IDEAS home Printed from https://ideas.repec.org/a/inm/orisre/v34y2023i3p910-934.html
   My bibliography  Save this article

Platform Refund Insurance or Being Cast Out: Quantifying the Signaling Effect of Refund Options in the Online Service Marketplace

Author

Listed:
  • Jinyang Zheng

    (Krannert School of Management, Purdue University, West Lafayette, Indiana 47906)

  • Youwei Wang

    (Department of Information Management and Business Intelligence, School of Management, Fudan University, Shanghai 200433, China)

  • Yong Tan

    (Michael G. Foster School of Business, University of Washington, Seattle, Washington 98195)

Abstract

This study examines whether and how an online service marketplace can leverage refund options endorsed by different parties (i.e., the platform or sellers) to address the “lemons” problem that is caused by the intangibility, variability, and unreturnable nature of the services sought. Through developing a signaling mechanism and a corresponding demand estimation model, we show that both platform refund insurance and a seller-guaranteed refund increase service demand, with platform refund insurance as the more effective option and hence having a more effective signaling mechanism. A reduced-form analysis suggests that sellers with a better reputation or less popularity might benefit less from refund options. An investigation on further use of the more effective refund option, a “having platform refund insurance or being cast out” policy (i.e., retaining platform refund-insured sellers but expelling uninsured ones), using counterfactual simulations and supply-side single interrupted time series designs, reveals the effectiveness of this policy in filtering out low-quality sellers, shown as an improved quality of sellers on the platform due to the change in sellers (i.e., new sellers’ replacing those who were expelled) both immediately after the policy and in the (near) equilibrium, yet a cost (i.e., a loss in demand and consumer welfare) for the platform in the (near) equilibrium due to the changes in characteristics (e.g., price) of sellers. This cost, however, is lower than the benefit from the improved quality of the sellers, so that the platform’s overall performance improves. The study also quantifies the consumer welfare of the online service marketplace and provides practical insight for consumers, sellers, and online service marketplace operators.

Suggested Citation

  • Jinyang Zheng & Youwei Wang & Yong Tan, 2023. "Platform Refund Insurance or Being Cast Out: Quantifying the Signaling Effect of Refund Options in the Online Service Marketplace," Information Systems Research, INFORMS, vol. 34(3), pages 910-934, September.
  • Handle: RePEc:inm:orisre:v:34:y:2023:i:3:p:910-934
    DOI: 10.1287/isre.2022.1162
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/isre.2022.1162
    Download Restriction: no

