The Value of Fit Information in Online Retail: Evidence from a Randomized Field Experiment

References

• 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
• Austin PC (2011) An introduction to propensity score methods for reducing the effects of confounding in observational studies. Multivariate Behavioral Res. 46(3):399–424.Crossref, Google Scholar
• Banjo S (2013) Rampant returns plague e-retailers. Wall Street Journal (December 22), https://www.wsj.com/articles/rampant-returns-plague-eretailers-1387752786.Google Scholar
• Bell DR, Gallino S, Moreno A (2014) How to win the omnichannel retail revolution. MIT Sloan Management Rev. 56(1):45–53.Google Scholar
• Bell DR, Gallino S, Moreno A (2017) Offline showrooms in omni-channel retail: Demand and operational benefits. Management Sci. 64(4):1629–1651.Link, Google Scholar
• Blackburn JD, Guide VDR, Souza GC, Van Wassenhove LN (2004) Reverse supply chains for commercial returns. California Management Rev. 46(2):6–22.Crossref, Google Scholar
• Cameron AC, Trivedi PK (2005) Microeconometrics: Methods and Applications (Cambridge University Press, Cambridge, UK).Crossref, Google Scholar
• Carmon Z, Ariely D (2000) Focusing on the forgone: How value can appear so different to buyers and sellers. J. Consumer Res. 27(3):360–370.Crossref, Google Scholar
• Cattaneo MD (2010) Efficient semiparametric estimation of multi-valued treatment effects under ignorability. J. Econometrics 155(2):138–154.Crossref, Google Scholar
• Cordero R (2010) Can technology help eashion etailers tackle “try before you buy?” Business of Fashion (September 22), https://www.businessoffashion.com/articles/fashion-tech/fashion-2-0-can-technology-help-fashion-etailers-tackle-try-before-you-buy.Google Scholar
• Craig N, DeHoratius N, Jiang Y, Klabjan D (2014) Execution quality: An analysis of fulfillment errors in a retail distribution center. J. Oper. Management 38(September):25–40.Google Scholar
• Davis S, Gerstner E, Hagerty M (1995) Money back guarantees in retailing: Matching products to consumer tastes. J. Retailing 71(1):7–22.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
• De P, Hu Y(J), Rahman MS (2010) Technology usage and online sales: An empirical study. Management Sci. 56(11):1930–1945.Link, Google Scholar
• De P, Hu Y(J), Rahman MS (2013) Product-oriented web technologies and product returns: An exploratory study. Inform. Systems Res. 24(4):998–1010.Link, Google Scholar
• Dimoka A, Hong Y, Pavlou PA (2012) On product fit uncertainty in online markets: Theory and evidence. MIS Quart. 36(2):395–426.Google Scholar
• Gallino S, Moreno A (2014) Integration of online and offline channels in retail: The impact of sharing reliable inventory availability information. Management Sci. 60(6):1434–1451.Link, Google Scholar
• Gallino S, Moreno A, Stamatopoulos I (2017) Channel integration, sales dispersion, and inventory management. Management Sci. 63(9):2813–2831.Link, Google Scholar
• Gao F, Su X (2017) Online and offline information for omnichannel retailing. Manufacturing Service Oper. Management 19(1):84–98.Link, Google Scholar
• Garcia K (2016) Apparel retailers and digital commerce: Trends and benchmarks. eMarketer (November 7), http://totalaccess.emarketer.com/reports/viewer.aspx?r=2001945.Google Scholar
• Gu Z, Liu Y (2013) Consumer fit search, retailer shelf layout, and channel interaction. Marketing Sci. 32(4):652–668.Link, Google Scholar
• Gu Z, Xie Y (2013) Facilitating fit revelation in the competitive market. Management Sci. 59(5):1196–1212.Link, Google Scholar
• Guide VDR, Souza GC, Van Wassenhove LN, Blackburn JD (2006) Time value of commercial product returns. Management Sci. 52(8):1200–1214.Link, Google Scholar
• Guo S, Fraser MW (2009) Propensity Score Analysis: Statistical Methods and Applications, 1st ed. (SAGE Publications, Thousand Oaks, CA).Google Scholar
• Hirano K, Imbens GW, Ridder G (2003) Efficient estimation of average treatment effects using the estimated propensity score. Econometrica 71(4):1161–1189.Crossref, Google Scholar
• Hollander M, Wolfe DA, Chicken E (2013) Nonparametric Statistical Methods (John Wiley & Sons, Hoboken, NJ).Google Scholar
• Hong Y(K), Pavlou PA (2014) Product fit uncertainty in online markets: Nature, effects, and antecedents. Inform. Systems Res. 25(2):328–344.Link, Google Scholar
• Imbens GW, Rubin DB (2015) Causal Inference in Statistics, Social, and Biomedical Sciences (Cambridge University Press, Cambridge, UK).Crossref, Google Scholar
