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Reducing ecommerce returns with return credits

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An Author Correction to this article was published on 21 January 2023

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Abstract

Massive ecommerce returns incur considerable costs for e-retailers, erode their competitiveness and make their product returns management complex and difficult. Reducing returns can help e-retailers mitigate these negative consequences. This article focuses on ecommerce returns due to satisfaction-related reasons, the most common reasons for ecommerce returns, and studied the use of return credits (a maximum free returns amount) to reduce these kinds of returns. This novel approach is different from full or partial return policy documented in existing literature. This article also studied the side effects of using return credits. A one-factor (credit amount: high vs. low) between-subject scenario experiment was conducted. ANOVA was used to test hypotheses. The results revealed that using return credits can significantly deter returns, while the high and low credit amount have a similar effect on deterring returns. Moreover, the high credit amount leads to weaker side effects than the low amount. These findings can help e-retailers decide whether to introduce return credits to manage returns, and help them design their return credits.

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Data availability

The questionnaire data that support the findings of this study are not openly available because the authors do not have the authority to disclose and share the personal data.

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Notes

  1. Gäthke et al. [98] studied the relationship between return restrictiveness and repurchase intention among online consumers, but they focused on effort restrictiveness and refund types (money back vs. store credit) from which our research are quite different.

References

  1. Li, M., & Choudhury, A. H. (2021). Using website information to reduce postpurchase dissonance: A mediated moderating role of perceived risk. Psychology & marketing, 38(1), 56–69.

    Google Scholar 

  2. Chevalier, S. (2022). Retail e-commerce sales worldwide from 2014 to 2025. Retrieved 15 August 2022 from https://www.statista.com/statistics/379046/worldwide-retail-e-commerce-sales/

  3. Ahsan, K., & Rahman, S. (2021). A systematic review of e-tail product returns and an agenda for future research. Industrial Management & Data Systems, 122(1), 137–166.

    Google Scholar 

  4. Serravalle, F., Vannucci, V., & Pantano, E. (2022). “Take it or leave it?”: Evidence on cultural differences affecting return behaviour for Gen Z. Journal of Retailing and Consumer Services, 66, 102942.

    Google Scholar 

  5. Duong, Q. H., Zhou, L., Meng, M., Van Nguyen, T., Ieromonachou, P., & Nguyen, D. T. (2022). Understanding product returns: A systematic literature review using machine learning and bibliometric analysis. International Journal of Production Economics, 243, 108340.

    Google Scholar 

  6. Djordjevic, M. (2021). 18 ecommerce return rate statistics for an enhanced shopping experience in 2021. Retrieved 16 April 2021 from https://savemycent.com/ecommerce-return-rate-statistics/

  7. ApprissRetail. (2022). Consumer returns in the retail industry 2021. Retrieved 15 August 2022 from https://cdn.nrf.com/sites/default/files/2022-01/Customer%20Returns%20in%20the%20Retail%20Industry%202021.pdf

  8. CBRE. (2021). ’Tis the stressful season for holiday gift returns. Retrieved 15 August 2022 from https://www.cbre.com/insights/viewpoints/reverse-logistics-tis-the-stressful-season-for-holiday-gift-returns

  9. Fan, H., Khouja, M., & Zhou, J. (2022). Design of win-win return policies for online retailers. European Journal of Operational Research, 301(2), 675–693.

    Google Scholar 

  10. Jena, S. K., & Meena, P. (2022). Shopping in the omnichannel supply chain under price competition and product return. Journal of Retailing and Consumer Services, 65, 102848.

    Google Scholar 

  11. Jack, L., Frei, R., & Krzyzaniak, S.-A. C. (2019). Buy online, return to store: the challenges and opportunities of product returns in a multichannel environment. ECR Shrink Group.

  12. Comstock, M. (2018). The return trip: How returns impact online shopping. Retrieved 15 May 2021 from https://moneyinc.com/the-return-trip-how-returns-impact-online-shopping/

  13. Röllecke, F. J., Huchzermeier, A., & Schröder, D. (2018). Returning customers: The hidden strategic opportunity of returns management. California Management Review, 60(2), 176–203.

    Google Scholar 

  14. Chen, J., & Chen, B. (2016). Competing with customer returns policies. International Journal of Production Research, 54(7), 2093–2107.

