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Percolation of new products

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  • Fibich, Gadi
  • Levin, Tomer

Abstract

In most models of diffusion of new products, every individual in the social network is a potential adopter. When, however, a fraction α of the individuals cannot adopt the product at any time, the new product percolates (rather than diffuses) in the network, similarly to movement through porous materials. We obtain explicit expressions for the fraction of adopters as a function of time, for complete networks, circular networks, D-dimensional Cartesian networks, small-worlds networks, and scale-free networks. These expressions show that the complex effect of percolation can be captured by two simple aggregate effects: Decreasing the market potential by 1−α, and reducing the peers effect by (1−α)k, where k depends on the network type. Hence, percolation of new products is qualitatively similar to diffusion of new products. In particular, there is no threshold value at which a phase transition occurs.

Suggested Citation

  • Fibich, Gadi & Levin, Tomer, 2020. "Percolation of new products," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 540(C).
  • Handle: RePEc:eee:phsmap:v:540:y:2020:i:c:s0378437119317261
    DOI: 10.1016/j.physa.2019.123055
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    1. Solomon, Sorin & Weisbuch, Gerard & de Arcangelis, Lucilla & Jan, Naeem & Stauffer, Dietrich, 2000. "Social percolation models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 277(1), pages 239-247.
    2. Marcello Graziano & Kenneth Gillingham, 2015. "Spatial patterns of solar photovoltaic system adoption: The influence of neighbors and the built environment," Journal of Economic Geography, Oxford University Press, vol. 15(4), pages 815-839.
    3. Rai, Varun & Reeves, D. Cale & Margolis, Robert, 2016. "Overcoming barriers and uncertainties in the adoption of residential solar PV," Renewable Energy, Elsevier, vol. 89(C), pages 498-505.
    4. Gordon T. Kraft-Todd & Bryan Bollinger & Kenneth Gillingham & Stefan Lamp & David G. Rand, 2018. "Credibility-enhancing displays promote the provision of non-normative public goods," Nature, Nature, vol. 563(7730), pages 245-248, November.
    5. Shun-Chen Niu, 2002. "A Stochastic Formulation of the Bass Model of New-Product Diffusion," Review of Marketing Science Working Papers 1-4-1000, Berkeley Electronic Press.
    6. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    7. Wallace J. Hopp (Editor-in-Chief), 2004. "Ten Most Influential Papers of Management Science's First Fifty Years," Management Science, INFORMS, vol. 50(12_supple), pages 1763-1763, December.
    8. Goldenberg, J & Libai, B & Solomon, S & Jan, N & Stauffer, D, 2000. "Marketing percolation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 284(1), pages 335-347.
    9. Peres, Renana & Muller, Eitan & Mahajan, Vijay, 2010. "Innovation diffusion and new product growth models: A critical review and research directions," International Journal of Research in Marketing, Elsevier, vol. 27(2), pages 91-106.
    10. Weisbuch, Gérard & Stauffer, Dietrich, 2000. "Hits and flops dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 287(3), pages 563-576.
    11. 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.
    12. Bryan Bollinger & Kenneth Gillingham, 2012. "Peer Effects in the Diffusion of Solar Photovoltaic Panels," Marketing Science, INFORMS, vol. 31(6), pages 900-912, November.
    13. Gadi Fibich & Ro'i Gibori, 2010. "Aggregate Diffusion Dynamics in Agent-Based Models with a Spatial Structure," Operations Research, INFORMS, vol. 58(5), pages 1450-1468, October.
    14. Gérard Weisbuch & Dietrich Stauffer, 2000. "Hits and Flops Dynamics," Working Papers 00-07-036, Santa Fe Institute.
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    1. Xenikos, D.G. & Constantoudis, V., 2023. "Weibull dynamics and power-law diffusion of epidemics in small world 2D networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 617(C).

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