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Adoption of Technologies With Network Effects: An Empirical Examination of the Adoption of Automated Teller Machines

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  • Garth Saloner
  • Andrea Shepard

Abstract

The literature on networks suggests that the value of a network is positively affected by the number of geographically dispersed locations it serves (the "network effect") and the number of its users (the "production scale effect"). We show that as a result a firm's expected time until adoption of technologies with network effects declines in both users and locations. We provide empirical evidence on the adoption of automated teller machines by banks that is consistent with this prediction. Using standard duration models, we find that a bank's date of adoption is decreasing in the number of its branches (a proxy for the number of locations and hence for the network effect) and the value of its deposits (a proxy for number of users and hence for production scale economies). The network effect is the larger of the two effects.

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  • Garth Saloner & Andrea Shepard, 1992. "Adoption of Technologies With Network Effects: An Empirical Examination of the Adoption of Automated Teller Machines," NBER Working Papers 4048, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:4048
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    References listed on IDEAS

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    1. Joseph Farrell & Garth Saloner, 1985. "Standardization, Compatibility, and Innovation," RAND Journal of Economics, The RAND Corporation, vol. 16(1), pages 70-83, Spring.
    2. David, Paul A, 1985. "Clio and the Economics of QWERTY," American Economic Review, American Economic Association, vol. 75(2), pages 332-337, May.
    3. Farrell, Joseph & Saloner, Garth, 1986. "Installed Base and Compatibility: Innovation, Product Preannouncements, and Predation," American Economic Review, American Economic Association, vol. 76(5), pages 940-955, December.
    4. Nancy L. Rose & Paul L. Joskow, 1990. "The Diffusion of New Technologies: Evidence from the Electric Utility Industry," RAND Journal of Economics, The RAND Corporation, vol. 21(3), pages 354-373, Autumn.
    5. Joseph Farrell & Garth Saloner, 1985. "Installed Base and Compatibility With Implications for Product Preannouncements," Working papers 385, Massachusetts Institute of Technology (MIT), Department of Economics.
    6. Kiefer, Nicholas M, 1988. "Economic Duration Data and Hazard Functions," Journal of Economic Literature, American Economic Association, vol. 26(2), pages 646-679, June.
    7. Timothy H. Hannan & John M. McDowell, 1984. "The Determinants of Technology Adoption: The Case of the Banking Firm," RAND Journal of Economics, The RAND Corporation, vol. 15(3), pages 328-335, Autumn.
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    Cited by:

    1. Honohan, Patrick & Vittas, Dimitri, 1996. "Bank regulation and the network paradigm : policy implications for developing and transition economies," Policy Research Working Paper Series 1631, The World Bank.
    2. Shi, Lefeng & Hao, Ying & Lv, Shengnan & Cipcigan, Liana & Liang, Jun, 2021. "A comprehensive charging network planning scheme for promoting EV charging infrastructure considering the Chicken-Eggs dilemma," Research in Transportation Economics, Elsevier, vol. 88(C).
    3. Domowitz, Ian, 1995. "Electronic derivatives exchanges: Implicit mergers, network externalities, and standardization," The Quarterly Review of Economics and Finance, Elsevier, vol. 35(2), pages 163-175.
    4. Howell, Bronwyn & Obren, Mark, 2002. "Broadband Diffusion: Lags from Vintage Capital, Learning by Doing, Information Barriers and Network Effects," Working Paper Series 3896, Victoria University of Wellington, The New Zealand Institute for the Study of Competition and Regulation.
    5. JAMES J. McANDREWS, 1999. "E‐Money And Payment System Risks," Contemporary Economic Policy, Western Economic Association International, vol. 17(3), pages 348-357, July.

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