    Google Scholar 

  15. Martínez-López, F. J., Feng, C., Li, Y., & Mata, M. S. (2022). Restoring the buyer–seller relationship through online return shipping: The role of return shipping method and return shipping fee. Electronic Commerce Research and Applications, 54, 101170.

    Google Scholar 

  16. Chaleshtari, A. S., Elahi, E., Fathi, M., & Khakifirooz, M. (2022). Impact of return leniency on retailers’ profit and social welfare in monopoly markets. Computers & Industrial Engineering, 169, 108149.

    Google Scholar 

  17. Li, Y., Feng, C., & Liu, H. (2021). Package-free returns: A trend in green ecommerce. In F. J. Martínez-López & D. LópezLópez (Eds.), Advances in Digital Marketing and eCommerce. DMEC 2021. Springer.

    Google Scholar 

  18. Ivanova, I. (2020). Those holiday returns come with a massive environmental footprint. Retrieved 15 August 2022 from https://www.cbsnews.com/news/holiday-returns-evironmental-costs-shipping/

  19. Lysenko-Ryba, K., Zimon, D., Madzík, P., & Šírová, E. (2022). Examination of aspects of pro-consumer refund system. The TQM Journal, Forthcoming.

  20. Dopson, E. (2021). The plague of ecommerce return rates and how to maintain profitability. Retrieved 8 September 2021 from https://www.shopify.com/enterprise/ecommerce-returns

  21. Ferguson, M., Guide, V. D. R., Jr., & Souza, G. C. (2006). Supply chain coordination for false failure returns. Manufacturing & Service Operations Management, 8(4), 376–393.

    Google Scholar 

  22. Janakiraman, N., Syrdal, H. A., & Freling, R. (2016). The effect of return policy leniency on consumer purchase and return decisions: A meta-analytic review. Journal of retailing, 92(2), 226–235.

    Google Scholar 

  23. Lantz, B., & Hjort, K. (2013). Real e-customer behavioural responses to free delivery and free returns. Electronic Commerce Research, 13(2), 183–198.

    Google Scholar 

  24. 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.

    Google Scholar 

  25. Shao, B., Cheng, Z., Wan, L., & Yue, J. (2021). The impact of cross border e-tailer’s return policy on consumer’s purchase intention. Journal of Retailing and Consumer Services, 59, 102367.

    Google Scholar 

  26. Shulman, J. D., Coughlan, A. T., & Savaskan, R. C. (2011). Managing consumer returns in a competitive environment. Management science, 57(2), 347–362.

    Google Scholar 

  27. Wood, S. L. (2001). Remote purchase environments: The influence of return policy leniency on two-stage decision processes. Journal of Marketing Research, 38(2), 157–169.

    Google Scholar 

  28. Abdulla, H., Abbey, J. D., & Ketzenberg, M. (2022). How consumers value retailer’s return policy leniency levers: An empirical investigation. Production and Operations Management, 31(4), 1719–1733.

    Google Scholar 

  29. Flanagin, A. J., Metzger, M. J., Pure, R., Markov, A., & Hartsell, E. (2014). Mitigating risk in ecommerce transactions: Perceptions of information credibility and the role of user-generated ratings in product quality and purchase intention. Electronic Commerce Research, 14(1), 1–23.

    Google Scholar 

  30. Wan, X., Li, D., Chen, J., & Lei, Y. (2020). Managing customer returns strategy with the option of selling returned products. International Journal of Production Economics, 230, 107794.

    Google Scholar 

  31. Flavián, C., Gurrea, R., & Orús, C. (2016). Choice confidence in the webrooming purchase process: The impact of online positive reviews and the motivation to touch. Journal of Consumer Behaviour, 15(5), 459–476.

    Google Scholar 

  32. Tran, T., Gurnani, H., & Desiraju, R. (2018). Optimal design of return policies. Marketing Science, 37(4), 649–667.

    Google Scholar 

  33. Herpin, T. (2021). The biggest ecommerce markets in 2021. Retrieved 10 October 2022 from https://www.ecommerce-nation.com/top-ecommerce-markets/

  34. Webretailer. (2021). Online marketplaces in Europe: Dynamic, diverse and disjointed. Retrieved 10 October 2022 from https://www.webretailer.com/marketplaces-worldwide/online-marketplaces-europe/

  35. Difrancesco, R. M., Huchzermeier, A., & Schröder, D. (2018). Optimizing the return window for online fashion retailers with closed-loop refurbishment. Omega, 78, 205–221.

