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Has the Internet accelerated the diffusion of new products?

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Author Info

  • Prince, Jeffrey T.
  • Simon, Daniel H.

Abstract

In this paper we measure the effect of Internet adoption on consumers' propensity to adopt a wide range of diffusing products. To do this, we utilize a rich panel of household surveys on purchases of relatively new technology products. Our results indicate that the Internet accelerates product diffusion, but with varying magnitude. In an attempt to determine the mechanisms underlying this effect, we find direct evidence that the Internet does not increase product awareness. However, we find suggestive evidence that the Internet increases adoption rates both through access to increased information about new products (via online research) and through online shopping. We also find that the magnitude of the Internet's effect is strongly tied to diffusion rates, and especially familiarity rates. This finding is consistent with Internet access having the greatest impact on the adoption of products with more developed marketing strategies (i.e., more developed information sources and online markets). Our findings indicate that the Internet helps bolster demand for products early in their diffusion process, and they suggest that improved access to information and the convenience of online shopping are likely the primary drivers of this effect. Consequently, to the extent that accelerated diffusion of new products is (on net) desirable, our findings may provide a further argument toward social promotion of Internet adoption.

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Bibliographic Info

Article provided by Elsevier in its journal Research Policy.

Volume (Year): 38 (2009)
Issue (Month): 8 (October)
Pages: 1269-1277

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Handle: RePEc:eee:respol:v:38:y:2009:i:8:p:1269-1277

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Web page: http://www.elsevier.com/locate/respol

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Keywords: Internet Diffusion New products Rank model Epidemic model;

References

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  1. Jess Gaspar & Edward Glaeser, 1996. "Information Technology and the Future of Cities," NBER Working Papers 5562, National Bureau of Economic Research, Inc.
  2. Heckman, James J & Ichimura, Hidehiko & Todd, Petra E, 1997. "Matching as an Econometric Evaluation Estimator: Evidence from Evaluating a Job Training Programme," Review of Economic Studies, Wiley Blackwell, vol. 64(4), pages 605-54, October.
  3. Geroski, P. A., 2000. "Models of technology diffusion," Research Policy, Elsevier, vol. 29(4-5), pages 603-625, April.
  4. Karshenas, Massoud & Stoneman, Paul, 1990. "Rank, Stock, Order And Epidemic Effects In The Diffusion Of New Process Technologies : An Empirical Model," The Warwick Economics Research Paper Series (TWERPS) 358, University of Warwick, Department of Economics.
  5. Ajay Agrawal & Avi Goldfarb, 2008. "Restructuring Research: Communication Costs and the Democratization of University Innovation," American Economic Review, American Economic Association, vol. 98(4), pages 1578-90, September.
  6. Alberto Abadie & David Drukker & Jane Leber Herr & Guido W. Imbens, 2004. "Implementing matching estimators for average treatment effects in Stata," Stata Journal, StataCorp LP, vol. 4(3), pages 290-311, September.
  7. Downes, Tom & Greenstein, Shane, 2002. "Universal access and local internet markets in the US," Research Policy, Elsevier, vol. 31(7), pages 1035-1052, September.
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Citations

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Cited by:
  1. Christoph Safferling & Aaron Lowen, 2011. "Economics in the Kingdom of Loathing: Analysis of Virtual Market Data," Working Paper Series of the Department of Economics, University of Konstanz 2011-30, Department of Economics, University of Konstanz.
  2. Beck, Jonathan, 2008. "Diderot´s rule," Discussion Papers, Research Unit: Competition and Innovation SP II 2008-13, Social Science Research Center Berlin (WZB).
  3. Marco Cioppi, 2013. "The role of web communication to enhance the value of retail trade in small cities," The International Journal of Economic Behavior - IJEB, Faculty of Business and Administration, University of Bucharest, vol. 3(1), pages 127-145, December.

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