• IMRG (2014) IMRG clear returns quarterly fashion returns review: Introductory fashion retail returns report February 2014. Accessed May 1, 2018, https://www.imrg.org/res/files/CA9E994FF31.pdf.Google Scholar
• Iyengar SS, Lepper MR (2000) When choice is demotivating: Can one desire too much of a good thing? J. Personality Soc. Psych. 79(6):995–1006.Crossref, Google Scholar
• Jiang Z, Benbasat I (2007) Research note-investigating the influence of the functional mechanisms of online product presentations. Inform. Systems Res. 18(4):454–470.Link, Google Scholar
• Johnson Ferreira K, Lee BHA, Simchi-Levi D (2016) Analytics for an online retailer: Demand forecasting and price optimization. Manufacturing Service Oper. Management 18(1):69–88.Link, Google Scholar
• Kahneman D, Knetsch JL, Thaler RH (1990) Experimental tests of the endowment effect and the Coase theorem. J. Political Econom. 98(6):1325–1348.Crossref, Google Scholar
• Kim Y, Krishnan R (2015) On product-level uncertainty and online purchase behavior: An empirical analysis. Management Sci. 61(10):2449–2467.Link, Google Scholar
• Kopalle PK, Lehmann DR (1995) The effects of advertised and observed quality on expectations about new product quality. J. Marketing Res. 32(3):280–290.Crossref, Google Scholar
• Lal R, Sarvary M (1999) When and how is the Internet likely to decrease price competition? Marketing Sci. 18(4):485–503.Link, Google Scholar
• Lee D, Hosanagar K (2016) When do recommender systems work the best? The moderating effects of product attributes and consumer reviews on recommender performance. Bourdeau J, Hendler J, Nkambou R, Horrocks I, Zhao BY, eds. Proc. 25th Annual Internat. Conf. World Wide Web, WWW 2016 (ACM, Montreal), 85–97.Google Scholar
• Li H, Daugherty T, Biocca F (2003) The role of virtual experience in consumer learning. J. Consumer Psych. 13(4):395–407.Crossref, Google Scholar
• Netessine S, Rudi N (2006) Supply chain choice on the Internet. Management Sci. 52(6):844–864.Link, Google Scholar
• Ng S, Stevens L (2015) Where your unwanted Christmas gifts get a second life. Wall Street Journal (December 27), http://www.wsj.com/articles/where-your-unwanted-christmas-gifts-get-a-second-life-1451212201.Google Scholar
• Norton MI, Mochon D, Ariely D (2012) The “IKEA effect”: When labor leads to love. J. Consumer Psych. 22(3):453–460.Crossref, Google Scholar
• Ofek E, Katona Z, Sarvary M (2011) Bricks and clicks: The impact of product returns on the strategies of multichannel retailers. Marketing Sci. 30(1):42–60.Link, 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
• Randall T, Netessine S, Rudi N (2006) An empirical examination of the decision to invest in fulfillment capabilities: A study of Internet retailers. Management Sci. 52(4):567–580.Link, Google Scholar
• Retana GF, Forman C, Wu DJ (2016) Proactive customer education, customer retention, and demand for technology support: Evidence from a field experiment. Manufacturing Service Oper. Management 18(1):34–50.Link, Google Scholar
• Rosenbaum PR (2010) Design of Observational Studies, Vol. 10 (Springer, New York).Crossref, Google Scholar
• Rosenbaum PR, Rubin DB (1983) The central role of the propensity score in observational studies for causal effects. Biometrika 70(1):41–55.Crossref, Google Scholar
• Salop SC (1979) Monopolistic competition with outside goods. Bell J. Econom. 19(1):141–156.Crossref, Google Scholar
• Shulman JD, Coughlan AT, Savaskan RC (2009) Optimal restocking fees and information provision in an integrated demand-supply model of product returns. Manufacturing Service Oper. Management 11(4):577–594.Link, Google Scholar
• Steuer J (1992) Defining virtual reality: Dimensions determining telepresence. J. Comm. 42(4):73–93.Crossref, Google Scholar
• Su X (2009) Consumer returns policies and supply chain performance. Manufacturing Service Oper. Management 11(4):595–612.Link, Google Scholar
• Tang C (2016) Avoiding the boomerang effect of online sales returns. (March 21), http://blogs.anderson.ucla.edu/global-supply-chain/2016/03/avoiding-the-boomerang-effect-of-online-sales-returns.html.Google Scholar
• Wigder ZD (2014) Key metrics in Brazil’s ecommerce market. (October 21), https://go.forrester.com/blogs/14-10-21-key_metrics_in_brazils_ecommerce_market/.Google Scholar
• Wooldridge JM (2010) Econometric Analysis of Cross Section and Panel Data, Second Edition (MIT Press, Cambridge, MA).Google Scholar