    Google Scholar 

  36. Difrancesco, R. M., & Huchzermeier, A. (2020). Multichannel retail competition with product returns: Effects of restocking fee legislation. Electronic Commerce Research and Applications, 43, 100993.

    Google Scholar 

  37. FindLaw. (2016). Return policies and refunds. Retrieved 8 October 2022 from https://www.findlaw.com/consumer/consumer-transactions/return-policies-and-refunds.html#:~:text=Merchants%20may%20charge%20a%20restocking,these%20fees%20prior%20to%20purchase.

  38. McGregor, L. (2018). Top 5 e-commerce challenges and how to overcome them. Retrieved 30 March 2021 from https://blog.edesk.com/resources/e-commerce-challenges-trends

  39. Do, T. (2020). Return too many items on Amazon? Will Amazon ban your account? Retrieved 16 April 2021 from https://www.techwalls.com/amazon-ban-return-too-many-items/

  40. Kaplan, M. (2018). Retailers (including Amazon) crack down on return fraud. Retrieved 15 December 2020 from https://www.practicalecommerce.com/retailers-including-amazon-crack-return-fraud

  41. Yuan, L. (2018). E-commerce returns 101: How to offer free shipping & more. Retrieved 15 December 2020 from https://goshippo.com/blog/ecommerce-returns-offer-free-shipping/

  42. Mollenkopf, D. A., Rabinovich, E., Laseter, T. M., & Boyer, K. K. (2007). Managing internet product returns: A focus on effective service operations. Decision Sciences, 38(2), 215–250.

    Google Scholar 

  43. Daugherty, P. J., Bolumole, Y., & Grawe, S. J. (2018). The new age of customer impatience: An agenda for reawakening logistics customer service research. International Journal of Physical Distribution & Logistics Management, 49(1), 4–32.

    Google Scholar 

  44. Lin, D., Lee, C. K. M., Siu, M., Lau, H., & Choy, K. L. (2020). Analysis of customers’ return behaviour after online shopping in China using SEM. Industrial Management & Data Systems, 120(5), 883–902.

    Google Scholar 

  45. Stöcker, B., Baier, D., & Brand, B. M. (2021). New insights in online fashion retail returns from a customers’ perspective and their dynamics. Journal of Business Economics, 91(8), 1149–1187.

    Google Scholar 

  46. Tyagi, V., & Dhingra, V. (2021). Return policy of e-retailers: An extensive review. Journal of Contemporary Issues in Business and Government, 27(2), 1316–1336.

    Google Scholar 

  47. eMarketer. (2017). The cost of acquiring a mobile app user. Retrieved 15 December 2020 from https://www.emarketer.com/Article/Cost-of-Acquiring-Mobile-App-User/1016688

  48. Olenski, S. (2017). Marketing's next big hurdle: the rising cost of customer acquisition. Retrieved 30 March 2021 from https://www.forbes.com/sites/steveolenski/2017/11/18/marketings-next-big-hurdle-the-rising-cost-of-customer-acquisition/#18de76d12298

  49. Jaiswal, A. K., Niraj, R., Park, C. H., & Agarwal, M. K. (2018). The effect of relationship and transactional characteristics on customer retention in emerging online markets. Journal of Business Research, 92, 25–35.

    Google Scholar 

  50. Mandal, P., Basu, P., & Saha, K. (2021). Forays into omnichannel: An online retailer’s strategies for managing product returns. European Journal of Operational Research, 292(2), 633–651.

    Google Scholar 

  51. Shulman, J. D., Coughlan, A. T., & Savaskan, R. C. (2010). Optimal reverse channel structure for consumer product returns. Marketing Science, 29(6), 1071–1085.

    Google Scholar 

  52. Gelbrich, K., Gäthke, J., & Hübner, A. (2017). Rewarding customers who keep a product: How reinforcement affects customers’ product return decision in online retailing. Psychology & marketing, 34(9), 853–867.