    File URL: https://libkey.io/10.1287/isre.2022.1162?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Jean‐Pierre Dubé & Jeremy T. Fox & Che‐Lin Su, 2012. "Improving the Numerical Performance of Static and Dynamic Aggregate Discrete Choice Random Coefficients Demand Estimation," Econometrica, Econometric Society, vol. 80(5), pages 2231-2267, September.
    2. Che, Yeon-Koo, 1996. "Customer Return Policies for Experience Goods," Journal of Industrial Economics, Wiley Blackwell, vol. 44(1), pages 17-24, March.
    3. Steven Berry & Philip Haile, 2016. "Identification in Differentiated Products Markets," Annual Review of Economics, Annual Reviews, vol. 8(1), pages 27-52, October.
    4. Peter Davis, 2006. "Spatial competition in retail markets: movie theaters," RAND Journal of Economics, The RAND Corporation, vol. 37(4), pages 964-982, December.
    5. Steven T. Berry & Philip A. Haile, 2014. "Identification in Differentiated Products Markets Using Market Level Data," Econometrica, Econometric Society, vol. 82, pages 1749-1797, September.
    6. Pei, Zhi & Paswan, Audhesh & Yan, Ruiliang, 2014. "E-tailer׳s return policy, consumer׳s perception of return policy fairness and purchase intention," Journal of Retailing and Consumer Services, Elsevier, vol. 21(3), pages 249-257.
    7. Hamilton Emmons & Stephen M. Gilbert, 1998. "Note. The Role of Returns Policies in Pricing and Inventory Decisions for Catalogue Goods," Management Science, INFORMS, vol. 44(2), pages 276-283, February.
    8. Liang Guo, 2009. "Service Cancellation and Competitive Refund Policy," Marketing Science, INFORMS, vol. 28(5), pages 901-917, 09-10.
    9. Steven T. Berry, 1994. "Estimating Discrete-Choice Models of Product Differentiation," RAND Journal of Economics, The RAND Corporation, vol. 25(2), pages 242-262, Summer.
    10. Eric T. Anderson & Karsten Hansen & Duncan Simester, 2009. "The Option Value of Returns: Theory and Empirical Evidence," Marketing Science, INFORMS, vol. 28(3), pages 405-423, 05-06.
    11. Pascal Courty & Li Hao, 2000. "Sequential Screening," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 67(4), pages 697-717.
    12. Anindya Ghose & Michael D. Smith & Rahul Telang, 2006. "Internet Exchanges for Used Books: An Empirical Analysis of Product Cannibalization and Welfare Impact," Information Systems Research, INFORMS, vol. 17(1), pages 3-19, March.
    13. Bonifield, Carolyn & Cole, Catherine & Schultz, Randall L., 2010. "Product returns on the Internet: A case of mixed signals?," Journal of Business Research, Elsevier, vol. 63(9-10), pages 1058-1065, September.
    14. Anindya Ghose & Sang Pil Han, 2014. "Estimating Demand for Mobile Applications in the New Economy," Management Science, INFORMS, vol. 60(6), pages 1470-1488, June.
    15. Xuanming Su, 2009. "Consumer Returns Policies and Supply Chain Performance," Manufacturing & Service Operations Management, INFORMS, vol. 11(4), pages 595-612, March.
    16. Ai, Chunrong & Norton, Edward C., 2003. "Interaction terms in logit and probit models," Economics Letters, Elsevier, vol. 80(1), pages 123-129, July.
    17. Lee, Boon-Chye & Ang, Lawrence & Dubelaar, Chris, 2005. "Lemons on the Web: A signalling approach to the problem of trust in Internet commerce," Journal of Economic Psychology, Elsevier, vol. 26(5), pages 607-623, October.
    18. Dellarocas, Chrysanthos, 2003. "The Digitization of Word-of-mouth: Promise and Challenges of Online Feedback Mechanisms," Working papers 4296-03, Massachusetts Institute of Technology (MIT), Sloan School of Management.
    19. Aviv Nevo, 2003. "New Products, Quality Changes, and Welfare Measures Computed from Estimated Demand Systems," The Review of Economics and Statistics, MIT Press, vol. 85(2), pages 266-275, May.
    20. Sridhar Moorthy & Kannan Srinivasan, 1995. "Signaling Quality with a Money-Back Guarantee: The Role of Transaction Costs," Marketing Science, INFORMS, vol. 14(4), pages 442-466.
    21. Erik Brynjolfsson & Yu (Jeffrey) Hu & Michael D. Smith, 2003. "Consumer Surplus in the Digital Economy: Estimating the Value of Increased Product Variety at Online Booksellers," Management Science, INFORMS, vol. 49(11), pages 1580-1596, November.
    22. J. Miguel Villas-Boas & Russell S. Winer, 1999. "Endogeneity in Brand Choice Models," Management Science, INFORMS, vol. 45(10), pages 1324-1338, October.
    23. Peter Davis, 2006. "Spatial competition in retail markets: movie theaters," RAND Journal of Economics, RAND Corporation, vol. 37(4), pages 964-982, December.
    24. Shunyuan Zhang & Dokyun Lee & Param Vir Singh & Kannan Srinivasan, 2022. "What Makes a Good Image? Airbnb Demand Analytics Leveraging Interpretable Image Features," Management Science, INFORMS, vol. 68(8), pages 5644-5666, August.
    25. Bruce McWilliams, 2012. "Money-Back Guarantees: Helping the Low-Quality Retailer," Management Science, INFORMS, vol. 58(8), pages 1521-1524, August.
    26. Suwelack, Thomas & Hogreve, Jens & Hoyer, Wayne D., 2011. "Understanding Money-Back Guarantees: Cognitive, Affective, and Behavioral Outcomes," Journal of Retailing, Elsevier, vol. 87(4), pages 462-478.
    27. Blundell, Richard & Bond, Stephen, 1998. "Initial conditions and moment restrictions in dynamic panel data models," Journal of Econometrics, Elsevier, vol. 87(1), pages 115-143, August.
    28. Chrysanthos Dellarocas, 2003. "The Digitization of Word of Mouth: Promise and Challenges of Online Feedback Mechanisms," Management Science, INFORMS, vol. 49(10), pages 1407-1424, October.
    29. Hema Yoganarasimhan, 2013. "The Value of Reputation in an Online Freelance Marketplace," Marketing Science, INFORMS, vol. 32(6), pages 860-891, November.
    30. Thanh Tran & Haresh Gurnani & Ramarao Desiraju, 2018. "Optimal Design of Return Policies," Marketing Science, INFORMS, vol. 37(4), pages 649-667, August.
    31. Andrew Sweeting, 2013. "Dynamic Product Positioning in Differentiated Product Markets: The Effect of Fees for Musical Performance Rights on the Commercial Radio Industry," Econometrica, Econometric Society, vol. 81(5), pages 1763-1803, September.
    32. Anindya Ghose & Panagiotis G. Ipeirotis & Beibei Li, 2012. "Designing Ranking Systems for Hotels on Travel Search Engines by Mining User-Generated and Crowdsourced Content," Marketing Science, INFORMS, vol. 31(3), pages 493-520, May.
    33. V. Padmanabhan & I. P. L. Png, 1997. "Manufacturer's Return Policies and Retail Competition," Marketing Science, INFORMS, vol. 16(1), pages 81-94.
    34. Amil Petrin, 2002. "Quantifying the Benefits of New Products: The Case of the Minivan," Journal of Political Economy, University of Chicago Press, vol. 110(4), pages 705-729, August.
    35. Manuel Arellano & Stephen Bond, 1991. "Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 58(2), pages 277-297.
    36. Xiao Huang & Dan Zhang, 2020. "Service Product Design and Consumer Refund Policies," Marketing Science, INFORMS, vol. 39(2), pages 366-381, March.
    Full references (including those not matched with items on IDEAS)

