IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i18p11748-d918773.html
   My bibliography  Save this article

Optimal Return Freight Insurance Policies in a Competitive Environment

Author

Listed:
  • Xiang Li

    (School of Economics and Management, Beijing Science and Technology University, Beijing 100083, China)

  • Shu Zhou

    (School of Management, Xiamen University, Xiamen 361005, China)

  • Guojun Ji

    (School of Management, Xiamen University, Xiamen 361005, China)

  • Weina Shi

    (School of Management, Xiamen University, Xiamen 361005, China)

Abstract

In recent years, return freight insurance (RFI) has emerged as a solution to the problem of returns of goods purchased online. However, although RFI reduces the return costs of both parties and increases the purchase intention of consumers, it also increases the rate of returns and reduces retailers’ profits. Therefore, some online retailers have looked at increasing their service effort as a means of improving the service level and reducing the rate of returns. Considering the impact of retailers’ service efforts on consumer returns, the retailers’ choice of RFI strategy is very important for its profit. It is worth studying how retailers choose RFI policies and the pricing and service effort level in different market environments. In this study, we examine the retailers’ RFI decision-making process, including the influence of retail service effort on consumer returns, by developing three duopolistic-competition game models based on three RFI markets. In this case, we analyze and compare the retailers’ optimal pricing decisions, optimal service effort decisions, and optimal profits in each RFI market, and identify the relationship between the retailers’ optimal decisions and the degree of competition. The result shows that under the market where RFI is provided by the retailer, the retailers’ optimal pricing and optimal service effort level both increase with the increase of market competition. In addition, retailers should consider the impact of market competition and return compensation on consumer demand when making the decision of whether to offer freight insurance.