    Google Scholar 

  53. Lee, S., & Yi, Y. (2017). “Seize the deal, or return it losing your free gift”: The effect of a gift-with-purchase promotion on product return intention. Psychology & marketing, 34(3), 249–263.

    Google Scholar 

  54. Heuer, D., Brettel, M., & Kemper, J. (2015). Brand competition in fashion e-commerce. Electronic Commerce Research and Applications, 14(6), 514–522.

    Google Scholar 

  55. Weiner, B., Graham, S., & Reyna, C. (1997). An attributional examination of retributive versus utilitarian philosophies of punishment. Social Justice Research, 10(4), 431–452.

    Google Scholar 

  56. Rong-Da Liang, A., Lee, C.-L., & Tung, W. (2014). The role of sunk costs in online consumer decision-making. Electronic Commerce Research and Applications, 13(1), 56–68.

    Google Scholar 

  57. Wang, X. (2009). Retail return policy, endowment effect, and consumption propensity: an experimental study. The BE Journal of Economic Analysis & Policy, 9(1), 2288.

    Google Scholar 

  58. Shang, G., Ghosh, B. P., & Galbreth, M. R. (2017). Optimal retail return policies with wardrobing. Production and Operations Management, 26(7), 1315–1332.

    Google Scholar 

  59. Levin, A. M., Dato-on, M. C., & Manolis, C. (2007). Deterring illegal downloading: The effects of threat appeals, past behavior, subjective norms, and attributions of harm. Journal of Consumer Behaviour: An International Research Review, 6(2–3), 111–122.

    Google Scholar 

  60. Su, X. (2009). Consumer returns policies and supply chain performance. Manufacturing & Service Operations Management, 11(4), 595–612.

    Google Scholar 

  61. Hausmann, L., Herrmann, N., Krause, J., & Netzer, T. (2014). Same-day delivery: The next evolutionary step in parcel logistics. Retrieved 15 December 2020 from https://www.mckinsey.com/industries/travel-transport-and-logistics/our-insights/same-day-delivery-the-next-evolutionary-step-in-parcel-logistics

  62. Shang, G., Ferguson, M. E., & Galbreth, M. R. (2019). Where should I focus my return reduction efforts? Empirical guidance for retailers. Decision Sciences, 50(4), 877–909.

    Google Scholar 

  63. Jablonsky, S. F., & DeVries, D. L. (1972). Operant conditioning principles extrapolated to the theory of management. Organizational Behavior and Human Performance, 7(2), 340–358.

    Google Scholar 

  64. Boisvert, J., & Ashill, N. J. (2018). The impact of branding strategies on horizontal and downward line extension of luxury brands: A cross-national study. International Marketing Review, 35(6), 1033–1052.

    Google Scholar 

  65. Liao, T. H., & Keng, C. J. (2013). Online shopping delivery delay: Finding a psychological recovery strategy by online consumer experiences. Computers in Human Behavior, 29(4), 1849–1861.

    Google Scholar 

  66. Suh, B., & Han, I. (2003). The impact of customer trust and perception of security control on the acceptance of electronic commerce. International Journal of Electronic Commerce, 7(3), 135–161.

    Google Scholar 

  67. Abbey, J., Ketzenberg, M., & Metters, R. (2018). A more profitable approach to product returns. MIT Sloan Management Review, 60(1), 1–6.

    Google Scholar 

  68. Chang, H., Wang, Y., & Yang, W. (2009). The impact of e-service quality, customer satisfaction and loyalty on e-marketing: Moderating effect of perceived value. Total Quality Management, 20(4), 423–443.

    Google Scholar 

  69. Ertekin, N. (2018). Immediate and long-term benefits of in-store return experience. Production and Operations Management, 27(1), 121–142.

    Google Scholar 

  70. Arvey, R. D., & Ivancevich, J. M. (1980). Punishment in organizations: A review, propositions, and research suggestions. Academy of Management Review, 5(1), 123–132.

    Google Scholar 

  71. Andreoni, J., Harbaugh, W., & Vesterlund, L. (2003). The carrot or the stick: Rewards, punishments, and cooperation. American Economic Review, 93(3), 893–902.

    Google Scholar 

  72. Chen, Y., Ramamurthy, K., & Wen, K. (2012). Organizations’ information security policy compliance: Stick or carrot approach? Journal of Management Information Systems, 29(3), 157–188.

