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    Title: 從顧客價值創造觀點探討退貨改善策略之研究
    A Study on the Improvement of Returns Strategy from the Perspective of Customer Value Creation
    Authors: 賴靜如
    Lai, Ching-Ju
    Contributors: 李易諭
    賴靜如
    Lai, Ching-Ju
    Keywords: 退貨率
    退貨政策
    退貨服務品質
    顧客忠誠度
    電商服飾業
    Product return rate
    Return policy
    Return service performance
    Customer loyalty
    Apparel e-commerce
    Date: 2022
    Issue Date: 2022-09-02 15:48:19 (UTC+8)
    Abstract: 如今線上購物已然成為許多人更為習慣的購物方式,但電商通路在為企業帶來效益的同時,對於忠實且完整呈現實體產品的資訊有其限制性,消費者無法實際確認產品資訊,可能致使消費者對於產品抱有錯誤的期待,期待與商品表現的落差不僅衍生出越發高漲的退貨率,也會降低顧客對企業服務的感知價值,而根據 Statista 2021 年的統計顯示,共有 88%的線上購物消費者表示過去曾經退貨過衣服類商品,遠高於其他的商品種類,故本研究希望能夠從零售服飾電商的角度切入,並選擇在台灣最早開始經營服飾電商品牌的零頭 O 公司作為個案公司進行研究,探討如何降低服飾電商退貨對企業顧客價值的影響。
    本研究透過 O 公司所提供 2021 年 1 月至 10 月的一手退貨資料以及針對 O公司高忠誠度顧客進行訪談,瞭解 O 公司目前的退貨現況,並檢視 O 公人的退貨預防機制是否有效,進而提出退貨率改善建議,降低顧客期待與實體商品的落差。另一方面也透過分析顧客感知 O 公司的退貨政策合理性及退貨服務品質,來檢視當退貨發生後,O 公司如何將其視為服務補救能夠重新創造顧客價值的機會,透過其退貨服務來鞏固顧客忠誠度。
    本研究發現,O 公司 80%以上的退貨都源自於消費者於訂購時對於尺寸及版型產生的期待與實際商品表現之間的落差,可善加利用顧客過去的訂單資料創造個人化的尺寸推薦系統來改善。另一方面,O 公司採行寬鬆的退貨政策,並具有另顧客滿意的退貨服務品質,受訪顧客表示 O 公司方便多元的退貨管道及寬鬆的退貨限制是吸引他們成為忠實顧客的主因之一,驗證了過去研究發現寬鬆退貨政策的制定及良好的退貨服務品質有助於提升顧客的忠誠度。
    With the development of information technology, people are more and more used to shopping online through e-commerce platforms. However, while e-commerce channels bring benefits to enterprises, there are limitations to presenting the information of physical products completely and truly through online platforms which results in the gaps between customers’ expectations and the performance of the actual product and leads to the increasingly high return rate. Base on a report from Statista, the return rates on apparels are much higher than other kinds of products. Therefore, this study hopes to explore how to reduce the impact of returns on business from the perspective of apparel e-commerce. This study selected O Company, one of the leaders of apparel e-commerce brands in Taiwan, as the case company to study how to reduce the impact of returns on enterprises.
    By analyzing the return records provided by Company O and interviewing high loyalty customers of the company, this research aims for understanding the effectiveness of the return prevention mechanism of Company O and how Company O managed to maintain the customers’ loyalty through its return service after a return occurs.
    The result of this research indicates that over 80% of Company O`s returns are due to the gap between the actual product performance and consumers` expectations toward size and pattern during the purchasing stage. It is suggested that Company O make good use of the historical purchase data to develop personalized size recommendation system to lower its return rate. On the other hand, Company O adopts a lenient return policy and provides the return service with high quality that increases customers’ satisfaction. The interviewed customers said that Company O’s convenient and diverse return channels and lenient return policy are one of the main reasons for them to become loyal customers which is consistent to the past research about how the lenient return policy and good return service performance can helpimprove customer loyalty.