Suggested Citation

  • Xiang Li & Shu Zhou & Guojun Ji & Weina Shi, 2022. "Optimal Return Freight Insurance Policies in a Competitive Environment," Sustainability, MDPI, vol. 14(18), pages 1-38, September.
  • Handle: RePEc:gam:jsusta:v:14:y:2022:i:18:p:11748-:d:918773
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/18/11748/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/14/18/11748/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Xiaomin Zhao & Shuhui Hu & Xiaoxiao Meng, 2020. "Who should pay for return freight in the online retailing? Retailers or consumers," Electronic Commerce Research, Springer, vol. 20(2), pages 427-452, June.
    2. Hao Wang, 2004. "Do Returns Policies Intensify Retail Competition?," Marketing Science, INFORMS, vol. 23(4), pages 611-613, March.
    3. Mehmet Sekip Altug & Tolga Aydinliyim, 2016. "Counteracting Strategic Purchase Deferrals: The Impact of Online Retailers’ Return Policy Decisions," Manufacturing & Service Operations Management, INFORMS, vol. 18(3), pages 376-392, July.
    4. Lin, Jiaxin & Zhang, Juliang & Cheng, T.C.E., 2020. "Optimal pricing and return policy and the value of freight insurance for a retailer facing heterogeneous consumers with uncertain product values," International Journal of Production Economics, Elsevier, vol. 229(C).
    5. Xuanming Su, 2009. "Consumer Returns Policies and Supply Chain Performance," Manufacturing & Service Operations Management, INFORMS, vol. 11(4), pages 595-612, March.
    6. Li, Yongjian & Xu, Lei & Li, Dahui, 2013. "Examining relationships between the return policy, product quality, and pricing strategy in online direct selling," International Journal of Production Economics, Elsevier, vol. 144(2), pages 451-460.
    7. Ji, Guojun & Zhou, Shu & Lai, Kee-Hung & Tan, Kim Hua & Kumar, Ajay, 2022. "Timing of blockchain adoption in a supply chain with competing manufacturers," International Journal of Production Economics, Elsevier, vol. 247(C).
    8. Ioannis Bellos & Stylianos Kavadias, 2021. "Service Design for a Holistic Customer Experience: A Process Framework," Management Science, INFORMS, vol. 67(3), pages 1718-1736, March.
    9. Chen, Zhongwei & Fan, Zhi-Ping & Zhao, Xuan, 2021. "Offering return-freight insurance or not: Strategic analysis of an e-seller's decisions," Omega, Elsevier, vol. 103(C).
    10. Xing Hu & Zhixi Wan & Nagesh N. Murthy, 2019. "Dynamic Pricing of Limited Inventories with Product Returns," Manufacturing & Service Operations Management, INFORMS, vol. 21(3), pages 501-518, July.
    11. Guangzhi Shang & Bikram P. Ghosh & Michael R. Galbreth, 2017. "Optimal Retail Return Policies with Wardrobing," Production and Operations Management, Production and Operations Management Society, vol. 26(7), pages 1315-1332, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Shuiwang Zhang & Qianlan Ding & Jingcheng Ding, 2023. "Return Strategy of E-Commerce Platform Based on Green and Sustainable Development," Sustainability, MDPI, vol. 15(14), pages 1-18, July.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Khouja, Moutaz & Hammami, Ramzi, 2023. "Optimizing price, order quantity, and return policy in the presence of consumer opportunistic behavior for online retailers," European Journal of Operational Research, Elsevier, vol. 309(2), pages 683-703.
    2. Duong, Quang Huy & Zhou, Li & Meng, Meng & Nguyen, Truong Van & Ieromonachou, Petros & Nguyen, Duy Tiep, 2022. "Understanding product returns: A systematic literature review using machine learning and bibliometric analysis," International Journal of Production Economics, Elsevier, vol. 243(C).
    3. Yang, Guangyong & Ji, Guojun, 2022. "The impact of cross-selling on managing consumer returns in omnichannel operations," Omega, Elsevier, vol. 111(C).
    4. Guangyong Yang & Guojun Ji & Kim Hua Tan, 2022. "Impact of artificial intelligence adoption on online returns policies," Annals of Operations Research, Springer, vol. 308(1), pages 703-726, January.
    5. Fan, Huirong & Khouja, Moutaz & Zhou, Jing, 2022. "Design of win-win return policies for online retailers," European Journal of Operational Research, Elsevier, vol. 301(2), pages 675-693.
    6. Buqing Ma & Chenchen Di & Lu Hsiao, 2020. "Return Window Decision in A Distribution Channel," Production and Operations Management, Production and Operations Management Society, vol. 29(9), pages 2121-2137, September.
    7. Lin, Jiaxin & Choi, Tsan-Ming & Kuo, Yong-Hong, 2023. "Will providing return-freight-insurances do more good than harm to dual-channel e-commerce retailers?," European Journal of Operational Research, Elsevier, vol. 307(3), pages 1225-1239.
    8. Chen, Hao-Wei, 2023. "Improving supply quality through the store-initiated returns in wholesale supply chains," International Journal of Production Economics, Elsevier, vol. 261(C).
    9. Ren, Minglun & Liu, Jiqiong & Feng, Shuai & Yang, Aifeng, 2021. "Pricing and return strategy of online retailers based on return insurance," Journal of Retailing and Consumer Services, Elsevier, vol. 59(C).
    10. Shi, Xiutian & Dong, Ciwei & Cheng, T.C.E., 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, Elsevier, vol. 206(C), pages 134-145.
    11. Cui, Hailong & Rajagopalan, Sampath & Ward, Amy R., 2020. "Predicting product return volume using machine learning methods," European Journal of Operational Research, Elsevier, vol. 281(3), pages 612-627.
    12. Vineet Kaushik & Ashwani Kumar & Himanshu Gupta & Gaurav Dixit, 2022. "Modelling and prioritizing the factors for online apparel return using BWM approach," Electronic Commerce Research, Springer, vol. 22(3), pages 843-873, September.
    13. Park, YoungSoo & Sim, Jeongeun & Kim, Bosung, 2022. "Online retail operations with “Try-Before-You-Buy”," European Journal of Operational Research, Elsevier, vol. 299(3), pages 987-1002.
    14. Chong Zhang & Man Yu & Jian Chen, 2022. "Signaling Quality with Return Insurance: Theory and Empirical Evidence," Management Science, INFORMS, vol. 68(8), pages 5847-5867, August.
    15. Wenting Pan & Candice H. Huynh, 2023. "Optimal operational strategies for online retailers with demand and return uncertainty," Operations Management Research, Springer, vol. 16(2), pages 755-767, June.
    16. Li, Yiming & Li, Gang & Tayi, Giri Kumar & Cheng, T.C.E., 2021. "Return shipping insurance: Free versus for-a-fee?," International Journal of Production Economics, Elsevier, vol. 235(C).
    17. Chen, Zhongwei & Fan, Zhi-Ping & Zhu, Stuart X., 2023. "Extracting values from consumer returns: The role of return-freight insurance for competing e-sellers," European Journal of Operational Research, Elsevier, vol. 306(1), pages 141-155.
    18. Zhang, Chong & Yu, Man & Chen, Jian, 2022. "Signaling quality with return insurance: Theory and empirical evidence," Other publications TiSEM 184da313-a89e-4a81-9f23-c, Tilburg University, School of Economics and Management.
    19. Necati Ertekin & Michael E. Ketzenberg & Gregory R. Heim, 2020. "Assessing Impacts of Store and Salesperson Dimensions of Retail Service Quality on Consumer Returns," Production and Operations Management, Production and Operations Management Society, vol. 29(5), pages 1232-1255, May.
    20. Xiaomin Zhao & Shuhui Hu & Xiaoxiao Meng, 2020. "Who should pay for return freight in the online retailing? Retailers or consumers," Electronic Commerce Research, Springer, vol. 20(2), pages 427-452, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:14:y:2022:i:18:p:11748-:d:918773. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.