    Google Scholar 

  73. Greitemeyer, T., & Weiner, B. (2008). Asymmetrical effects of reward and punishment on attributions of morality. The Journal of Social Psychology, 148(4), 407–422.

    Google Scholar 

  74. Dootson, P., Johnston, K. A., Lings, I., & Beatson, A. (2018). Tactics to deter deviant consumer behavior: A research agenda. Journal of Consumer Marketing, 35(6), 577–587.

    Google Scholar 

  75. Chiu, C. M., Chang, C. C., Cheng, H. L., & Fang, Y. H. (2009). Determinants of customer repurchase intention in online shopping. Online information review, 33(4), 761–784.

    Google Scholar 

  76. San Martín, S., & Jiménez, N. H. (2011). Online buying perceptions in Spain: Can gender make a difference? Electronic Markets, 21(4), 267–281.

    Google Scholar 

  77. Zhang, J., Li, H., Yan, R., & Johnston, C. (2017). Examining the signaling effect of e-tailers’ return policies. Journal of Computer Information Systems, 57(3), 191–200.

    Google Scholar 

  78. McCollough, M. A., & Gremler, D. D. (2004). A conceptual model and empirical examination of the effect of service guarantees on post‐purchase consumption evaluations. Managing Service Quality: An International Journal.

  79. Bonifield, C., Cole, C., & Schultz, R. L. (2010). Product returns on the internet: A case of mixed signals? Journal of Business Research, 63(9–10), 1058–1065.

    Google Scholar 

  80. Oghazi, P., Karlsson, S., Hellström, D., & Hjort, K. (2018). Online purchase return policy leniency and purchase decision: Mediating role of consumer trust. Journal of Retailing and Consumer Services, 41, 190–200.

    Google Scholar 

  81. Frisby, J. (2018). The world’s biggest online spenders revealed. Retrieved 30 March 2021 from https://www.websitebuilderexpert.com/blog/worlds-biggest-online-spenders/

  82. Ranganathan, C., Seo, D., & Babad, Y. (2006). Switching behavior of mobile users: Do users’ relational investments and demographics matter? European Journal of Information Systems, 15(3), 269–276.

    Google Scholar 

  83. Zhou, T. (2011). An empirical examination of initial trust in mobile banking. Internet Research, 21(5), 527–540.

    Google Scholar 

  84. Go-Globe. (2018). Shopping apps usage - statistics and trends [Infographic]. Retrieved 15 January 2020 from https://www.go-globe.hk/blog/shopping-apps-usage/

  85. Nikbin, D., Ismail, I., Marimuthu, M., & Armesh, H. (2012). Perceived justice in service recovery and switching intention: Evidence from Malaysian mobile telecommunication industry. Management Research Review, 35(3/4), 309–325.

    Google Scholar 

  86. Deutskens, E., de Jong, A., de Ruyter, K., & Wetzels, M. (2006). Comparing the generalizability of online and mail surveys in cross-national service quality research. Marketing letters, 17(2), 119–136.

    Google Scholar 

  87. Cui, X., Lai, V. S., & Lowry, P. B. (2016). How do bidders’ organism reactions mediate auction stimuli and bidder loyalty in online auctions? The case of Taobao in China. Information & Management, 53(5), 609–624.

    Google Scholar 

  88. Yu, H., & Gao, Q. (2012). Analysis of influencing factors of taobao consumer behavior. WHICEB, AIS Electronic Library.

  89. Dailey, L. C., & Ülkü, M. A. (2018). Retailers beware: On denied product returns and consumer behavior. Journal of Business Research, 86, 202–209.

    Google Scholar 

  90. Winkler, N. (2018). Ecommerce returns: Policy, rates, best practices & statistics (2018 holiday Ed.). Retrieved 16 April 2021 from https://www.shopify.com/enterprise/ecommerce-returns

  91. AlibabaGroup. (2018). Annual and transition report (foreign private issuer). Retrieved 15 January 2020 from https://otp.investis.com/clients/us/alibaba/SEC/sec-show.aspx?FilingId=12879202&Cik=0001577552&Type=PDF&hasPdf=1

  92. Akçay, Y., Boyacı, T., & Zhang, D. (2013). Selling with money-back guarantees: The impact on prices, quantities, and retail profitability. Production and Operations Management, 22(4), 777–791.