    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. Sun, Miao & Chen, Jing & Tian, Ye & Yan, Yufei, 2021. "The impact of online reviews in the presence of customer returns," International Journal of Production Economics, Elsevier, vol. 232(C).
    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. Victor Aguirregabiria & Margaret Slade, 2017. "Empirical models of firms and industries," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 50(5), pages 1445-1488, December.
    4. Rokonuzzaman, Md & Iyer, Pramod & Harun, Ahasan, 2021. "Return policy, No joke: An investigation into the impact of a retailer's return policy on consumers' decision making," Journal of Retailing and Consumer Services, Elsevier, vol. 59(C).
    5. Zhang, Qiao & Chen, Jing & Chen, Bintong, 2021. "Information strategy in a supply chain under asymmetric customer returns information," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 155(C).
    6. Ratchford, Brian & Soysal, Gonca & Zentner, Alejandro & Gauri, Dinesh K., 2022. "Online and offline retailing: What we know and directions for future research," Journal of Retailing, Elsevier, vol. 98(1), pages 152-177.
    7. Wenyan Zhou & Oliver Hinz, 2016. "Determining profit-optimizing return policies – a two-step approach on data from taobao.com," Electronic Markets, Springer;IIM University of St. Gallen, vol. 26(2), pages 103-114, May.
    8. 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).
    9. Xiao Huang & Dan Zhang, 2020. "Service Product Design and Consumer Refund Policies," Marketing Science, INFORMS, vol. 39(2), pages 366-381, March.
    10. Tridib Sharma & Levent Ülkü, 2015. "Money-Back Guarantees," Working Papers 1502, Centro de Investigacion Economica, ITAM.
    11. Anindya Ghose & Sang Pil Han, 2014. "Estimating Demand for Mobile Applications in the New Economy," Management Science, INFORMS, vol. 60(6), pages 1470-1488, June.
    12. Moon, Hyungsik Roger & Shum, Matthew & Weidner, Martin, 2018. "Estimation of random coefficients logit demand models with interactive fixed effects," Journal of Econometrics, Elsevier, vol. 206(2), pages 613-644.
    13. Hailiang Chen & Prabuddha De & Yu Jeffrey Hu, 2015. "IT-Enabled Broadcasting in Social Media: An Empirical Study of Artists’ Activities and Music Sales," Information Systems Research, INFORMS, vol. 26(3), pages 513-531, September.
    14. Joon Yong Seo & Sukki Yoon & Milena Vangelova, 2016. "Shopping plans, buying motivations, and return policies: impacts on product returns and purchase likelihoods," Marketing Letters, Springer, vol. 27(4), pages 645-659, December.
    15. Xu, Xun & Jackson, Jonathan E., 2019. "Investigating the influential factors of return channel loyalty in omni-channel retailing," International Journal of Production Economics, Elsevier, vol. 216(C), pages 118-132.
    16. Gianfranco Walsh & Michael Möhring, 2017. "Effectiveness of product return-prevention instruments: Empirical evidence," Electronic Markets, Springer;IIM University of St. Gallen, vol. 27(4), pages 341-350, November.
    17. Miravete, Eugenio J. & Seim, Katja & Thurk, Jeff, 2023. "Pass-through and tax incidence in differentiated product markets," International Journal of Industrial Organization, Elsevier, vol. 90(C).
    18. Philip G. Gayle & Ying Lin, 2022. "Market effects of new product introduction: Evidence from the brew‐at‐home coffee market," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 31(3), pages 525-557, August.
    19. Yang Li & Brett R. Gordon & Oded Netzer, 2018. "An Empirical Study of National vs. Local Pricing by Chain Stores Under Competition," Marketing Science, INFORMS, vol. 37(5), pages 812-837, September.
    20. Jason Chan & Jing Wang, 2018. "Hiring Preferences in Online Labor Markets: Evidence of a Female Hiring Bias," Management Science, INFORMS, vol. 64(7), pages 2973-2994, July.

    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:inm:orisre:v:34:y:2023:i:3:p:910-934. 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: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    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.