Online product returns: A study on the strategic advantages of brands

Öz

This study examines the product return behaviors of consumers who shop for clothing from online marketplaces through predetermined variables. To achieve this aim, a survey form was created, including a section on the demographic characteristics of individuals and sections related to the research scales. The survey form includes items related to the variables of motivation and flexibility, product return behavior, and trust, satisfaction, loyalty, and reputation. Data were collected through convenience sampling and an online survey methods. Explanatory factor analysis, multiple linear regression, multiple correlation, and descriptive statistics were used to analyze the data. According to the research findings, the variables of motivation and flexibility positively and significantly affect product return behavior, and there is a positive and significant relationship between the variables of trust, satisfaction, loyalty, and reputation and product return behavior. In this context, unlike studies that generally focus on the negative aspects of product return behavior, this study focuses on the positive outcomes of product return behavior for brands and supports these findings with evidence.

Anahtar Kelimeler

• Product return behavior
• online marketplaces
• clothing

Çevrimiçi ürün iadeleri: Markaların stratejik avantajları Üzerine bir araştırma

Öz

Bu çalışmanın amacı, önceden belirlenmiş değişkenler aracılığıyla çevrimiçi pazaryerlerinden giyim alışverişi yapan tüketicilerin ürün iade davranışlarını incelemektir. Bu amacın gerçekleştirilmesine yönelik olarak bir anket formu oluşturulmuş ve bireylerin demografik özelliklerine ilişkin bir bölüm ile araştırma ölçeklerine ilişkin bölümler anket formuna yerleştirilmiştir. Anket formunda; motivasyon ve esneklik, ürün iade davranışı ve güven, memnuniyet, sadakat ve itibar değişkenlerine ilişkin soru ifadelerine yer verilmiştir. Araştırmada veriler kolayda örnekleme yöntemiyle ve çevrimiçi anket aracılığıyla ile toplanmıştır. Verilerin analiz edilmesinde; açıklayıcı faktör analizi, çoklu doğrusal regresyon, çoklu korelasyon ve betimsel istatistiklerden yararlanılmıştır. Araştırma bulgularına göre; motivasyon ve esneklik değişkenleri ürün iade davranışını pozitif ve anlamlı bir şekilde etkilemekte, güven, memnuniyet, sadakat ve itibar değişkenleri ile ürün iade davranışı arasında ise pozitif ve anlamlı bir ilişki bulunmaktadır. Bu bağlamda, genel olarak ürün iade davranışının olumsuz yönlerine odaklanan çalışmalardan farklı olarak, bu çalışmada ürün iade davranışının markalar açısından olumlu sonuçlarına odaklanılmakta ve kanıtlarla desteklenmektedir.

Anahtar Kelimeler

• Ürün iade davranışı
• çevrimiçi pazar yerleri
• giyim

Kaynakça

1. Accenture (2012). European cross-border e-commerce: The challenge of achieving profitable growth. Erişim: 17 Nisan 2023. http://www.accenture.com/SiteCollectionDocuments/PDF/Accenture-ERRT-Brochure.pdf.
2. Ahsan, K. & Rahman, S. (2022). A systematic review of e-tail product returns and an agenda for future research. Industrial Management & Data Systems, 122(1), 137-166.
3. Ambilkar, P., Dohale, V., Gunasekaran, A. & Bilolikar, V. (2022). Product returns management: A comprehensive review and future research agenda. International Journal of Production Research, 60(12), 3920-3944.
4. Anderson, E. T., Hansen, K. & Simester, D. (2009). The option value of returns: Theory and empirical evidence. Marketing Science, 28(3), 405–423. Autry, C. W. (2005). Formalization of reverse logistics programs: A strategy for managing liberalized returns. Industrial Marketing Management 34(7), 749–757.
5. Bernon, M., Upperton, J., Bastl, M. & Cullen, J. (2013). An exploration of supply chain integration in the retail product returns process. International Journal of Physical Distribution & Logistics Management, 43(7), 586-608.
6. 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.
7. Boyle, M. (2006). Best Buy’s Giant Gamble. Fortune, 153(6), 68–75.
8. Cardoni, A., Dumay, J., Palmaccio, M. & Celenza, D. (2019). Knowledge transfer in a start-up craft brewery. Bus. Process. Manag. 25, 219–243.

