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Customer-Driven vs. Retailer-Driven Search: Channel Performance and Implications


  • Li Jiang

    () (Department of Logistics and Maritime Studies, Faculty of Business, Hong Kong Polytechnic University, Hong Kong SAR, China)

  • Ravi Anupindi

    () (Stephen M. Ross School of Business, University of Michigan, Ann Arbor, Michigan 48109)


A common phenomenon that occurs in any decentralized multilocation system is stock imbalance, whereby some locations have unsatisfied demands while others are overstocked. The system can be rebalanced by using a search process that is driven by either the customers or the retailers. In a customer-driven search (CDS), the customer with unmet demand may search for the product at another location and, if it is available, complete the purchase. In a retailer-driven search (RDS), the retailer with unsatisfied demand searches for product and schedules transshipment to fulfill the unmet demand at his location. Of course, the revenues generated through search in RDS need to be shared between the parties according to a transfer pricing scheme. In a setting of one manufacturer and two retailers with price-dependent and random demand, we explore the impact of the search method and the transfer price scheme used on the preferences of the manufacturer, the retailers, and the customers. With endogenous retail prices, we find that both the manufacturer and the retailers prefer RDS over CDS when they can design the transfer pricing scheme in RDS. Interestingly, neither party prefers the fixed transfer pricing scheme commonly assumed in the literature. Instead, transfer price that is proportional to the price of the retailer with either excess stock or excess demand is preferred. However, although both parties favor an RDS system when they can design the transfer pricing scheme in RDS, they may prefer RDS or CDS when the other party designs the RDS. Thus, the interests of the manufacturer and the retailers are rarely aligned. Customers benefit from a lower price in an RDS but at the expense of lower availability (as measured by the level of safety stock).

Suggested Citation

  • Li Jiang & Ravi Anupindi, 2010. "Customer-Driven vs. Retailer-Driven Search: Channel Performance and Implications," Manufacturing & Service Operations Management, INFORMS, vol. 12(1), pages 102-119, January.
  • Handle: RePEc:inm:ormsom:v:12:y:2010:i:1:p:102-119

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    References listed on IDEAS

    1. GĂ©rard P. Cachon & Christian Terwiesch & Yi Xu, 2008. "On the Effects of Consumer Search and Firm Entry in a Multiproduct Competitive Market," Marketing Science, INFORMS, vol. 27(3), pages 461-473, 05-06.
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    6. Xinxin Hu & Izak Duenyas & Roman Kapuscinski, 2007. "Existence of Coordinating Transshipment Prices in a Two-Location Inventory Model," Management Science, INFORMS, vol. 53(8), pages 1289-1302, August.
    7. Dana, James D, Jr, 2001. "Competition in Price and Availability When Availability is Unobservable," RAND Journal of Economics, The RAND Corporation, vol. 32(3), pages 497-513, Autumn.
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    1. repec:eee:transe:v:106:y:2017:i:c:p:78-97 is not listed on IDEAS
    2. Ji, Xiang & Sun, Jiasen & Wang, Zebin, 2017. "Turn bad into good: Using transshipment-before-buyback for disruptions of stochastic demand," International Journal of Production Economics, Elsevier, vol. 185(C), pages 150-161.
    3. Liao, Yi & Shen, Wenjing & Hu, Xinxin & Yang, Shilei, 2014. "Optimal responses to stockouts: Lateral transshipment versus emergency order policies," Omega, Elsevier, vol. 49(C), pages 79-92.
    4. Jing Shao & Harish Krishnan & S. Thomas McCormick, 2011. "Incentives for Transshipment in a Supply Chain with Decentralized Retailers," Manufacturing & Service Operations Management, INFORMS, vol. 13(3), pages 361-372, July.


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