Abstract
One more time the online channel differs from the offline one. Our study on the product return strategies in the retail industry shows that even if more expensive for online retailers, product return policies are more generous and perceived as lenient by consumers. Our measures were collected in the Italian jeans retail industry, with a comparative study done on firms active on both channels and representing more than 50% of the overall industry sales. The impossibility to serve the customer at a distance and the need to strategically boost online sales are becoming a serious trap for online retailers.
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References
Bhutta, K.S., Huq, F.: Supplier selection problem. Supply Chain Manage. Int. J. 7(3), 126–135 (2002)
Bonifield, C., Cole, C., Schultz, R.: Product returns on the internet—a case of mixed signals. J. Bus. Res. 63(10), 1058–1065 (2010)
Choi, T.M.: Analytical Modeling Research in Fashion Business. Springer, Berlin (2016)
Chu, R.: What online Hong Kong travelers look for on airline/travel websites? Int. J. Hospitality Manage. 20(1), 95–100 (2001)
Dae-Ho, B.: The AHP approach for selecting an automobile purchase model. Inf. Manage. 38, 289–297 (2001)
De, P., Hu, Y., Rahman, M.: Product-oriented web technologies and product returns: an exploratory study. Inf. Syst. Res. 24(4), 998–1010 (2013)
Dholakia, R., Zhao, M., Dholakia, N.: Multi-channel retailing: a case study of early experiences. J. Interact. Mark. 19(2), 63–74 (2005)
Eisenhardt, K.: Building theories from case study research. Acad. Manage. Rev. 14(4), 532–550 (1989)
Govindan, K., Soleimani, H., Kannan, D.: Reverse logistics and closed-loop supply chain: a comprehensive review to explore the future. Eur. J. Oper. Res. 240, 603–626 (2015)
Grewal, D., Iyer, G.R., Levy, M.: Internet retailing: enablers, limiters, and market consequences. J. Bus. Res. 57(7), 703–713 (2004)
Griffis, S.E., Rao, S., Goldsby, T.J., Niranjan, T.T.: The customer consequences of returns in online retailing: an empirical analysis. J. Oper. Manage. 30, 282–294 (2012)
Guide, V.D., Van Wassenhove, L.N.: OR FORUM—the evolution of closed-loop supply chain research. Oper. Res. 57(1), 10–18 (2009)
Janakiraman, N., Ordónez, L.: Effect of effort and deadlines on consumer product returns. J. Consum. Psychol. 22(2), 260–271 (2012)
Janakiramana, N., Syrdalb, H.A., Freling, R.: The effect of return policy Leniency on consumer purchase and return decisions: a meta-analytic review. J. Retail. 92, 226–235 (2016)
Kalton, G., Flores-Cervantes, I.: Weighting methods. J. Official Stat. 19, 81–97 (2003)
Kasturi Rangan, V., Moriarty, R.T., Swartz, G.S.: Segmenting customers in mature industrial markets. J. Mark. 56(4), 72–82 (1992)
Kish, L.: Methods for design effects. J. Official Stat. 11, 55–77 (1995)
Liang, T., Huang, J.: An empirical study on consumer acceptance of products in electronic markets: a transaction cost model. Decis. Support Syst. 24(1), 29–43 (1998)
Rogers, D.S., Tibben-Lembke, R.S.: Going Backwards: Reverse Logistics Trends and Practices. Center for Logistics Management, University of Nevada, Reno. Reverse Logistics Executive Council, Reno (1998)
Saaty, R.W.: The analytic hierarchy process-what it is and how it is used. Math Model. 9(3–5), 161–176 (1987)
Sahoo, N., Srinivasan, S., Dellarocas, C.: The Impact of Online Product Reviews on Product Returns and Net Sales. Workshop on Information Systems Economics, Milan, Italy (2013)
Shim, S., Eastlick, M., Lotz, S., Warrington, P.: An online prepurchase intentions model: the role of intention to search. J. Retail. 77(3), 397–416 (2001)
Speights, D., Hilinski, M.: Return Fraud and Abuse: How to Protect Profits. Accessed in Sept 10, 2016 from The Retail Equation: https://www.theretailequation.com/Retailers/images/public/pdfs/whitepapers/wpTRE4013WhitePaperReturnFraud101Feb2013.pdf (2013)
Stock, J.: Development and Implementation of Reverse Logistics Programs. Oak Brook, Illinois, US (1998)
The Retail Equation: Consumer Returns in the Retail Industry. Accessed in June 12, 2016 from The Retail Equation: http://www.theretailequation.com/Retailers/images/public/pdfs/industryreports/ir2014nrfretailreturnssurvey.pdf (2014)
Tonanont, A.: Performance Evaluation in Reverse Logistics With Data Envelopment Analysis. The University of Texas at Arlington, p. 2 (2009)
Van den Poel, D., Leunis, J.: Consumer acceptance of the internet as a channel of distribution. J. Bus. Res. 45(3), 249–256 (1999)
Wood, S.L.: Remote purchase environments: the influence of return policy Leniency on two-stage decision processes. J. Mark. Res. 38(2), 157–169 (2001)
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Pennarola, F., Caporarello, L., Magni, M. (2019). Different Strategies for Different Channels: Influencing Behaviors in Product Return Policies for Consumer Goods. In: Lazazzara, A., Nacamulli, R., Rossignoli, C., Za, S. (eds) Organizing for Digital Innovation. Lecture Notes in Information Systems and Organisation, vol 27. Springer, Cham. https://doi.org/10.1007/978-3-319-90500-6_19
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