臺灣博碩士論文加值系統

隨著網路的興起，越來越多的消費者為了節省時間和金錢偏好在網路上購買各種商品（食品，電子產品，服裝等）。因此近年來，電子商務產業開始蓬勃的發展。過往的研究對象主要針對英文電子商務平台，並只針對單一影響因素進行探討，如網路口碑，促銷，運輸，品牌等。而本研究欲探討中文口碑評論及促銷同時對平台銷售之影響。
本研究使用網路購物平台的資料，先以中文文字探勘進行簡單貝氏分類法將評論分為三類（正、負和中立），並透過多元迴歸分析口碑評論與促銷對自家網路購物平台及(或)其他網路購物平台之銷售影響。
本研究以中國天貓網路購物平台和京東網路購物平台之牛軋糖和鳳梨酥為對象，採用2017年11月22日至2018年3月15日之排名、價格、促銷和到貨時間作為研究數據，並採取2017年9月23日至2018年3月14日之評論作為評論數據。結果得知，天貓網路購物平台之促銷活動較常以價格優惠為主，因此對於天貓平台之牛軋糖與鳳梨酥產品而言，發送優惠券等促銷方式對銷售並無顯著的影響，反之對其價格有顯著的影響。而京東則是以優惠券或是滿額贈為主要的促銷方式，因此對京東平台之牛軋糖與鳳梨酥產品而言，價格對銷售並無顯著的影響，反之對牛軋糖之促銷有顯著影響。另外，京東網路購物平台的非正面的評論較容易影響自家的銷售；當非正面評論越多，易使銷售減少(排名下降)。因此在競爭的電子商務平台環境下，透過數據的收集和處理，可以觀察出促銷和評論對於網路平台的影響，其結果能在做相關管理決策時有參考依據

Nowadays, more and more consumers prefer to purchase a wide variety of goods (food, electronics, clothing etc) on the internet in order to save time and money. Thus the e-commerce industry of the online marketplaces has bloomed over the recent years. Previous research objects mainly aimed at English online marketplaces, and investigate only one single factor that affects online marketplaces, for instance electronic word-of-mouth (eWOM), promotion, shipping, brand etc. Therefore, the main purpose of this research is to identify whether the eWOM (review) and promotion of a product will simultaneously affect their online sales itself and other online marketplaces' sales.
This study collected data from the online shopping platform to classify online reviews into three categories (positive, negative, and neutral) using simple Bayesian classification in Chinese text mining, and analyzes word-of-mouth reviews and promotions on online shopping platforms through multiple regression analysis and examine whether theeffect of their sales will influence itself and (or) other online marketplaces' sales.
This research selected Nougat and pineapple cakes fromtmall.com and jd.com as the main product, and collected the ranking, price, promotion, and delivery time from November 22, 2017 to March 15, 2018, and the online reviews from September 23, 2017 to March 14, 2018. As a result, tmall.com's promotional activities are more based on price; therefore, for T-mall platform's nougat and pineapple products, providing promotion coupons have no significant impact on sales. Jingdong, on the other hand, uses coupons or free gifts as its main sales promotion; therefore their price has no significant effect on sales. In addition, the non-positive reviews of jd.com's online shopping platform are more likely to affect their own sales; the more non-positive reviews, the easier it is to reduce in sales (decline in rankings). Therefore, under the competitive e-commerce environment and through the collection and processing of data, the results provide useful insights to enhance the online marketplace management.

第一章　緒論 1
第一節　研究背景 1
第二節　研究動機與目的 6
第三節　研究方法 8
第四節　研究架構 9
第二章文獻探討 11
第一節影響網路購物因素之相關文獻 11
第二節口碑評論之相關文獻 15
第三節促銷活動之相關文獻 18
第四節研究方法之相關文獻 23
第三章模型建構 27
第一節銷售模型建構 28
第二節口碑模型建構 29
第三節促銷模型建構 31
第四節出貨時間模型建構 31
第五節多元迴歸模型建構 32
第四章模型案例 33
第一節銷售資料說明 33
第二節銷售資料處理 34
第三節評論資料處理 42
第四節促銷活動資料說明 48
第五節到貨時間資料說明 53
第六節多元迴歸模型與結果分析 56
第五章結論與建議 61
第一節結論 61
第二節研究限制與未來發展 62
參考文獻 63

1. 中文參考文獻
联商网 (2017) 2016中国B2C网络零售报告：天猫京东占比83.1％[online] Available at: http://www.linkshop.com.cn/web/archives/2017/377847.shtml [Accessed 15 Nov. 2017].