    Reference: 2021年阿里巴巴財政年度報告(2022)。檢自https://www.alibabagroup.com/cn/ir/report (Apr. 15, 2022)
    Line Biz – Solutions。OB 嚴選運用 Cross Targeting 助攻,母親節檔期營業額一舉成長4.7倍。檢自https://tw.linebiz.com/case-study/ob-1/ (Apr. 15, 2022)
    Meta ( 2021 )。掌握四大趨勢,贏得2022數位市場。檢自https://www.facebook.com/business/news/how-to-win-with-digital-shoppers-in-2022-and-beyond?locale=zh_TW&content_id=UMwaP7OSv1xsow9(Apr. 15, 2022)
    王信文、何巧齡(2006)。影響網路購物行為之關鍵因素分析。經營管理論叢,2(1),1-28。
    朱柔若(譯)(2000)。社會研究方法-質化與量化取向。台灣:揚智文化。(Neuman, W. L., 1994)
    朱海成(2019)。電子商務概論與前瞻:跨境電商、行動商務、大數據。臺北:碁峰資訊。
    尚榮安(譯)(2001)。個案研究法。台北:揚智文化。(Yin, R. K., 1994)
    林金定、嚴嘉楓、陳美花(2005)。質性研究方法:訪談模式與實施步驟分析。身心障礙研究季刊,3(2),122-136。
    政府資料開放平臺。營利事業家數-按地區別及稅務行業別分(107~111年)。檢自https://data.gov.tw/dataset/90556(Apr. 11, 2022)
    張筱祺(2021)。台灣社群與通訊使用行為調查:團購與直播 。資策會。檢自https://mic.iii.org.tw/AISP/ChartS?docid=PPT1100914-2(Apr. 11, 2022)
    產業價值鏈資訊平台。紡織產業鏈簡介。 檢自https://ic.tpex.org.tw/introduce.php?ic=O000(Apr. 11, 2022)
    黃子恬、莊懿妃、黃新福(2021)。全通路零售的退貨政策與退貨服務品質對忠誠度之影響。商略學報,13(2),103-116。
    楊政學(2005)。實務專題製作:企業研究方法的實踐。台北:新文京出版社。
    經濟部統計處(2016)。產業經濟統計簡訊。檢自https://www.moea.gov.tw/Mns/dos/bulletin/Bulletin.aspx?kind=9&html=1&menu_id=18808&bull_id=9673 (Apr. 11, 2022)
    經濟部統計處(2021)。批發、零售及餐飲業經營實況調查報告。檢自https://www.moea.gov.tw/MNS/dos/content/ContentLink.aspx?menu_id=9431 (Apr. 11, 2022)
    經濟部統計處。零售業網路銷售額統計調查。檢自https://dmz26.moea.gov.tw/GMWeb/investigate/InvestigateEA05.aspx(Apr. 11, 2022)
    廖珈燕(2022)。2021年網購消費意向調查:網路購物經驗(上)。資策會。檢自https://mic-iii-org-tw.autorpa.lib.nccu.edu.tw/AISP/ChartS?docid=PPT1110331-2 (Apr. 11, 2022)
    數位時代(2022)。【圖解】直播電商轉換率,比傳統電商高10倍?台灣如何打出獨步全球「+1」模式?。檢自https://www.bnext.com.tw/article/68005/live-commerce(Apr. 11, 2022)
    盧希鵬(2009)。長尾效應下的經營模式與電子化策略。台北:雙葉書廊。
    蘇柏全、蔡宇智(2015)。網購退貨:顧客服務構面、產品構面、安全構面、退貨機率。Electronic Commerce Studies,13(2),141-166。
    Abdulla, H., Abbey, J. D., & Ketzenberg, M. (2022). How consumers value retailer`s return policy leniency levers: An empirical investigation. Production and Operations Management, 31(4), 1719-1733.
    Ahsan, K., & Rahman, S. (2016). An investigation into critical service determinants of customer to business (C2B) type product returns in retail firms. International Journal of Physical Distribution and Logistics Management, 46(6–7), 606–633.
    Appriss Retail. (2020). Consumer Returns in the Retail Industry 2020. Retrieved from https://apprisscommerce.com/wp-content/uploads/sites/4/2021/01/AR3020-2020-Customer-Returns-in-the-Retail-Industry.pdf (Apr. 15, 2022)
    Appriss Retail. (2021). Consumer Returns in the Retail Industry 2021. Retrieved from https://cdn.nrf.com/sites/default/files/2022-01/Customer%20Returns%20in%20the%20Retail%20Industry%202021.pdf (Apr. 15, 2022)
    Balu, N., Fares, M., & Baertlein, L. (2020). Next up for retailers: A big wave of gift returns. Reuters. Retrieved from https://www.reuters.com/article/us-usa-holidayshopping-returns-idUSKBN28Z0O2 (Apr. 17, 2022)
    Bougie, R., & Sekaran, U. (2019). Research methods for business: A skill building approach. West Sussex, U.K :John Wiley & Sons.
    Chang, H. H., & Wang, H. W. (2011). The moderating effect of customer perceived value on online shopping behaviour. Online Information Review, 35(3), 333-359.
    Chatterjee, S., & Datta, P. (2008). Examining inefficiencies and consumer uncertainty in e-commerce. Communications of the Association for Information Systems, 22(1), 29.
