Who should pay for return freight in the online retailing? Retailers or consumers

  • Xiaomin ZhaoEmail author
  • Shuhui Hu
  • Xiaoxiao Meng


This paper studies the online shopping situation where retailer faces uncertain demand and uncertain consumer valuations. We compare the suitability and effectiveness of two return freight policies, consumer affording return freight (C-Policy) and retailer paying return freight (R-Policy). Moreover, we explore the effect of non-defective returns, and the retailer’s optimal decisions on retail price and order quantity. Our results suggest that return freight policy is related to the actual quantity of returns and the proportion of non-defective returns. In general, R-Policy is reasonable when the actual returns is low, C-Policy is reasonable when the actual returns is high. But when the actual returns is not too low or too high, the return freight policy is closely related to the proportion of non-defective returns. Our study shows C-Policy is better if the proportion of non-defective returns is lower, otherwise R-Policy is better. In addition, we find the optimal retail price and the optimal order quantity decisions are also related to the actual returns and the proportion of non-defective returns. Usually, the higher the actual returns, the lower the optimal retail price and the more optimal order quantity. The higher the proportion of non-defective returns, the higher the optimal retail price and the less the optimal order quantity. At last, we find high returns are harmful to retailers, which erode the profitability of online retailers. However, an interesting observation is that the damage of high returns can be alleviated when most of returns are non-defective returns. That indicates the risk of high returns is not as terrible as we intuitively think.


Online retailing Consumer returns Demand uncertainty Valuation uncertainty Return freight policy 



We wish to express our sincerest thanks to the editors and anonymous reviewers for their constructive comments and suggestions on the earlier versions of the paper. We also gratefully acknowledge the support of grants from Philosophy and Social Sciences Plan General Project of Shanghai (No. 2014BGL005).


