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A Punctuated-Equilibrium Model of Technology Diffusion

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Author Info

  • Christoph H. Loch

    (INSEAD, Boulevard de Constance, 77305 Fontainebleau Cedex, France)

  • Bernardo A. Huberman

    (Xerox PARC, Palo Alto, California 94304)

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    Abstract

    We present an evolutionary model of technology diffusion in which an old and a new technology are available, both of which improve their performance incrementally over time. Technology adopters make repeated choices between the established and the new technology based on their perceived performance, which is subject to uncertainty. Both technologies exhibit positive externalities, or performance benefits from others using the same technology. We find that the superior technology will not necessarily be broadly adopted by the population. Externalities cause two stable usage equilibria to exist, one with the old technology being the standard and the other with the new technology the standard. Punctuations, or sudden shifts, in these equilibria determine the patterns of technology diffusion. The time for an equilibrium punctuation depends on the rate of incremental improvement of both technologies, and on the system's resistance to switching between equilibria. If the new technology has a higher rate of incremental improvement, it is adopted faster, and adoption may precede performance parity if the system's resistance to switching is low. Adoption of the new technology may trail performance parity if the system's resistance to switching is high.

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    File URL: http://dx.doi.org/10.1287/mnsc.45.2.160
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    Bibliographic Info

    Article provided by INFORMS in its journal Management Science.

    Volume (Year): 45 (1999)
    Issue (Month): 2 (February)
    Pages: 160-177

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    Handle: RePEc:inm:ormnsc:v:45:y:1999:i:2:p:160-177

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    Related research

    Keywords: technology diffusion; punctuated equilibria; network externalities; path dependence; dynamic systems; evolutionary models; simulation;

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    Cited by:
    1. Liangjie Zhao & Wenqi Duan, 2014. "Simulating the Evolution of Market Shares: The Effects of Customer Learning and Local Network Externalities," Computational Economics, Society for Computational Economics, vol. 43(1), pages 53-70, January.
    2. Antonelli, Cristiano, 2004. "The System Dynamics of Collective Knowledge: from Gradualism and Saltationism to Punctuated Change," 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 200404, University of Turin.
    3. Armstrong, Michael J & Levesque, Moren, 2002. "Timing and quality decisions for entrepreneurial product development," European Journal of Operational Research, Elsevier, vol. 141(1), pages 88-106, August.
    4. Zhang, T. & Nuttall, W.J., 2007. "An Agent Based Simulation Of Smart Metering Technology Adoption," Cambridge Working Papers in Economics 0760, Faculty of Economics, University of Cambridge.
    5. Narayanan, V.K. & Chen, Tianxu, 2012. "Research on technology standards: Accomplishment and challenges," Research Policy, Elsevier, vol. 41(8), pages 1375-1406.
    6. Yair Orbach & Gila Fruchter, 2014. "Predicting product life cycle patterns," Marketing Letters, Springer, vol. 25(1), pages 37-52, March.
    7. Carrillo-Hermosilla, Javier, 2006. "A policy approach to the environmental impacts of technological lock-in," Ecological Economics, Elsevier, vol. 58(4), pages 717-742, July.
    8. Stephan Aier & Tobias Bucher & Robert Winter, 2011. "Critical Success Factors of Service Orientation in Information Systems Engineering," Business & Information Systems Engineering, Springer, vol. 3(2), pages 77-88, April.
    9. José López-Sánchez & José Arroyo-Barrigüete & Domingo Ribeiro, 2008. "Development of a technological competition model in the presence of network effects from the modified law of Metcalfe," Service Business, Springer, vol. 2(2), pages 83-98, June.
    10. Grajek, Michał & Kretschmer, Tobias, 2012. "Identifying critical mass in the global cellular telephony market," International Journal of Industrial Organization, Elsevier, vol. 30(6), pages 496-507.
    11. Rui Leite & Aurora Teixeira, 2012. "Innovation diffusion with heterogeneous networked agents: a computational model," Journal of Economic Interaction and Coordination, Springer, vol. 7(2), pages 125-144, October.
    12. Minniti, Maria, 2004. "Entrepreneurial alertness and asymmetric information in a spin-glass model," Journal of Business Venturing, Elsevier, vol. 19(5), pages 637-658, September.
    13. Warr, Benjamin & Ayres, Robert, 2006. "REXS: A forecasting model for assessing the impact of natural resource consumption and technological change on economic growth," Structural Change and Economic Dynamics, Elsevier, vol. 17(3), pages 329-378, September.
    14. Costa, Álvaro & Fernandes, Ruben, 2012. "Urban public transport in Europe: Technology diffusion and market organisation," Transportation Research Part A: Policy and Practice, Elsevier, vol. 46(2), pages 269-284.
    15. Bonaccorsi, Andrea & Rossi, Cristina, 2003. "Why Open Source software can succeed," Research Policy, Elsevier, vol. 32(7), pages 1243-1258, July.

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