    Chen, B., & Chen, J. (2017). When to introduce an online channel, and offer money back guarantees and personalized pricing? European Journal of Operational Research, 257(2), 614–624.
    Coiro J, Knobel M, Lankshear C, Leu DJ. (2008). Handbook of research on new literacies. New York : Routledge.
    Coppola, D. (2020). Global e-commerce share of retail sales 2023. Statista. Retrieved from https://www.statista.com/statistics/534123/e-commerce-share-of-retail-sales-worldwide/ (Apr. 14, 2022)
    Cox, J., & Dale, B. G. (2001). Service quality and e‐commerce: an exploratory analysis. Managing Service Quality: An International Journal, 11(2), 121-131.
    Cui, T. H., Ghose, A., Halaburda, H., Iyengar, R., Pauwels, K., Sriram, S., Tucker, C., & Venkataraman, S. (2021). Informational Challenges in Omnichannel Marketing: Remedies and Future Research. Journal of Marketing, 85(1), 103–120.
    Davis, S., Hagerty, M., & Gerstner, E. (1998). Return policies and the optimal level of “hassle.” Journal of Economics and Business, 50(5), 445–460.
    Ertekin, N., & Agrawal, A. (2020). How Does a Return Period Policy Change Affect Multichannel Retailer Profitability? Manufacturing & Service Operations Management, 23(1), 210–229.
    Gelbrich, K., Gäthke, J., & Hübner, A. (2017). Rewarding customers who keep a product: How reinforcement affects customers’ product return decision in online retailing. Psychology & Marketing, 34(9), 853–867.
    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.
    Holbrook, M. B. (2005). Customer value and autoethnography: subjective personal introspection and the meanings of a photograph collection. Journal of Business Research, 58(1), 45-61.
    International Trade Administration. (2021). Impact of COVID Pandemic on eCommerce. Retrieved from https://www.trade.gov/impact-covid-pandemic-ecommerce (Apr. 15, 2022)
    International Trade Administration. (2022). eCommerce Sales & Size Forecast. Retrieved from https://www.trade.gov/ecommerce-sales-size-forecast (Apr. 15, 2022)
    Kalpoe, R. (2020). Technology acceptance and return management in apparel e-commerce. Journal of Supply Chain Management Science, 1(3–4), 118–137.
    Kang, M., & Johnson, K. (2009). Identifying characteristics of consumers who frequently return apparel. Journal of Fashion Marketing and Management: An International Journal, 13(1), 37-48.
    Kaushik, V., Kumar, A., Gupta, H., & Dixit, G. (2020). Modelling and prioritizing the factors for online apparel return using BWM approach. Electronic Commerce Research, 1-31.
    Keeney, R. L. (1999). The value of Internet commerce to the customer. Management Science, 45(4), 533-542.
    Kim, J., & Damhorst, M. L. (2010). Effects of level of internet retailer’s service quality on perceived apparel quality, perceived service quality, perceived value, satisfaction, and behavioral intentions toward an internet retailer. Clothing and Textiles Research Journal, 28(1), 56-73.
    Kotler, P., Ang, S. H., Leong, S. M., & Tan, C. T. (1999). Marketing Management: An Asian Perspective. Harlow: Pearson.
    Lohse, T., Kemper, J., & Brettel, M. (2017, June 5-10). How online customer reviews affect sales and return behavior–an empirical analysis in fashion e-commerce. 25th European Conference on Information Systems (ECIS), Guimarães, Portugal. 2635-2644
    McKinsey & Company. (2021). It’s showtime! How live commerce is transforming the shopping experience. Retrieved from https://www.mckinsey.com/business-functions/mckinsey-digital/our-insights/its-showtime-how-live-commerce-is-transforming-the-shopping-experience (Apr. 17, 2022)
    Mialki, S. (2019). Hyper-Personalized Marketing: How to Do It Right with 3 Examples to Prove It. Instapage. Retrieved from https://instapage.com/blog/hyper-personalization (Apr. 20, 2022)
    Microsoft Canada. (2015). Attention spans. Retrieved from https://dl.motamem.org/microsoft-attention-spans-research-report.pdf (Apr. 17, 2022)
    Minichiello V., Aroni R., Timewell E. & Alexander L. (1995). In-depth Interviewing (2nd ed.). South Melbourne: Longman.
    Molinillo, S., Gomez-Ortiz, B., Pérez-Aranda, J., & Navarro-García, A. (2017). Building customer loyalty: The effect of experiential state, the value of shopping, and trust and perceived value of service on online clothes shopping. Clothing and Textiles Research Journal, 35(3), 156-171.