2. 英文參考文獻
Barber, D. (2015). Bayesian reasoning and machine learning. Cambridge: Cambridge University Press.
Chern, C., Wei, C., Shen, F. and Fan, Y. (2014). A sales forecasting model for consumer products based on the influence of online word-of-mouth. Information Systems and e-Business Management, 13(3), pp.445-473.
Chevalier, J. and Mayzlin, D. (2006). The Effect of Word of Mouth on Sales: Online Book Reviews. Journal of Marketing Research, 43(3), pp.345-354.
Chong, A., Li, B., Ngai, E., Ch'ng, E. and Lee, F. (2016).Predicting online product sales via online reviews, sentiments, and promotion strategies. International Journal of Operations & Production Management, 36(4), pp.358-383.
Fan, Z., Che, Y. and Chen, Z. (2017). Product sales forecasting using online reviews and historical sales data: A method combining the Bass model and sentiment analysis. Journal of Business Research, 74, pp.90-100.
Golob, P. (2017). Who Tops The Big 15 Ecommerce Markets in 2017 - China or US? - GFluence. [online] GFluence. Available at: https://gfluence.com/ecommerce-predictions-2017-happening-top-15-markets/ [Accessed 15 Nov. 2017].
Jamal, A., Peattie, S. and Peattie, K. (2012). Ethnic minority consumers' responses to sales promotions in the packaged food market. Journal of Retailing and Consumer Services, 19(1), pp.98-108.
Jupiter Communications. (2001). Creating loyalty: Building profitable relationships. Jupiter Vision Report: Digital Commerce, Vol. 2.
Klibanoff, P., Sandroni,A, Moselle,B, Sarantit,B. (2006). Managerial statistics: a case-based approach. Thomson South-Western.
Kutner, M., Nachtsheim, C. and Neter, J. (2008). Applied linear regression models. Boston: McGraw-Hill.
Lewis, M., Singh, V. and 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), pp.51-64
Liang, A., Yang, W., Chen, D. and Chung, Y. (2017). The effect of sales promotions on consumers’ organic food response. British Food Journal, 119(6), pp.1247-1262.
Oh, H. and Kwon, K. (2009). An exploratory study of sales promotions for multichannel holiday shopping. International Journal of Retail & Distribution Management, 37(10), pp.867-887.
Pierson, L. (2015). Data Science For Dummies. John Wiley & Sons, Inc.
Practical Ecommerce. (2007). Merchants Should Be Preparing For The Holidays Now. [online] Available at: https://www.practicalecommerce.com/Merchants-Should-Be-Preparing-For-The-Holidays-Now [Accessed 21 Dec. 2017].
Ratcliff, C. (2015). 12 illuminating ecommerce stats from January-March 2015. [online] Econsultancy. Available at: https://econsultancy.com/blog/66235-12-illuminating-ecommerce-stats-from-january-march-2015/ [Accessed 21 Dec. 2017].
Smith, M. and Brynjolfsson, E. (2001). Consumer Decision-making at an Internet Shopbot: Brand Still Matters. The Journal of Industrial Economics, 49(4), pp.541-558.
Statista (2017a). Global retail e-commerce market size 2014-2021 Statista. [online] Statista. Available at: https://www.statista.com/statistics/379046/worldwide-retail-e-commerce-sales/ [Accessed 15 Nov. 2017].
Statista (2017b). Asia Pacific retail e-commerce sales 2019 Statistic. [online] Statista. Available at: https://www.statista.com/statistics/533860/retail-e-commerce-revenue-asia-pacific/ [Accessed 15 Nov. 2017].
Statista (2017c). Alibaba e-commerce revenue by region 2017 Statistic. [online] Statista. Available at: https://www.statista.com/statistics/226793/e-commerce-revenue-of-alibabacom/ [Accessed 16 Nov. 2017].