9. Che, Y.K. (1996). Customer return policies for experience goods, The Journal of Industrial Economics, 17–24. Dailey, L.C. & Ülkü, M.A. (2018). Retailers beware: On denied product returns and consumer behavior. Journal of Business Research, 86, 202-209.
10. Davis, S., Gerstner, E. & Hagerty, M. (1995). Money back guarantees in retailing: Matching products to consumer tastes. Journal of Retailing, 71(1), 7-22.
11. De, P., Hu, Y. & Rahman, M.S. (2012). An Empirical Investigation of the Effects of Product-Oriented Web Technologies on Product Returns, Working Paper, 1–34.
12. de Koster, R.B.M., de Brito, M.P. & van de Vendel, M.A. (2002). Return handling: an exploratory study with nine retailer warehouses. International Journal of Retail & Distribution Management, 30(8), 407-421.
13. Emmons, H. & Gilbert, S.M. (1998). Note. The role of returns policies in pricing and inventory decisions for catalogue goods. Management Science, 44(2), 276-283.
14. Ewoldt, J. (2018). Buy TV, watch Super Bowl, return TV, it still happens. Minneapolis, MN: StarTribune.com. Erişim: 2 Şubat 2024. http://www.startribune.com/buy-tv-watch-super-bowl-return-tv-it-still-happens/472393253/
15. Foscht, T., Ernstreiter, K., Maloles, C. III, Sinha, I. & Swoboda, B. (2013). Retaining or returning?. International Journal of Retail & Distribution Management, 41(2), 113-134.
16. Gurnani, H., Sharma, A. & Grewal, D. (2010). Optimal returns policy under demand uncertainty. Journal of Retailing , Vol. 86(2), 137-147.
17. Hair, J. F. (1995). Multivariate Data Analysis with Readings. Prentice Hall. International Inc, A Viacorn Company, New Jersey.
18. Heiman, A., McWilliams, B. & Zilberman, D. (2001). Demonstrations and money-back guarantees: market mechanisms to reduce uncertainty. Journal of Business Research, 54(1), 71–84.
19. Hjort, K. & Lantz, B. (2012). (R)e-tail borrowing of party dresses: an experimental study. International Journal of Retail & Distribution Management, 9(12), 997-1012.
20. Hong, Y. & Pavlou, P.A. (2014). Product fit uncertainty in online markets: nature, effects, and antecedents. Information Systems Research, 25(2), 328-344.
21. Kalaycı, Ş. (2006). SPSS Uygulamalı Çok Değişkenli İstatistik Teknikleri (Ed.). Asil Yayın Dağıtım, Ankara.
22. Ketzenberg, M. E., Abbey, J. D., Heim, G. R. & Kumar, S. (2020). Assessing customer return behaviors through data analytics. Journal of Operations Management, 66(6), 622-645. https://doi.org/10.1002/joom.1086
23. Li, X. (2013). Managing dynamic inventory systems with product returns: a Markov decision process. Journal of Optimization Theory and Applications, 157(2), 577-592.
24. Lysenko-Ryba, K. & Zimon, D. (2021). Customer behavioral reactions to negative experiences during the product return. Sustainability, 13(2), 448.
25. Marvel, H.P. & Peck, J. (1995). Demand uncertainty and returns policies. International Economic Review, 36(3), 691-714.
26. Menke, W. W. (1969). Determination of warranty reserves. Management Science, 15(10), 542.
27. Mollenkopf, D.A., Frankel, R. & Russo, I. (2011). Creating value through returns management: Exploring the marketing–operations interface. Journal of Operations Management, 29(5), 391-403.
28. Patankar, J.G. & Mitra, A. (1995). Effects of warranty execution on warranty reserve costs. Management Science, 41(3), 395-400.
29. Pei, Z. & Paswan, A. (2018). Consumers'legitimate and opportunistic product return behaviors in online shopping. Journal of Electronic Commerce Research, 19(4), 301-319.
30. Petersen, J.A. & Kumar, V. (2009). Are product returns a necessary Evil? Antecedents and consequences. Journal of Marketing, 73(3), 35–51. https://doi.org/:10.1509/jmkg.73.3.035
31. Piron, F. & Young, M. (2000). Retail borrowing: insights and implications on returning used merchandise”, International Journal of Retail & Distribution Management, 28(1), 27-36.
32. Razali, N.M. & Wah, Y.B. (2011). Power Comparisons of Shapiro-Wilk, Kolmogorov-Smirnov, Lilliefors and Anderson-Darling Tests. Journal of Statistical Modeling And Analytics, 2(1), 21-33.
33. Richey, R.G., Chen, H., Genchev, S.E. & Daugherty, P.J. (2005). Developing effective reverse logistics programs. Industrial Marketing Management, 34, 830–840.
34. Rogers, D. S., Lambert, D. M., Croxton, K. L. & García-Dastugue, S. J. (2002). The returns management process. The international journal of logistics management, 13(2), 1-18.
35. Rosenbaum, M.S. & Kuntze, R. (2003). The relationship between anomie and unethical retail disposition. Psychology & Marketing, 20, 1067–1093.
36. Statista (2019). Retail E-Commerce Sales Worldwide from 2014 to 2023, Erişim: 21 Aralık 2023. https://www.statista.com/statistics/379046/worldwide-retail-e-commerce-sales/.
37. Stock, J., Speh, T. & Shear, H. (2006). Managing product returns for competitive advantage, MIT Sloan Management Review, 48(1), 57–62.
38. Tabachnick, B.G., Fidell, L.S. & Ullman, J. B. (2013). Using multivariate statistics (6th ed.). Pearson.
39. Webster, S. & Weng, Z.K. (2000). A risk-free perishable item returns policy. Manufacturing & Service Operations Management, 2(1), 100-106.
40. 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.
41. Xu, L., Li, Y., Govindan, K. & Xu, X. (2015). Consumer returns policies with endogenous deadline and supply chain coordination. Eur. J. Oper. Res., 242, 88–99.