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Demand forecast information sharing in the competitive online and traditional retailers

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  • Yan, Ruiliang

Abstract

An important strategic issue for retailing business managers to study is the information strategy. In this paper, we develop a profit-maximization model to investigate the benefits of demand forecast information sharing for the competitive online and traditional retailers with the consideration of the compatibility of the product with online marketing. Both retailers use a Bertrand model to compete. We analyze and compare two scenarios: (1) when forecast information is not shared between the online and traditional retailers; (2) when forecast information is shared between the online and traditional retailers. Our results show that both the online and traditional retailers will be better off from information sharing. Especially when the channel forecast is less accurate, the product is more compatible with online marketing, and the market is more volatile, both retailers will profit more. Based on our results, optimal strategies are derived and probable paths of future research are identified.

Suggested Citation

  • Yan, Ruiliang, 2010. "Demand forecast information sharing in the competitive online and traditional retailers," Journal of Retailing and Consumer Services, Elsevier, vol. 17(5), pages 386-394.
    • Handle: RePEc:eee:joreco:v:17:y:2010:i:5:p:386-394
    DOI: 10.1016/j.jretconser.2010.03.019
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    Cited by:

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    5. Rofin T.M. & Umakanta Mishra & Jei-Zheng Wu, 2022. "Performance of Channel Members under Emission-Sensitive Demand for Green Supply Chain Management: A Game Theory Approach," Mathematics, MDPI, vol. 10(11), pages 1-23, May.
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