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

References

• Ai C, Norton EC (2003) Interaction terms in logit and probit models. Econom. Lett. 80(1):123–129.Crossref, Google Scholar
• Akerlof G (1970) The market for lemons. Quart. J. Econom. 84(3):488–500.Crossref, Google Scholar
• Anderson ET, Hansen K, Simester D (2009) The option value of returns: Theory and empirical evidence. Marketing Sci. 28(3):405–423.Link, Google Scholar
• Arellano M, Bond S (1991) Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. Rev. Econom. Stud. 58(2):277–297.Crossref, Google Scholar
• Ba S, Pavlou PA (2002) Evidence of the effect of trust building technology in electronic markets: Price premiums and buyer behavior. Management Inform. Systems Quart. 26(3):243–268.Crossref, Google Scholar
• Berry S (1994) Estimating discrete-choice models of product differentiation. RAND J. Econom. 25(2):242–262.Crossref, Google Scholar
• Berry S, Haile P (2014) Identification in differentiated products markets using market level data. Econometrica 82(5):1749–1797.Crossref, Google Scholar
• Berry S, Haile P (2016) Identification in differentiated products markets. Annu. Rev. Econom. 8:27–52.Crossref, Google Scholar
• Berry S, Levinsohn J, Pakes A (1995) Automobile prices in market equilibrium. Econometrica 63(4):841–890.Crossref, Google Scholar
• Berry S, Levinsohn J, Pakes A (1999) Voluntary export restraints on automobiles: Evaluating a trade policy. Amer. Econom. Rev. 89(3):400–430.Crossref, Google Scholar
• Blundell R, Bond S (1998) Initial conditions and moment restrictions in dynamic panel data models. J. Econometrics 87(1):115–143.Crossref, Google Scholar
• Bockstedt J, Goh KH (2011) Seller strategies for differentiation in highly competitive online auction markets. J. Management Inform. Systems 28(3):235–268.Crossref, Google Scholar
• Bonifield C, Cole C, Schultz RL (2010) Product returns on the Internet: A case of mixed signals? J. Bus. Res. 63(9-10):1058–1065.Crossref, Google Scholar
• Boulding W, Kirmani A (1993) A consumer-side experimental examination of signaling theory: Do consumers perceive warranties as signals of quality? J. Consumer Res. 20(1):111–123.Crossref, Google Scholar
• Bower AB, Maxham JG III (2012) Return shipping policies of online retailers: Normative assumptions and the long-term consequences of fee and free returns. J. Marketing 76(5):110–124.Crossref, Google Scholar
• Brynjolfsson E, Hu Y, Smith MD (2003) Consumer surplus in the digital economy: Estimating the value of increased product variety at online booksellers. Management Sci. 49(11):1580–1596.Link, Google Scholar
• Che YK (1996) Customer return policies for experience goods. J. Industry Econom. 44(1):17–24.Crossref, Google Scholar
• Chu W, Gerstner E, Hess JD (1998) Managing dissatisfaction: How to decrease customer opportunism by partial refunds. J. Service Res. 1(2):140–155.Crossref, Google Scholar
• Courty P, Li H (2000) Sequential screening. Rev. Econom. Stud. 67(4):697–717.Crossref, Google Scholar
• Davis P (2006) Spatial competition in retail markets: Movie theaters. RAND J. Econom. 37(4):964–982.Crossref, Google Scholar
• Davis S, Hagerty M, Gerstner E (1998) Return policies and the optimal level of “hassle”. J. Econom. Bus. 50(5):445–460.Crossref, Google Scholar
• Dean DH, Biswas A (2001) Third-party organization endorsement of products: An advertising cue affecting consumer prepurchase evaluation of goods and services. J. Advertising 30(4):41–57.Crossref, Google Scholar
• Dellarocas C (2003) The digitization of word of mouth: Promise and challenges of online feedback mechanisms. Management Sci. 49(10):1407–1424.Link, Google Scholar
• Desmet P (2014) How retailer money-back guarantees influence consumer preferences for retailer vs. national brands. J. Bus. Res. 67(9):1971–1978.Crossref, Google Scholar
• Dimoka A, Hong Y, Pavlou PA (2012) On product uncertainty in online markets: Theory and evidence. Management Inform. Systems Quart. 36(2):395–426.Crossref, Google Scholar