    Neely, A., Gregory, M., & Platts, K. (2005). Performance measurement system design: A literature review and research agenda. International Journal of Operations & Production Management, 25(12), 1228-1263.
    Oliver, R. L. (1999). Whence consumer loyalty?. Journal of Marketing, 63(4_suppl1), 33-44.
    Osborne, N., & Grant-Smith, D. (2021). In-Depth Interviewing. In Methods in Urban Analysis. Singapore : Springer. 105-125
    Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1985). A conceptual model of service quality and its implications for future research. Journal of Marketing, 49(4), 41-50.
    Pei, Z., Paswan, A. & Yan, R. (2014). E-tailer’s return policy, consumer’s perception of return policy fairness and purchase intention. Journal of Retailing and Consumer Services, 21(3), 249-257.
    Pratminingsih, S. A., Pramita, N. C., & Bahri, S. (2022). The effect of reference group, online review and product return policy on online purchasing decisions. Central Asia and the Caucasus, 23(1).
    Radhi, M., & Zhang, G. (2019). Optimal cross-channel return policy in dual-channel retailing systems. International Journal of Production Economics, 210, 184–198.
    Ren, M., Liu, J., Feng, S., & Yang, A. (2021). Pricing and return strategy of online retailers based on return insurance. Journal of Retailing and Consumer Services, 59, 102-350.
    Saarijärvi, H., Sutinen, U. M., & Harris, L. C. (2017). Uncovering consumers’ returning behaviour: a study of fashion e-commerce. International Review of Retail, Distribution and Consumer Research, 27(3), 284–299.
    Sajjanit, C., & Rompho, N. (2019). Measuring customer-oriented product returns service performance. International Journal of Logistics Management, 30(3), 772–796.
    Statista. (2022). E-commerce: reverse logistics costs in United States 2020. Retrieved from https://www.statista.com/statistics/872773/e-commerce-reverse-logistics-cost-united-states/ (Apr. 15, 2022)
    Statista. (2022). Global Retail E-Commerce Market Size 2014-2021. Retrieved from https://www.statista.com/statistics/379046/worldwide-retail-e-commerce-sales/ (Apr. 15, 2022)
    Statista. (2022). Reverse logistics - most returned items in the U.S. 2021. Retrieved from https://www.statista.com/statistics/806122/most-returned-items-reverse-logistics-united-states/ (Apr. 15, 2022)
    Ulaga, W. (2003). Capturing value creation in business relationships: A customer perspective. Industrial Marketing Management, 32(8), 677-693.
    Verhoef, P. C., Kannan, P. K., & Inman, J. J. (2015). From Multi-Channel Retailing to Omni-Channel Retailing: Introduction to the Special Issue on Multi-Channel Retailing. Journal of Retailing, 91(2), 174–181.
    Wachter, K., Vitell, S. J., Shelton, R. K., & Park, K. (2012). Exploring consumer orientation toward returns: unethical dimensions. Business Ethics: A European Review, 21(1), 115-128.
    Walsh, G., Möhring, M., Koot, C., & Schaarschmidt, M. (2014). Preventive Product Returns Management Systems-a Review and Model. In ECIS.
    Wang, Y., Anderson, J., Joo, S. J., & Huscroft, J. R. (2019). The leniency of return policy and consumers’ repurchase intention in online retailing. Industrial Management & Data Systems, 120(1), 21-39.
    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.
    Woodruff, R. B. (1997). Customer value: the next source for competitive advantage. Journal of the Academy of Marketing Science, 25(2), 139-153.
    Wu, L. Y., Chen, K. Y., Chen, P. Y., & Cheng, S. L. (2014). Perceived value, transaction cost, and repurchase-intention in online shopping: A relational exchange perspective. Journal of Business Research, 67(1), 2768-2776.
    Yalabik, B., Petruzzi, N.C., & Chhajed, D. (2005). An integrated product returns model with logistics and marketing coordination. European Journal of Operational Research, 161(1), 162-182.
    Yu, Y., & Kim, H. S. (2019). Online retailers’ return policy and prefactual thinking: An exploratory study of USA and China e-commerce markets. Journal of Fashion Marketing and Management, 23(4), 504–518.
    Zeithaml, V. A. (1988). Consumer perceptions of price, quality, and value: a means-end model and synthesis of evidence. Journal of Marketing, 52(3), 2-22.
    Zeithaml, V. A., Berry, L. L., & Parasuraman, A. (1996). The behavioral consequences of service quality. Journal of Marketing, 60(2), 31-46.
    Description: 碩士
    國立政治大學
    企業管理研究所(MBA學位學程)
    109363005
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0109363005
    Data Type: thesis
    DOI: 10.6814/NCCU202201208
    Appears in Collections:[MBA Program] Theses

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