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Market Structure, Uncertainty, and Intrafirm Diffusion: The Case of Optical Scanners in Grocery Stores

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  • Levin, Sharon G
  • Levin, Stanford L
  • Meisel, John B

Abstract

This study uses monthly data on the adoption of optical scanners by sixty-three grocery chains in thirty-two large U.S. cities to identify the determinants of the rate of intrafirm diffusion. The methodology involves a two-stage approach that relates market environment characteristics to the estimated rate of intrafirm diffusion. The results indicate that firms with larger market shares adopt a new innovation (scanners) more quickly initially but diffuse the innovation through their stores more slowly than firms with smaller market shares. In addition, firms that lag competitors in the initial adoption of scanners tend to diffuse the innovation more quickly. Copyright 1992 by MIT Press.

Suggested Citation

  • Levin, Sharon G & Levin, Stanford L & Meisel, John B, 1992. "Market Structure, Uncertainty, and Intrafirm Diffusion: The Case of Optical Scanners in Grocery Stores," The Review of Economics and Statistics, MIT Press, vol. 74(2), pages 345-350, May.
    • Handle: RePEc:tpr:restat:v:74:y:1992:i:2:p:345-50
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    Cited by:

    1. Hairault, Jean-Olivier & Langot, Francois & Portier, Franck, 1997. "Time to implement and aggregate fluctuations," Journal of Economic Dynamics and Control, Elsevier, vol. 22(1), pages 109-121, November.
    2. Emek Basker, 2012. "Raising the Barcode Scanner: Technology and Productivity in the Retail Sector," NBER Chapters,in: Standards, Patents and Innovations National Bureau of Economic Research, Inc.
    3. Raghuram Iyengar & Christophe Van den Bulte & Jae Young Lee, 2015. "Social Contagion in New Product Trial and Repeat," Marketing Science, INFORMS, vol. 34(3), pages 408-429, May.
    4. Robert S. Huckman, 2003. "The Utilization of Competing Technologies Within the Firm: Evidence from Cardiac Procedures," Management Science, INFORMS, vol. 49(5), pages 599-617, May.
    5. Rossella Argenziano & Philipp Schmidt-Dengler, 2014. "Clustering In N-Player Preemption Games," Journal of the European Economic Association, European Economic Association, vol. 12(2), pages 368-396, April.
    6. Linda Argote & Henrich R. Greve, 2007. "A Behavioral Theory of the Firm ---40 Years and Counting: Introduction and Impact," Organization Science, INFORMS, vol. 18(3), pages 337-349, June.
    7. Jonathan Beck & Michal Grajek & Christian Wey, 2011. "Estimating level effects in diffusion of a new technology: barcode scanning at the checkout counter," Applied Economics, Taylor & Francis Journals, vol. 43(14), pages 1737-1748.
    8. Emek Basker, 2015. "Change at the Checkout: Tracing the Impact of a Process Innovation," Journal of Industrial Economics, Wiley Blackwell, vol. 63(2), pages 339-370, June.
    9. Fuentelsaz, Lucio & Gómez, Jaime & Palomas, Sergio, 2016. "Interdependences in the intrafirm diffusion of technological innovations: Confronting the rational and social accounts of diffusion," Research Policy, Elsevier, vol. 45(5), pages 951-963.
    10. repec:esx:essedp:741 is not listed on IDEAS
    11. Battisti, Giuliana & Stoneman, Paul, 2005. "The intra-firm diffusion of new process technologies," International Journal of Industrial Organization, Elsevier, vol. 23(1-2), pages 1-22, February.
    12. Fuentelsaz, Lucio & Gomez, Jaime & Polo, Yolanda, 2003. "Intrafirm diffusion of new technologies: an empirical application," Research Policy, Elsevier, vol. 32(4), pages 533-551, April.
    13. James G. Mulligan & Nilotpal Das, 2005. "Persistent Adoption of Time-Saving Process Innovations," Working Papers 05-03, University of Delaware, Department of Economics.
    14. Rossella Argenziano & Philipp Schmidt-Dengler, 2014. "Clustering In N-Player Preemption Games," Journal of the European Economic Association, European Economic Association, vol. 12(2), pages 368-396, 04.

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