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Emerging innovation niches: An agent based model

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  • Lopolito, A.
  • Morone, P.
  • Taylor, R.

Abstract

The creation of an innovation niche depends on the interaction of three mechanisms involving: converging expectations, networking among the innovation actors, and learning about the novelty through efficient knowledge creation and diffusion. Such mechanisms define the key characteristics of a network of firms (i.e. the innovation niche), and the interaction among them guides the development and diffusion of a new technology. In this paper, we propose an agent-based model to investigate the dynamics characterising such interactions and the role that policy intervention can have in governing the niche development process. Specifically, we consider and assess the impact of two policy actions: (1) increasing actors’ expectations towards the new technology by means of information spreading and (2) providing subsidies aimed at stimulating technological switch. Our results confirm the importance of policy intervention and show the dominance of information spreading activities over subsidies. The former policy action, in fact, preserves a broad consensus around the new technology, a fact which turned out to be fundamental in order to promote efficient knowledge diffusion and the effective use of individual and network resources.

Suggested Citation

  • Lopolito, A. & Morone, P. & Taylor, R., 2013. "Emerging innovation niches: An agent based model," Research Policy, Elsevier, vol. 42(6), pages 1225-1238.
    • Handle: RePEc:eee:respol:v:42:y:2013:i:6:p:1225-1238
    DOI: 10.1016/j.respol.2013.03.002
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    References listed on IDEAS

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    2. Kwon, Seokbeom & Motohashi, Kazuyuki, 2017. "How institutional arrangements in the National Innovation System affect industrial competitiveness: A study of Japan and the U.S. with multiagent simulation," Technological Forecasting and Social Change, Elsevier, vol. 115(C), pages 221-235.
    3. Nabila Arfaoui & Eric Brouillat & Maïder Saint-Jean, 2015. "The Impact of REACH on Eco-Innovation: How Perception Misfits on Policy Stringency Matter," GREDEG Working Papers 2015-45, Groupe de REcherche en Droit, Economie, Gestion (GREDEG CNRS), Université Côte d'Azur, France.
    4. Herrmann, J.K. & Savin, I., 2017. "Optimal policy identification: Insights from the German electricity market," Technological Forecasting and Social Change, Elsevier, vol. 122(C), pages 71-90.
    5. KWON Seokbeom & MOTOHASHI Kazuyuki, 2015. "How Institutional Arrangements in the National Innovation System Affect Industrial Competitiveness: A study of Japan and the United States with multiagent simulation," Discussion papers 15065, Research Institute of Economy, Trade and Industry (RIETI).
    6. Paolo Zeppini & Koen Frenken & Roland Kupers, 2013. "Threshold models of technological transitions," Working Papers 13-06, Eindhoven Center for Innovation Studies, revised Aug 2013.
    7. Peter Hall & Robert Wylie, 2014. "Isolation and technological innovation," Journal of Evolutionary Economics, Springer, vol. 24(2), pages 357-376, April.
    8. Walrave, Bob & Raven, Rob, 2016. "Modelling the dynamics of technological innovation systems," Research Policy, Elsevier, vol. 45(9), pages 1833-1844.
    9. Jorge Gustavo Rodríguez Aboytes & Matthias Barth, 2020. "Learning Processes in the Early Development of Sustainable Niches: The Case of Sustainable Fashion Entrepreneurs in Mexico," Sustainability, MDPI, vol. 12(20), pages 1-26, October.
    10. Haruo H. Horaguchi & Toichiro Susumago, 2022. "Global R&D Location Strategy of Multinational Enterprises: an Agent-Based Simulation Modeling Approach," Journal of Industry, Competition and Trade, Springer, vol. 22(3), pages 457-479, December.
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    12. Könnölä, Totti & Eloranta, Ville & Turunen, Taija & Salo, Ahti, 2021. "Transformative governance of innovation ecosystems," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    13. Fabian Adelt & Johannes Weyer & Sebastian Hoffmann & Andreas Ihrig, 2018. "Simulation of the Governance of Complex Systems (SimCo): Basic Concepts and Experiments on Urban Transportation," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 21(2), pages 1-2.
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    More about this item

    Keywords

    Socio-technical transition; Strategic niche management; Policy action;
    All these keywords.

    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
    • O38 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Government Policy

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