  1. 1.
    Batarfi, R., Jaber, M. Y., & Aljazzar, S. M. (2017). A profit maximization for a reverse logistics dual-channel supply chain with a return policy. Computers & Industrial Engineering, 106, 58–82.CrossRefGoogle Scholar
  2. 2.
    Bower, A. B., & Maxham, J. G. (2012). Return shipping policies of online retailers: Normative assumptions and the long-term consequences of fee and free returns. Journal of Marketing, 76(5), 110–124.CrossRefGoogle Scholar
  3. 3.
    Brill, J. (2015). Rethinking online returns. UPS.Google Scholar
  4. 4.
    CNNIC. (2016). Report on China’s online shopping market in 2015. Accessed 30 June 2018
  5. 5.
    Chen, J., & Bell, P. C. (2011). Coordinating a decentralized supply chain with customer returns and price-dependent stochastic demand using a buyback policy. European Journal of Operational Research, 212(2), 293–300.CrossRefGoogle Scholar
  6. 6.
    Chen, J., & Chen, B. T. (2016). Competing with customer returns policies. International Journal of Production Research, 54(7), 2093–2107.CrossRefGoogle Scholar
  7. 7.
    Ferguson, M., Guide, V. D. R., & Souza, G. C. (2006). Supply chain coordination for false failure returns. Manufacturing & Service Operations Management, 8(4), 376–393.CrossRefGoogle Scholar
  8. 8.
    Geng, S. D., & Li, W. L. (2017). Complimentary return-freight insurance serves as quality signal or noise? In: Australasian conference on information systems.Google Scholar
  9. 9.
    Griffis, S. E., Rao, S., Goldsby, T. J., & Niranjan, T. T. (2012). The customer consequences of returns in online retailing: An empirical analysis. Journal of Operations Management, 30(4), 282–294.CrossRefGoogle Scholar
  10. 10.
    He, Y., Zhao, X., Zhao, L. D., & He, J. (2009). Coordinating a supply chain with effort and price dependent stochastic demand. Applied Mathematical Modelling, 33(6), 2777–2790.CrossRefGoogle Scholar
  11. 11.
    Hua, Z. S., Hou, H. J., & Bian, Y. W. (2016). Optimal shipping strategy and return service charge under no-reason return policy in online retailing. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 47(12), 3189–3206.CrossRefGoogle Scholar
  12. 12.
    Kang, M., & Johnson, K. (2009). Identifying characteristics of consumers who frequently return apparel. Journal of Fashion Marketing and Management, 13(1), 37–48.CrossRefGoogle Scholar
  13. 13.
    Lantz, B., & Hjort, K. (2013). Real e-customer behavioural responses to free delivery and free returns. Electronic Commerce Research, 13, 183–198.CrossRefGoogle Scholar
  14. 14.
    Leng, M., & Becerril-Arreola, R. (2010). Joint pricing and contingent frees hipping decisions in B2C transactions. Production and Operations Management, 19(4), 390–405.CrossRefGoogle Scholar
  15. 15.
    Leng, M., & Parlar, M. (2005). Free shipping and purchasing decisions in B2B transactions: A game-theoretic analysis. IIE Transactions, 37(12), 1119–1128.CrossRefGoogle Scholar
  16. 16.
    Li, Y. J., Xu, L., & Li, D. H. (2013). Examining relationships between the return policy, product quality, and pricing strategy in online direct selling. International Journal of Production Economics, 144(2), 451–460.CrossRefGoogle Scholar
  17. 17.
    Mukhopadhyay, S. K., & Setoputro, R. (2004). Reverse logistics in e-business. International Journal of Physical Distribution & Logistics Management, 34(1), 70–89.CrossRefGoogle Scholar
  18. 18.
    Mukhopadhyay, S. K., & Setoputro, R. (2005). Optimal return policy and modular design for build-to-order products. Journal of Operations Management, 23(5), 496–506.CrossRefGoogle Scholar
  19. 19.
    Mukhopadhyay, S. K., & Setaputra, R. (2007). A dynamic model for optimal design quality and return policies. European Journal of Operational Research, 180(3), 1144–1154.CrossRefGoogle Scholar
  20. 20.
    Pei, Z., Paswan, A., & Yan, R. (2014). E-tailer’s return policy, consumer’s perception of return policy fairness and purchase intention. Journal of Retailing and Consumer Services, 21(3), 249–257.CrossRefGoogle Scholar
  21. 21.
    Shang, G. Z., Pekgün, P., Ferguson, M., & Galbreth, M. (2017). How much do online consumers really value free product returns? Evidence from eBay. Journal of Operations Management, 53–56, 45–62.CrossRefGoogle Scholar
  22. 22.
    Shi, X. T., Dong, C. W., & Cheng, T. (2018). Does the buy-online-and-pick-up-in-store strategy with pre-orders benefit a retailer with the consideration of returns? International Journal of Production Economics, 206, 134–145.CrossRefGoogle Scholar
  23. 23.
    Stock, J. R., & Mulki, J. P. (2009). Product returns processing: An examination of practices of manufacturers, wholesalers/distributors, and retailers. Journal of Business Logistics, 30(1), 33–62.CrossRefGoogle Scholar
  24. 24.
    Su, X. M. (2009). Consumer returns policies and supply chain performance. Manufacturing & Service Operations Management, 11(4), 595–612.CrossRefGoogle Scholar
  25. 25.
    Swinney, R. (2011). Selling to strategic consumers when product value is uncertain: The value of matching supply and demand. Management Science, 57(10), 1737–1751.CrossRefGoogle Scholar
  26. 26.
    UPS and comScore. (2014). Pulse of the online shopper: A customer experience study. September 15.Google Scholar
  27. 27.
    UPS and comScore. (2015). Pulse of the online shopper: Empowered shoppers propel retail change. October 27.Google Scholar
  28. 28.
    Xiao, T. J., Shi, K. R., & Yang, D. Q. (2010). Coordination of a supply chain with consumer return under demand uncertainty. International Journal of Production Economics, 124(1), 171–180.CrossRefGoogle Scholar
  29. 29.
    Xu, L., Li, Y., Govindan, K., & Xu, X. (2015). Consumer returns policies with endogenous deadline and supply chain coordination. European Journal of Operational Research, 242(1), 88–99.CrossRefGoogle Scholar
  30. 30.
    Yang, S. (2012). Customers’ cognition value and analysis of return freight insurance. Journal of Insurance Professional College, 26(3), 21–23.Google Scholar
  31. 31.
    Zhang, J. (2013). Revenue maximizing with return policy when buyers have uncertain valuations. International Journal of Industrial Organization, 31(5), 452–461.CrossRefGoogle Scholar
  32. 32.
    Zhang, J. L., Chen, J., & Lee, C. Y. (2008). Joint optimization on pricing, promotion and inventory control with stochastic demand. International Journal of Production Economics, 116(2), 190–198.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of ManagementShanghai UniversityShanghaiChina

Personalised recommendations