Optimal Reverse Channel Structure for Consumer Product Returns

Consumers often return a product to a retailer because they learn after purchase that the product does not match as well with preferences as had been expected. This is a costly issue for retailers and manufacturers---in fact, it is estimated that the U.S. electronics industry alone spent $13.8 billion dollars in 2007 to restock returned products [Lawton, C. 2008. The war on returns. Wall Street Journal (May 8) D1]. The bulk of these returns were nondefective items that simply were not what the consumer wanted. To eliminate returns and to recoup the cost of handling returns, many retailers are adopting the practice of charging restocking fees to consumers as a penalty for making returns. This paper employs an analytical model of a bilateral monopoly to examine the impact of reverse channel structure on the equilibrium return policy and profit. More specifically, we examine how the return penalty is affected by whether returns are salvaged by the manufacturer or by the retailer. Interestingly, we find that the return penalty may be more severe when returns are salvaged by a channel member who derives greater value from a returned unit. Also, the manufacturer may earn greater profit by accepting returns even if the retailer has a more efficient outlet for salvaging units.

[1]  P. Nelson Information and Consumer Behavior , 1970, Journal of Political Economy.

[2]  A. Tversky,et al.  Prospect theory: analysis of decision under risk , 1979 .

[3]  S. Gilbert,et al.  Note. the Role of Returns Policies in Pricing and Inventory Decisions for Catalogue Goods , 1998 .

[4]  Barry Alan Pasternack,et al.  Optimal Pricing and Return Policies for Perishable Commodities , 2008, Mark. Sci..

[5]  A. Tversky,et al.  Prospect theory: an analysis of decision under risk — Source link , 2007 .

[6]  Johan Marklund,et al.  Vertical Information Sharing in a Volatile Market , 2006 .

[7]  Karsten Hansen,et al.  The Option Value of Returns: Theory and Empirical Evidence , 2009, Mark. Sci..

[8]  Wujin Chu,et al.  Managing Dissatisfaction , 1998 .

[9]  Zsolt Katona,et al.  "Bricks and Clicks": The Impact of Product Returns on the Strategies of Multichannel Retailers , 2011, Mark. Sci..

[10]  Vineet Padmanabhan,et al.  Reply to Do Returns Policies Intensify Retail Competition , 2004 .

[11]  Chuan He,et al.  Research Note - Vertical Information Sharing in a Volatile Market , 2008, Mark. Sci..

[12]  Gary H. Chao,et al.  Quality Improvement Incentives and Product Recall Cost Sharing Contracts , 2009, Manag. Sci..

[13]  Eric T. Anderson,et al.  A Bargaining Theory of Distribution Channels , 2003 .

[14]  Michael R. Hagerty,et al.  Return Policies and the Optimal Level of "Hassle" , 1998 .

[15]  Eugene Kandel The Right to Return , 1996, The Journal of Law and Economics.

[16]  Yeon-Koo Che Customer Return Policies for Experience Goods , 1996 .

[17]  Hao Wang Do Returns Policies Intensify Retail Competition , 2004 .

[18]  Steven A. Matthews,et al.  Information Acquisition and the Excess Refund Puzzle , 2005 .

[19]  Gérard P. Cachon Supply Chain Coordination with Contracts , 2003, Supply Chain Management.

[20]  Scott M. Davis,et al.  Money back guarantees in retailing: matching products to consumer tastes , 1995 .

[21]  Jeffrey D. Shulman,et al.  Optimal Restocking Fees and Information Provision in an Integrated Demand-Supply Model of Product Returns , 2009, Manuf. Serv. Oper. Manag..

[22]  I. Png,et al.  Manufacturer's Return Policies and Retail Competition , 1997 .

[23]  Anil Arya,et al.  Using Return Polices to Elicit Retailer Information , 2004 .

[24]  Ronald S. Tibben-Lembke,et al.  Going Backwards: Reverse Logistics Trends and Practices , 1999 .