政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/143843

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/143843

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政大典藏 > College of Commerce > Department of MIS > Theses > Item 140.119/143843

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/143843

Title:	亞馬遜物流與跨境電商績效：以交互固定效果分析 Fulfillment by Amazon and Cross-Border E-Commerce Performance: An Interactive Fixed-Effects Approach
Authors:	蕭郁儒 Hsiao, Yu-Ru
Contributors:	周彥君莊皓鈞 Chou, Yen-Chun Chuang, Hao-Chun 蕭郁儒 Hsiao, Yu-Ru
Keywords:	電子商務訂單履行追蹤資料處方效果交互固定效應一般化合成控制法 e-Commerce Order Fulfillment Panel Data Treatment Effect Interactive Fixed Effect Generalized Synthetic Control Methods
Date:	2022
Issue Date:	2023-03-09 18:39:13 (UTC+8)
Abstract:	訂單履行是供應鏈管理的關鍵流程，而電子零售商的成功及市場主導地位，將取決於有效的訂單履行流程。對於電子商務賣家而言，他們期望能為商品選擇最佳的訂單履行渠道，使其在滿足消費者需求的同時，也降低總交付成本。因此，本文專注於研究訂單履行渠道對跨境電商的銷售量、退貨量和毛利，分別帶來什麼樣的影響，並採用跨境平台Amazon上的真實資料，針對一家中國跨境電子商務品牌的禮服銷售資料進行實證分析。該品牌的商品根據其訂單履行渠道在銷售期間是否轉換，可以分為兩類，一類是訂單履行渠道始終維持賣家物流(Fulfillment by Seller, FBS)的商品，另一類是訂單履行渠道從賣家物流(FBS)轉換到亞馬遜物流(Fulfillment by Amazon, FBA)的商品，因此本研究將亞馬遜物流(FBA)視為處方(Treatment)，並利用交互固定效應模型(Interactive Fixed Effect)和一般化合成控制法(Generalized Synthetic Control Method)來估計亞馬遜物流的處方效果。本研究結果顯示，相較於雙向固定效應，交互固定效應可以透過共同因素(Common Factor)和因素負荷量(Factor Loadings)，控制未觀察到的異質性(Heterogeneity)，證實了在實務問題中交互固定效應可以更準確地反映現實。此外，從交互固定效應的分析結果，我們也可以進一步推論亞馬遜物流對三個因變量的處方效果。最後，本研究也採用了一般化合成控制法，透過反事實(Counterfactual)的估計，進行更嚴謹的模型推論，同時檢驗亞馬遜物流與因變量之間是否存在因果關係。本研究所採用的兩個分析方法，在過往研究中大多應用於社會科學領域，用來推估政策或法令推行後，對整體社會或國家的影響；而今，我們將其應用於訂單履行的決策上，期望能為電子零售與供應鏈管理的計量研究方法論帶來貢獻。 Order fulfillment is a critical process in supply chain management, and the success and market dominance of e-retailers will depend on an effective order fulfillment process. For e-retailers, they expect to choose the best order fulfillment channel for their products, so that they can meet consumer demand while reducing the total cost of delivery. Therefore, we focus on studying the impact of order fulfillment options on the order quantity, returned quantity and gross profit, respectively. We use the real data on Amazon to conduct an empirical analysis on the transaction records of a Chinese cross-border e-retailer. This e-retailer sells wedding dress and full-dress products and their products can be divided into two categories. Some were fulfilled entirely by seller (FBS) during their life cycles on Amazon, the others were first fulfilled by seller (FBS) before being transferred to Fulfillment by Amazon (FBA). Hence, we consider FBA as “Treatment”, and use Interactive Fixed Effects and Generalized Synthetic Control Method to estimate the treatment effect of FBA. Our findings suggest that Interactive Fixed Effects can control for unobserved heterogeneity through common factors and factor loadings, so it can estimate the treatment effect of FBA more accurately than Two-Way Fixed Effects. In addition, we conduct a more rigorous analysis by Generalized Synthetic Control method, which enables us to estimate counterfactuals and examine whether there is a causal relationship between the treatment and the dependent variables. This research provides a new methodology of treatment identification problems, and we expect to make a contribution to the quantitative research in the field of e-retailer and supply chain management.