    Google Scholar 

  93. Zhu, Z., Nakata, C., Sivakumar, K., & Grewal, D. (2007). Self-service technology effectiveness: The role of design features and individual traits. Journal of the Academy of Marketing Science, 35(4), 492–506.

    Google Scholar 

  94. Chen, H., Marmorstein, H., Tsiros, M., & Rao, A. R. (2012). When more is less: The impact of base value neglect on consumer preferences for bonus packs over price discounts. Journal of Marketing, 76(4), 64–77.

    Google Scholar 

  95. Shulman, J. D., Coughlan, A. T., & Savaskan, R. C. (2009). Optimal restocking fees and information provision in an integrated demand-supply model of product returns. Manufacturing & Service Operations Management, 11(4), 577–594.

    Google Scholar 

  96. Abdulla, H., Ketzenberg, M., & Abbey, J. D. (2019). Taking stock of consumer returns: A review and classification of the literature. Journal of Operations Management, 65(6), 560–605.

    Google Scholar 

  97. Wang, S., Beatty, S. E., & Foxx, W. (2004). Signaling the trustworthiness of small online retailers. Journal of Interactive Marketing, 18(1), 53–69.

    Google Scholar 

  98. Gäthke, J., Gelbrich, K., & Chen, S. (2022). A cross-national service strategy to manage product returns: e-tailers’ return policies and the legitimating role of the institutional environment. Journal of Service Research, 25(3), 402–421.

    Google Scholar 

  99. Ahsan, K., & Rahman, S. (2016). An investigation into critical service determinants of customer to business (C2B) type product returns in retail firms. International Journal of Physical Distribution & Logistics Management, 46(6/7), 606–633.

    Google Scholar 

  100. Hjort, K., Hellström, D., Karlsson, S., & Oghazi, P. (2019). Typology of practices for managing consumer returns in internet retailing. International Journal of Physical Distribution & Logistics Management, 49(7), 767–790.

    Google Scholar 

  101. Martín-Ruiz, D., & Rondán-Cataluña, F. J. (2008). The nature and consequences of price unfairness in services: A comparison to tangible goods. International Journal of Service Industry Management, 19, 325–352.

    Google Scholar 

  102. Hjort, K., Lantz, B., Ericsson, D., & Gattorna, J. (2013). Customer segmentation based on buying and returning behaviour. International Journal of Physical Distribution & Logistics Management, 43(10), 852–865.

    Google Scholar 

  103. Chang, I., Chang, C., Wu, J., & Huang, T. (2015). Assessing the performance of long-term care information systems and the continued use intention of users. Telematics and Informatics, 32(2), 273–281.

    Google Scholar 

  104. Shin, J. I., Chung, K. H., Oh, J. S., & Lee, C. W. (2013). The effect of site quality on repurchase intention in Internet shopping through mediating variables: The case of university students in South Korea. International Journal of Information Management, 33(3), 453–463.

    Google Scholar 

  105. Lin, C.-Y., Chao, Y.-C., & Tang, T.-W. (2017). Why not be “smarter”? Examining the factors that influence the behavioral intentions of non-smartphone users. Industrial Management & Data Systems, 117(1), 32–49.

    Google Scholar 

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Acknowledgements

A part of Yangchun Li’s work was done at the University of Granada. This work was supported by Research Start-Up Fund of Zhejiang University of Technology (number: 2021132007929) and the China Scholarship Council.

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Authors and Affiliations

  1. Business School, University of Granada, Campus Universitario de Cartuja, 18071, Granada, Spain

    Francisco J. Martínez-López, Changyuan Feng & Huaming Liu

  2. EAE Business School, Barcelona, Spain

    Francisco J. Martínez-López

  3. School of Management, Zhejiang University of Technology, Boyi Building A, 288 Liuhe Road, Xihu District, Hangzhou, 310023, China

    Yangchun Li

  4. ESADE Business School, 08034, Barcelona, Spain

    David López-López

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Martínez-López, F.J., Li, Y., Feng, C. et al. Reducing ecommerce returns with return credits. Electron Commer Res 23, 2011–2033 (2023). https://doi.org/10.1007/s10660-022-09638-5

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