• Dubé JP, Fox JT, Su CL (2012) Improving the numerical performance of static and dynamic aggregate discrete choice random coefficients demand estimation. Econometrica 80(5):2231–2267.Crossref, Google Scholar
• Efron B, Tibshirani R (1986) Bootstrap methods for standard errors, confidence intervals, and other measures of statistical accuracy. Statist. Sci. 1(1):54–75.Crossref, Google Scholar
• Emmons H, Gilbert SM (1998) Note. The role of returns policies in pricing and inventory decisions for catalogue goods. Management Sci. 44(2):276–283.Link, Google Scholar
• Gandhi A, Lu Z, Shi X (2022) Estimating demand for differentiated products with zeroes in market share data. Quant. Econom. Forthcoming.Google Scholar
• Ghose A (2009) Internet exchanges for used goods: An empirical analysis of trade patterns and adverse selection. Management Inform. Systems Quart. 33(2):263–291.Crossref, Google Scholar
• Ghose A, Han SP (2014) Estimating demand for mobile applications in the new economy. Management Sci. 60(6):1470–1488.Link, Google Scholar
• Ghose A, Ipeirotis PG, Li B (2012) Designing ranking systems for hotels on travel search engines by mining user-generated and crowdsourced content. Marketing Sci. 31(3):493–520.Link, Google Scholar
• Ghose A, Smith MD, Telang R (2006) Internet exchanges for used books: An empirical analysis of product cannibalization and welfare impact. Inform. Systems Res. 17(1):3–19.Link, Google Scholar
• Gregg DG, Walczak S (2008) Dressing your online auction business for success: An experiment comparing two eBay businesses. Management Inform. Systems Quart. 32(3):653–670.Crossref, Google Scholar
• Guo L (2009) Service cancellation and competitive refund policy. Marketing Sci. 28(5):901–917.Link, Google Scholar
• Hausman JA (1996) Valuation of new goods under perfect and imperfect competition. Bresnahan TF, Gordon RJ, eds. The Economics of New Goods (University of Chicago Press, Chicago), 209–248.Google Scholar
• Hess JD, Chu W, Gerstner E (1996) Controlling product returns in direct marketing. Marketing Lett. 7(4):307–317.Crossref, Google Scholar
• Huang X, Zhang D (2020) Service product design and consumer refund policies. Marketing Sci. 39(2):366–381.Link, Google Scholar
• Lee BC, Ang L, Dubelaar C (2005) Lemons on the Web: A signaling approach to the problem of trust in Internet commerce. J. Econom. Psych. 26(5):607–623.Crossref, Google Scholar
• Li S, Srinivasan K, Sun B (2009) Internet auction features as quality signals. J. Marketing 73(1):75–92.Crossref, Google Scholar
• Li X, Zhuang Y, Lu B, Chen G (2019) A multi-stage hidden Markov model of customer repurchase motivation in online shopping. Decision Support Systems 120:72–80.Crossref, Google Scholar
• McFadden D (1973) Conditional logit analysis of qualitative choice behavior. Zarembka P, ed. Frontiers in Econometrics (Academic Press, New York), 105–142.Google Scholar
• McFadden D (1981) Econometric models of probabilistic choice. Manski C, McFadden D, eds. Structural Analysis of Discrete Data with Econometric Applications (MIT Press, Cambridge, MA), 198–272.Google Scholar
• McWilliams B (2012) Money-back guarantees: Helping the low-quality retailer. Management Sci. 58(8):1521–1524.Link, Google Scholar
• Moorthy S, Srinivasan K (1995) Signaling quality with a money-back guarantee: The role of transaction costs. Marketing Sci. 14(4):442–466.Link, Google Scholar
• Nasr-Bechwati NN, Siegal WS (2005) The impact of the prechoice process on product returns. J. Marketing Res. 42(3):358–367.Crossref, Google Scholar
• Nevo A (2000a) A practitioner’s guide to estimation of random‐coefficients logit models of demand. J. Econom. Management Strategy 9(4):513–548.Crossref, Google Scholar
• Nevo A (2000b) Mergers with differentiated products: The case of the ready-to-eat cereal industry. RAND J. Econom. 31(3):395–421.Crossref, Google Scholar
• Nevo A (2001) Measuring market power in the ready‐to‐eat cereal industry. Econometrica. 69(2):307–342.Crossref, Google Scholar
• Nevo A (2003) New products, quality changes, and welfare measures computed from estimated demand systems. Rev. Econom. Statist. 85(2):266–275.Crossref, Google Scholar