Reference:	Acimovic, J., & Graves, S. (2015). Making better fulfillment decisions on the fly in an online retail environment. Manufacturing & Service Operations Management, 17(1), 34-51. Andrews, J., Farias, V., Khojandi, A., & Yan, C. (2019). Primal–Dual Algorithms for Order Fulfillment at Urban Outfitters. Journal on Applied Analytics, 49(5), 355-370. Bai, J. (2009). Panel data models with interactive fixed effects. Econometrica, 77(4), 1229-1279. Bada, O., & Liebl, D. (2014). The R-package phtt: Panel Data Analysis with Heterogeneous Time Trends. arXiv preprint arXiv:1407.6484. Camilleri, M. (2021). E-commerce websites, consumer order fulfillment and after-sales service satisfaction: The customer is always right, even after the shopping cart check-out. Journal of Strategy and Management, 15(3), 377-396. Christian, R., & Stewart, W. (1965). Industrial Marketing: Physical Distribution: Key to Improved Volume and Profits. Journal of Marketing, 29(1), 65-70. Davis-Sramek, B., Mentzer, J., & Stank, T. (2008). Creating consumer durable retailer customer loyalty through order fulfillment service operations. Journal of operations management, 26(6), 781-797. Dholakia, R., & Zhao, M. (2010). Effects of online store attributes on customer satisfaction and repurchase intentions. International journal of retail & distribution management, 38(7), 482-496. Kim, D., & Oka, T. (2014). Divorce law reforms and divorce rates in the USA: an interactive fixed‐effects approach. Journal of Applied Econometrics, 29(2), 231-245. Lantz, B., & Hjort, K. (2013). Real e-customer behavioural responses to free delivery and free returns. Electronic Commerce Research, 13(2), 183-198. Leung, K., Choy, K., Siu, P., Ho, G., Lam, H., & Lee, C. (2018). A B2C e-commerce intelligent system for re-engineering the e-order fulfilment process. Expert Systems with Applications, 91, 386-401. Lewis, M., Singh, V., & Fay, S. (2006). An empirical study of the impact of nonlinear shipping and handling fees on purchase incidence and expenditure decisions. Marketing Science, 25(1), 51-64. Lim, S., Jin, X., & Srai, J.(2018). Consumer-driven e-commerce: A literature review, design framework, and research agenda on last-mile logistics models. International Journal of Physical Distribution & Logistics Management. Liu, L., Wang, Y., & Xu, Y. (2021). A practical guide to counterfactual estimators for causal inference with time-series cross-sectional data. arXiv preprint arXiv:2107.00856. Petersen, J., & Kumar, V. (2010). Can product returns make you money?. MIT Sloan Management Review, 51(3), 85. Pyke, D., Johnson, M., & Desmond, P. (2001). E-fulfillment. Supply Chain Management Review, 27(5), 50-62. Qi, M., Mak, H., & Shen, Z. (2020). Data‐driven research in retail operations—A review. Naval Research Logistics (NRL), 67(8), 595-616. Ricker, F., & Kalakota, R. (1999). Order fulfillment: the hidden key to e-commerce success. Supply chain management review, 11(3), 60-70. Sun, L., Lyu, G., Yu, Y., & Teo, C. (2021). Cross-border e-commerce data set: Choosing the right fulfillment option. Manufacturing & Service Operations Management, 23(5), 1297-1313. Thirumalai, S., & Sinha, K. (2005). Customer satisfaction with order fulfillment in retail supply chains: implications of product type in electronic B2C transactions. Journal of Operations Management, 23(3-4), 291-303. Xu, P. (2005). Order fulfillment in online retailing: What goes where. Doctoral dissertation, Massachusetts Institute of Technology. Xu, Y. (2017). Generalized synthetic control method: Causal inference with interactive fixed effects models. Political Analysis, 25(1), 57-76.
Description:	碩士國立政治大學資訊管理學系 109356007
Source URI:	http://thesis.lib.nccu.edu.tw/record/#G0109356007
Data Type:	thesis
Appears in Collections:	[Department of MIS] Theses

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