A systemic analysis of the technological trajectory at company level based on patent data: The case of Sanofi's vaccine technology," Technovation, Elsevier, vol. 124(C).
    10. Antonelli, Cristiano & Colombelli, Alessandra, 2015. "The knowledge cost function," International Journal of Production Economics, Elsevier, vol. 168(C), pages 290-302.
    11. Sadowski, Bert & Nomaler, Onder & Whalley, Jason, 2016. "Technological Diversification of ICT companies into the Internet of things (IoT): A Patent -based Analysis," 27th European Regional ITS Conference, Cambridge (UK) 2016 148701, International Telecommunications Society (ITS).
    12. Maleki, Ali & Rosiello, Alessandro, 2019. "Does knowledge base complexity affect spatial patterns of innovation? An empirical analysis in the upstream petroleum industry," Technological Forecasting and Social Change, Elsevier, vol. 143(C), pages 273-288.
    13. Spyros Arvanitis & Areti Gkypali & Kostas Tsekouras, 2014. "Knowledge Base, Exporting Activities, Innovation Openness and Innovation Performance," KOF Working papers 14-361, KOF Swiss Economic Institute, ETH Zurich.
    14. KOUAKOU, Dorgyles C.M. & SZEGO, Eva, 2024. "Evaluating the integration of artificial intelligence technologies in defense activities and the effect of national innovation system performance on its enhancement," MPRA Paper 120617, University Library of Munich, Germany.

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    JEL classification:

    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
    • L2 - Industrial Organization - - Firm Objectives, Organization, and Behavior

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