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A percolation-based model explaining delayed takeoff in new-product diffusion

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  • Martin Hohnisch
  • Sabine Pittnauer
  • Dietrich Stauffer

Abstract

A model of new-product diffusion is proposed in which a site-percolation dynamics represents socially driven diffusion of knowledge about the product's characteristics in a population of consumers. A consumer buys the new product if her valuation of it—formed after her witnessing the product in use among her friends or neighbors—is not below the price of the product announced in a given period. Our model attributes the empirical finding of a delayed takeoff of a new product to a drift of the percolation dynamics from a nonpercolating regime to a percolating regime. This drift is caused by learning effects lowering the price of the product, or by network effects increasing its individual valuations by consumers, both with an increasing number of buyers. Copyright 2008 , Oxford University Press.

Suggested Citation

  • Martin Hohnisch & Sabine Pittnauer & Dietrich Stauffer, 2008. "A percolation-based model explaining delayed takeoff in new-product diffusion," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 17(5), pages 1001-1017, October.
    • Handle: RePEc:oup:indcch:v:17:y:2008:i:5:p:1001-1017
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    File URL: http://hdl.handle.net/10.1093/icc/dtn031
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    Cited by:

    1. Cantono, Simona, 2012. "Unveiling diffusion dynamics: an autocatalytic percolation model of environmental innovation diffusion and the optimal dynamic path of adoption subsidies," Department of Economics and Statistics Cognetti de Martiis LEI & BRICK - Laboratory of Economics of Innovation "Franco Momigliano", Bureau of Research in Innovation, Complexity and Knowledge, Collegio 201222, University of Turin.
    2. Katarzyna Maciejowska & Arkadiusz Jedrzejewski & Anna Kowalska-Pyzalska & Katarzyna Sznajd-Weron & Rafal Weron, 2015. "Two faces of word-of-mouth: Understanding the impact of social interactions on demand curves for innovative products," HSC Research Reports HSC/15/09, Hugo Steinhaus Center, Wroclaw University of Science and Technology.
    3. Kanai, Yasuhiro & Abe, Keiji & Seki, Yoichi, 2015. "Price percolation model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 427(C), pages 226-233.
    4. Muhammad Al Atiqi & Shuang Chang & Hiroshi Deguchi, 2023. "Simulating the influence of Facebook fan pages on individual attitudes toward vaccination using agent‐based modelling," Systems Research and Behavioral Science, Wiley Blackwell, vol. 40(3), pages 595-610, May.
    5. Bichraoui-Draper, Najet & Xu, Ming & Miller, Shelie A. & Guillaume, Bertrand, 2015. "Agent-based life cycle assessment for switchgrass-based bioenergy systems," Resources, Conservation & Recycling, Elsevier, vol. 103(C), pages 171-178.
    6. Thomas Grebel, 2011. "Innovation and Health," Books, Edward Elgar Publishing, number 14375.
    7. Jiang, Yonglei & Liao, Feixiong & Jin, Lianjie, 2021. "Effects of locational accessibility on firm diffusion characteristics: The case of Sino-Europe Economic Corridor," Transport Policy, Elsevier, vol. 105(C), pages 80-93.
    8. Thomas Grebel, 2013. "Network Evolution in Basic Science," Economic Complexity and Evolution, in: Guido Buenstorf & Uwe Cantner & Horst Hanusch & Michael Hutter & Hans-Walter Lorenz & Fritz Rahmeyer (ed.), The Two Sides of Innovation, edition 127, pages 39-53, Springer.
    9. D’Orazio, Paola & Valente, Marco, 2019. "The role of finance in environmental innovation diffusion: An evolutionary modeling approach," Journal of Economic Behavior & Organization, Elsevier, vol. 162(C), pages 417-439.
    10. Chandrasekaran, Deepa & Arts, Joep W.C. & Tellis, Gerard J. & Frambach, Ruud T., 2013. "Pricing in the international takeoff of new products," International Journal of Research in Marketing, Elsevier, vol. 30(3), pages 249-264.
    11. Jiang, Yonglei & Timmermans, Harry J.P. & Yu, Bin, 2018. "Relocation of manufacturing industry from the perspective of transport accessibility – An application of percolation theory," Transport Policy, Elsevier, vol. 63(C), pages 10-29.
    12. Paolo Zeppini & Koen Frenken, 2018. "Networks, Percolation, and Consumer Demand," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 21(3), pages 1-1.
    13. Shi, Yingying & Zeng, Yongchao & Engo, Jean & Han, Botang & Li, Yang & Muehleisen, Ralph T., 2020. "Leveraging inter-firm influence in the diffusion of energy efficiency technologies: An agent-based model," Applied Energy, Elsevier, vol. 263(C).
    14. Paolo Zeppini & Koen Frenken & Roland Kupers, 2013. "Threshold models of technological transitions," Working Papers 13-06, Eindhoven Center for Innovation Studies, revised Aug 2013.
    15. Paolo Zeppini & Koen Frenken, 2015. "Networks, Percolation, and Demand," Department of Economics Working Papers 38/15, University of Bath, Department of Economics.
    16. Solomon Sorin & Golo Natasa, 2013. "Minsky Financial Instability, Interscale Feedback, Percolation and Marshall–Walras Disequilibrium," Accounting, Economics, and Law: A Convivium, De Gruyter, vol. 3(3), pages 167-260, October.
    17. Fibich, Gadi & Levin, Tomer, 2020. "Percolation of new products," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 540(C).
    18. Giovanni Pegoretti & Francesco Rentocchini & Giuseppe Vittucci Marzetti, 2012. "An agent-based model of innovation diffusion: network structure and coexistence under different information regimes," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 7(2), pages 145-165, October.
    19. Paola D’Orazio & Marco Valente, 2018. "Do Financial Constraints Hamper Environmental Innovation Diffusion? An Agent-Based Approach," SPRU Working Paper Series 2018-10, SPRU - Science Policy Research Unit, University of Sussex Business School.
    20. Karolina Safarzyńska & Jeroen Bergh, 2010. "Evolutionary models in economics: a survey of methods and building blocks," Journal of Evolutionary Economics, Springer, vol. 20(3), pages 329-373, June.
    21. Cantono Simona, 2012. "A percolation model of multi-technology diffusion: information feedbacks, learning economies and subsidy policy," Department of Economics and Statistics Cognetti de Martiis. Working Papers 201205, University of Turin.
    22. Elmar Kiesling & Markus Günther & Christian Stummer & Lea Wakolbinger, 2012. "Agent-based simulation of innovation diffusion: a review," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 20(2), pages 183-230, June.

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