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Ushering Buyers into Electronic Channels: An Empirical Analysis

Author

Listed:
  • Nishtha Langer

    (Indian School of Business, Hyderabad, Andhra Pradesh, 500032, India)

  • Chris Forman

    (College of Management, Georgia Institute of Technology, Atlanta, Georgia 30308)

  • Sunder Kekre

    (Tepper School of Business, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213)

  • Baohong Sun

    (Cheung Kong Graduate School of Business, New York 10019, New York)

Abstract

Despite many success stories, B2B e-commerce penetration remains low. Many firms introduce electronic channels in addition to their traditional sales channels but find that buyer usage of the e -channel over time does not keep up with initial expectations. Firms must understand the underlying factors that drive channel usage and how these factors change over time and across buyers. Using panel data pertaining to the purchase histories of 683 buyers over a 43-month period, we estimate a dynamic discrete choice model in a B2B setting that (i) recognizes how price, channel inertia, and inventory change over time; (ii) allows buyers to dynamically trade off these factors when making e -channel adoption decisions; and (iii) takes into account buyer heterogeneity. We find that channel usage is both heterogeneous and dynamic across buyers. Our findings reveal the dynamic tradeoff between channel inertia and the adverse price effect, which interact in opposing directions as the e -channel grows more popular over time: price increases resulting from more bids deter buyers, whereas channel inertia built from sampling experience helps retain repeat buyers for the new channel. Second, we find that the buyers' size and diversity influence purchase decisions, and the e -channel appears more attractive to small and/or diversified buyers. Based on our analysis, we postulate that the seller's allocation decisions of products across channels, if not aligned with buyer behavior, can alienate some buyers. Based on the parameter estimates from the buyer response model, we propose an improved channel allocation that enables firms to selectively attract more buyers to the e -channel and improve revenues. Channel acceptance increases as a result of smart allocation when firms understand and account for individual buyers' channel usage behavior.

Suggested Citation

  • Nishtha Langer & Chris Forman & Sunder Kekre & Baohong Sun, 2012. "Ushering Buyers into Electronic Channels: An Empirical Analysis," Information Systems Research, INFORMS, vol. 23(4), pages 1212-1231, December.
  • Handle: RePEc:inm:orisre:v:23:y:2012:i:4:p:1212-1231
    DOI: 10.1287/isre.1110.0410
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    3. Ajay Agrawal & Joshua S. Gans & Avi Goldfarb, 2023. "Similarities and Differences in the Adoption of General Purpose Technologies," NBER Chapters, in: Technology, Productivity, and Economic Growth, National Bureau of Economic Research, Inc.
    4. May Truong & Alok Gupta & Wolfgang Ketter & Eric van Heck, 2022. "Buyers’ Strategic Behavior in B2B Multichannel Auction Markets: When an Online Posted Price Channel Is Incorporated into a Dutch Auction System," Information Systems Research, INFORMS, vol. 33(4), pages 1344-1367, December.
    5. Wedad Elmaghraby & Anandasivam Gopal & Ali Pilehvar, 2012. "Reference Prices and Bidder Heterogeneity in Secondary Market Online B2B Auctions," Working Papers 12-06, NET Institute, revised Sep 2012.
    6. Ke Gong & Yi Peng & Yong Wang & Maozeng Xu, 2018. "Time series analysis for C2C conversion rate," Electronic Commerce Research, Springer, vol. 18(4), pages 763-789, December.
    7. Richard, James E. & Purnell, Fruen, 2017. "Rethinking Catalogue and Online B2B Buyer Channel Preferences in the Education Supplies Market," Journal of Interactive Marketing, Elsevier, vol. 37(C), pages 1-15.

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