• Padmanabhan V, Png IP (1997) Manufacturer’s return policies and retail competition. Marketing Sci. 16(1):81–94.Link, Google Scholar
• Pavlou PA, Dimoka A (2006) The nature and role of feedback text comments in online marketplaces: Implications for trust building, price premiums, and seller differentiation. Inform. Systems Res. 17(4):392–414.Link, Google Scholar
• Pei Z, Paswan A, Yan R (2014) E-tailers’ return policy, consumers’ perception of return policy fairness and purchase intention. J. Retailing Consumer Service 21(3):249–257.Crossref, Google Scholar
• Perez S, Etherington D (2015) Amazon’s on-demand services marketplace launches Monday. Accessed May 3, 2016, http://techcrunch.com/2015/03/25/amazons-on-demand-services-marketplace-launches-monday.Google Scholar
• Petersen JA, Kumar V (2009) Are product returns a necessary evil? Antecedents and consequences. J. Marketing 73(3):35–51.Crossref, Google Scholar
• Petrin A (2002) Quantifying the benefits of new products: The case of the minivan. J. Political Econom. 110(4):705–729.Crossref, Google Scholar
• Purohit D, Srivastava J (2001) Effect of manufacturer reputation, retailer reputation, and product warranty on consumer judgments of product quality: A cue diagnosticity framework. J. Consumer Psych. 10(3):123–134.Crossref, Google Scholar
• Schlosser AE, White TB, Lloyd SM (2006) Converting website visitors into buyers: How website investment increases consumer trusting beliefs and online purchase intentions. J. Marketing 70(2):133–148.Crossref, Google Scholar
• Seetharaman D (2014) Exclusive: Amazon.com plans local services marketplace this year - sources. Accessed May 3, 2016, https://www.reuters.com/article/us-amazon-com-services/exclusive-amazon-com-plans-local-services-marketplace-this-year-sources-idUSKBN0EL20S20140610.Google Scholar
• Shang G, Pekgün P, Ferguson M, Galbreth M (2017) How much do online consumers really value free product returns? Evidence from eBay. J. Oper. Management 53:45–62.Crossref, Google Scholar
• Shieh S (1996) Price and money‐back guarantees as signals of product quality. J. Econom. Management Strategy 5(3):361–377.Crossref, Google Scholar
• Sohu IT (2015) New market structure for domestic mobile phone: Increment in 4G phone, decrement in contract bundled phone. Accessed May 3, 2016, http://it.sohu.com/20141017/n405198791.shtmlGoogle Scholar
• Su X (2009) Consumer returns policies and supply chain performance. Manufacturing Service Oper. Management 11(4):595–612.Link, Google Scholar
• Suwelack T, Hogreve J, Hoyer WD (2011) Understanding money-back guarantees: Cognitive, affective, and behavioral outcomes. J. Retailing 87(4):462–478.Crossref, Google Scholar
• Sweeting A (2013) Dynamic product positioning in differentiated product markets: The effect of fees for musical performance rights on the commercial radio industry. Econometrica 81(5):1763–1803.Crossref, Google Scholar
• Tran T, Gurnani H, Desiraju R (2018) Optimal design of return policies. Marketing Sci. 37(4):649–667.Link, Google Scholar
• Villas-Boas JM, Winer RS (1999) Endogeneity in brand choice models. Management Sci. 45(10):1324–1338.Link, Google Scholar
• Wells JD, Valacich JS, Hess TJ (2011) What signals are you sending? How website quality influences perceptions of product quality and purchase intentions. Management Inform. Systems Quart. 35(2):373–396.Crossref, Google Scholar
• Wood SL (2001) Remote purchase environments: The influence of return policy leniency on two-stage decision processes. J. Marketing Res. 38(2):157–169.Crossref, Google Scholar
• Yoganarasimhan H (2013) The value of reputation in an online freelance marketplace. Marketing Sci. 32(6):860–891.Link, Google Scholar
• Zhang J, Li H, Yan R, Johnston C (2017) Examining the signaling effect of e-tailers’ return policies. J. Comput. Inform. Systems 57(3):191–200.Crossref, Google Scholar
• Zhang S, Lee D, Singh P, Srinivasan K (2022) What makes a good image? Airbnb demand analytics leveraging interpretable image features. Management Sci. 68(8)5644–5666.Google Scholar