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Recombinant Innovation and Endogenous Transitions

Author

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  • Koen Frenken
  • Luis R. Izquierdo
  • Paolo Zeppini

Abstract

We propose a model of technological transitions based on two different types of innovations. Branching innovations refer to technological improvements along a particular path, while recombinant innovations represent fusions of multiple paths. Recombinant innovations create “short-cuts” which reduce switching costs allowing agents to escape a technological lock-in. As a result, recombinant innovations speed up technological progress allowing transitions that are impossible with only branching innovations. Our model replicates some stylized facts of technological change, such as technological lockin, experimental failure, punctuated change and irreversibility. Furthermore, an extensive simulation experiment suggests that there is an optimal rate of innovation, which is strongly correlated with the number of recombination innovations. This underlines the pivotal role of technological variety as a seed for recombinant innovation leading to technological transitions.

Suggested Citation

  • Koen Frenken & Luis R. Izquierdo & Paolo Zeppini, 2012. "Recombinant Innovation and Endogenous Transitions," Working Papers 12-01, Eindhoven Center for Innovation Studies, revised Jan 2012.
    • Handle: RePEc:ein:tuecis:1201
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    References listed on IDEAS

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    Cited by:

    1. Lorenzo Napolitano & Evangelos Evangelou & Emanuele Pugliese & Paolo Zeppini & Graham Room, 2017. "Technology networks: the autocatalytic origins of innovation," Papers 1708.03511, arXiv.org, revised Apr 2018.
    2. André Dumas TSAMBOU & Nicolae BIBU, 2017. "A Comparative Analysis of the Determinants of Innovation Behaviour Between Cameroon, Cote d'Ivoire and Senegal," REVISTA DE MANAGEMENT COMPARAT INTERNATIONAL/REVIEW OF INTERNATIONAL COMPARATIVE MANAGEMENT, Faculty of Management, Academy of Economic Studies, Bucharest, Romania, vol. 18(3), pages 234-259, July.
    3. Zhang, JingJing & Yan, Yan & Guan, JianCheng, 2019. "Recombinant distance, network governance and recombinant innovation," Technological Forecasting and Social Change, Elsevier, vol. 143(C), pages 260-272.
    4. Caviggioli, Federico & Jensen, Paul & Scellato, Giuseppe, 2020. "Highly skilled migrants and technological diversification in the US and Europe," Technological Forecasting and Social Change, Elsevier, vol. 154(C).
    5. Maciej Tarkowski, 2021. "On the Emergence of Sociotechnical Regimes of Electric Urban Water Transit Systems," Energies, MDPI, vol. 14(19), pages 1-21, September.
    6. Carolina Castaldi & Koen Frenken & Bart Los, 2015. "Related Variety, Unrelated Variety and Technological Breakthroughs: An analysis of US State-Level Patenting," Regional Studies, Taylor & Francis Journals, vol. 49(5), pages 767-781, May.
    7. Matheus E. Leusin & Bjoern Jindra & Daniel S. Hain, 2021. "An evolutionary view on the emergence of Artificial Intelligence," Papers 2102.00233, arXiv.org.
    8. Lara Agostini & Federico Caviggioli & Francesco Galati & Barbara Bigliardi, 2020. "A social perspective of knowledge-based innovation: mobility and agglomeration. Introduction to the special section," The Journal of Technology Transfer, Springer, vol. 45(5), pages 1309-1323, October.
    9. Zeppini, Paolo, 2015. "A discrete choice model of transitions to sustainable technologies," Journal of Economic Behavior & Organization, Elsevier, vol. 112(C), pages 187-203.
    10. Aldieri, Luigi & Makkonen, Teemu & Paolo Vinci, Concetto, 2020. "Environmental knowledge spillovers and productivity: A patent analysis for large international firms in the energy, water and land resources fields," Resources Policy, Elsevier, vol. 69(C).
    11. Essletzbichler Jürgen, 2012. "Generalized Darwinism, group selection and evolutionary economic geography," ZFW – Advances in Economic Geography, De Gruyter, vol. 56(1-2), pages 129-146, October.
    12. Maciej Tarkowski & Krystian Puzdrakiewicz, 2021. "Connectivity Benefits of Small Zero-Emission Autonomous Ferries in Urban Mobility—Case of the Coastal City of Gdańsk (Poland)," Sustainability, MDPI, vol. 13(23), pages 1-13, November.
    13. van Meeteren, Michiel & Trincado-Munoz, Francisco & Rubin, Tzameret H. & Vorley, Tim, 2022. "Rethinking the digital transformation in knowledge-intensive services: A technology space analysis," Technological Forecasting and Social Change, Elsevier, vol. 179(C).
    14. Thomas Brenner & Franziska Pudelko, 2019. "The effects of public research and subsidies on regional structural strength," Journal of Evolutionary Economics, Springer, vol. 29(5), pages 1433-1458, November.
    15. Paolo Zeppini & Koen Frenken & Roland Kupers, 2013. "Threshold models of technological transitions," Working Papers 13-06, Eindhoven Center for Innovation Studies, revised Aug 2013.
    16. Lopolito, A. & Morone, P. & Taylor, R., 2013. "Emerging innovation niches: An agent based model," Research Policy, Elsevier, vol. 42(6), pages 1225-1238.
    17. Paolo Zeppini, 2014. "A discrete choice model of transitions to sustainable technologies: speed limits and optimal monetary policies," Department of Economics Working Papers 28/14, University of Bath, Department of Economics.
    18. Sandro Montresor & Francesco Quatraro, 2020. "Green technologies and Smart Specialisation Strategies: a European patent-based analysis of the intertwining of technological relatedness and key enabling technologies," Regional Studies, Taylor & Francis Journals, vol. 54(10), pages 1354-1365, October.
    19. Safarzyńska, Karolina & Frenken, Koen & van den Bergh, Jeroen C.J.M., 2012. "Evolutionary theorizing and modeling of sustainability transitions," Research Policy, Elsevier, vol. 41(6), pages 1011-1024.
    20. P. G. J. Persoon & R. N. A. Bekkers & F. Alkemade, 2020. "How cumulative is technological knowledge?," Papers 2012.00095, arXiv.org, revised May 2021.
    21. M. Lynne Markus & Kevin Mentzer, 2014. "Foresight for a responsible future with ICT," Information Systems Frontiers, Springer, vol. 16(3), pages 353-368, July.
    22. Artur Santoalha & Davide Consoli & Fulvio Castellacci, 2019. "Do digital skills foster green diversification? A study of European regions," Working Papers on Innovation Studies 20191029, Centre for Technology, Innovation and Culture, University of Oslo.
    23. J. Farmer & Cameron Hepburn & Penny Mealy & Alexander Teytelboym, 2015. "A Third Wave in the Economics of Climate Change," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 62(2), pages 329-357, October.
    24. Jürgen Essletzbichler, 2013. "Relatedness, industrial branching and technological cohesion in U.S. metropolitan areas," Papers in Evolutionary Economic Geography (PEEG) 1307, Utrecht University, Department of Human Geography and Spatial Planning, Group Economic Geography, revised May 2013.

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    More about this item

    Keywords

    variety; network externalities; lock-in; switching costs; recombinant innovations; transition; punctuated change;
    All these keywords.

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes

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