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Heterogeneity and the dynamics of technology adoption

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  • Stephen Ryan

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  • Catherine Tucker

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Abstract

We estimate the demand for a videocalling technology in the presence of both network effects and heterogeneity. Using a unique dataset from a large multinational firm, we pose and estimate a fully dynamic model of technology adoption. We propose a novel identification strategy based on post-adoption technology usage to disentangle equilibrium beliefs concerning the evolution of the network from observed and unobserved heterogeneity in technology adoption costs and use benefits. We find that employees have significant heterogeneity in both adoption costs and network benefits, and have preferences for diverse networks. Using our estimates, we evaluate a number of counterfactual adoption policies, and find that a policy of strategically targeting the right subtype for initial adoption can lead to a faster-growing and larger network than a policy of uncoordinated or diffuse adoption.
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Suggested Citation

  • Stephen Ryan & Catherine Tucker, 2012. "Heterogeneity and the dynamics of technology adoption," Quantitative Marketing and Economics (QME), Springer, vol. 10(1), pages 63-109, March.
  • Handle: RePEc:kap:qmktec:v:10:y:2012:i:1:p:63-109
    DOI: 10.1007/s11129-011-9109-0
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    Cited by:

    1. Daniel Ackerberg & Xiaohong Chen & Jinyong Hahn, 2012. "A Practical Asymptotic Variance Estimator for Two-Step Semiparametric Estimators," The Review of Economics and Statistics, MIT Press, vol. 94(2), pages 481-498, May.
    2. Matttia De' Grassi Di Pianura, 2012. "Subsidising network technology adoption the case of publishers and E-readers," Working Papers hal-00714447, HAL.
    3. Andre Veiga, 2014. "Dynamic Platform Design," Working Papers 14-15, NET Institute.
    4. Grajek, Michal & Kretschmer, Tobias, 2009. "Usage and diffusion of cellular telephony, 1998-2004," International Journal of Industrial Organization, Elsevier, vol. 27(2), pages 238-249, March.
    5. Gupta, Sunil & Mela, Carl F. & Vidal-Sanz, Jose M., 2009. "The value of a "free" customer," DEE - Working Papers. Business Economics. WB wb092903, Universidad Carlos III de Madrid. Departamento de Economía de la Empresa.
    6. Pradeep K. Chintagunta & Harikesh S. Nair, 2011. "Structural Workshop Paper --Discrete-Choice Models of Consumer Demand in Marketing," Marketing Science, INFORMS, vol. 30(6), pages 977-996, November.
    7. Patrick Bajari & C. Lanier Benkard & Jonathan Levin, 2007. "Estimating Dynamic Models of Imperfect Competition," Econometrica, Econometric Society, vol. 75(5), pages 1331-1370, September.
    8. Daniel Ackerberg & Xiaohong Chen & Jinyong Hahn, 2011. "Asymptotic Variance Estimator for Two-Step Semiparametric Estimators," Cowles Foundation Discussion Papers 1803, Cowles Foundation for Research in Economics, Yale University.
    9. Jake Kendall & Nirvikar Singh & Kristin Williams & Yan Zhou & P.D. Kaushik, 2007. "Network Economics and the Digital Divide in Rural South Asia," Working Papers 07-30, NET Institute, revised Sep 2007.
    10. Van Hove, Leo, 2016. "Testing Metcalfe's law: Pitfalls and possibilities," Information Economics and Policy, Elsevier, vol. 37(C), pages 67-76.
    11. Andrew Sweeting, 2007. "Dynamic Product Repositioning in Differentiated Product Markets: The Case of Format Switching in the Commercial Radio Industry," NBER Working Papers 13522, National Bureau of Economic Research, Inc.
    12. Michaela Draganska & Sanjog Misra & Victor Aguirregabiria & Pat Bajari & Liran Einav & Paul Ellickson & Dan Horsky & Sridhar Narayanan & Yesim Orhun & Peter Reiss & Katja Seim & Vishal Singh & Raphael, 2008. "Discrete choice models of firms’ strategic decisions," Marketing Letters, Springer, vol. 19(3), pages 399-416, December.
    13. Catherine Tucker, 2011. "Network Stability, Network Externalities and Technology Adoption," NBER Working Papers 17246, National Bureau of Economic Research, Inc.
    14. Przemysław Jeziorski, 2014. "Estimation of cost efficiencies from mergers: application to US radio," RAND Journal of Economics, RAND Corporation, vol. 45(4), pages 816-846, December.
    15. Andre Veiga & E. Glen Weyl, 2011. "Multidimensional Heterogeneity and Platform Design," Working Papers 11-33, NET Institute, revised Nov 2011.
    16. Wesley Hartmann & Puneet Manchanda & Harikesh Nair & Matthew Bothner & Peter Dodds & David Godes & Kartik Hosanagar & Catherine Tucker, 2008. "Modeling social interactions: Identification, empirical methods and policy implications," Marketing Letters, Springer, vol. 19(3), pages 287-304, December.
    17. Song Yao & Carl F. Mela, 2008. "A Dynamic Model of Sponsored Search Advertising," Working Papers 08-16, NET Institute, revised Sep 2008.
    18. Estelle Cantillon & Pai-Ling Yin, 2008. "Asymmetric Network Effects," Working Papers 08-42, NET Institute.
    19. Maican, Florin G., 2012. "From Boom to Bust and Back Again: A dynamic analysis of IT services," Working Papers in Economics 543, University of Gothenburg, Department of Economics.
    20. Carl F. Mela, 2011. "Structural Workshop Paper --Data Selection and Procurement," Marketing Science, INFORMS, vol. 30(6), pages 965-976, November.
    21. Catherine Tucker, 2008. "Social Interactions, Network Fluidity and Network Effects," Working Papers 08-30, NET Institute.
    22. Pinar Karaca-Mandic, 2011. "Role of complementarities in technology adoption: The case of DVD players," Quantitative Marketing and Economics (QME), Springer, vol. 9(2), pages 179-210, June.

    More about this item

    Keywords

    Technology adoption; Videoconferencing; Dynamics; Network effects; Unobserved heterogeneity; C10; L10; L86; M3; O32;

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • L10 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - General
    • O32 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Management of Technological Innovation and R&D

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