 Demand forecast information sharing in the competitive online and traditional retailersRuiliang Yan Journal of Retailing and Consumer Services, 2010, vol. 17, issue 5, 386-394 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. Keywords: Retailing; Forecasting; Information sharing; Marketing channels; Uncertainty modeling; Bertrand competition (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:joreco:v:17:y:2010:i:5:p:386-394 DOI: 10.1016/j.jretconser.2010.03.019 Access Statistics for this article Journal of Retailing and Consumer Services is currently edited by Harry Timmermans More articles in Journal of Retailing and Consumer Services from Elsevier |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.