| 研究生: |
林長億 Chang-Yi Lin |
|---|---|
| 論文名稱: |
賣場能見度及其銷售績效之典型相關分析 Canonical Analysis Between Marketplaces’ Visibility and PerformanceEmpirical Study of Popular Online Auction Website’s Buyout Women Clothing |
| 指導教授: |
何靖遠
Chin-Yuan Ho |
| 口試委員: | |
| 學位類別: |
碩士 Master |
| 系所名稱: |
管理學院 - 工業管理研究所 Graduate Institute of Industrial Management |
| 畢業學年度: | 99 |
| 語文別: | 中文 |
| 論文頁數: | 75 |
| 中文關鍵詞: | 賣場績效 、直購價 、資訊不對稱 、女裝網拍 、典型相關分析 |
| 外文關鍵詞: | women clothing online auction, buyout prices, information asymmetry, canonical analysis, marketplaces performance |
| 相關次數: | 點閱:18 下載:0 |
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網路拍賣已經是一種非常普遍的線上交易方式。然而交易雙方的資訊不對稱,始終是網路拍賣中最令人關切的。根據文獻和實際觀察,總體評價是拍賣頁面中最容易被顧客所取得的資訊,而這也是呈現賣家過往交易紀錄的一項重要指標。根據調查顯示,女性網友喜歡在網路賣場購買服飾,所以網拍的種類當中女性服飾的數量通常很大,拍賣大多採取直購價的形式。因此本研究的問題為:在女裝直購價的拍賣方式下,賣場能見度與賣場績效是否有關係?商品價格與賣場績效有何關係? 能見度的各個指標之影響程度為何?
本研究自行開發網頁耙取代理人程式,大量抓取Yahoo!台灣奇摩拍賣為期兩個月,七項女裝交易資料進行分析。本研究分別以賣場觀點和商品觀點進行賣場績效的典型相關分析。賣場能見度是綜合賣場的總體評價、賣場評價的排名、排名頁次、和頁排名等來表示,賣場績效則是以成交人次和每日平均成交量來衡量,以賣場觀點就是用典型相關分析檢驗賣場能見度和賣場績效之間的相關程度;商品觀點則是檢驗商品價格和賣場績效之間的相關程度。最後用交叉驗證的方法檢驗外部效度。由賣場觀點的典型相關分析發現,賣場能見度和賣場績效之間高度相關,其中總體評價的影響最大,其次是總體評價排名和排名頁次,二者對賣場績效均有部分影響力而商品在頁面的位置順序(頁排名)對賣場績效影響不大;商品觀點的典型相關分析發現,商品價格在女裝直購價拍賣中,對賣場績效是一個重要但是影響力低的因素。
Online auction has gained its popularity among traders. However, the issue of information asymmetry has always been concerned in online auction. According to literature and observation, the marketplaces overall ratings is not only the easiest information for the buyer to refer but also the evidence of transaction history. Empirical studies have shown that most female buyers prefer buying clothing in online marketplaces. We want to know on the condition that women clothing and buyout prices: (1)How are the relationship between the visibility of the seller in online marketplaces and its performance? (2)How are the relationship between the product price and the seller’s online market performance? How’s the influence of each visibility indicators.
We developed an automated agent-based program to collect two-month-period transaction data of seven kinds of different women clothing. We take sellers’ perspective and products’ perspective respectively. Marketplaces visibility construct is composed of(1) marketplace overall ratings(2)ratings orders(3)ratings orders’ pages(4)ratings orders of each page, and marketplaces performance construct comprise (1)number of transactions(2)daily average transaction volume. Then we used canonical analysis to examine these two constructs’ relationship on sellers’ perspective. After that, we used products’ perspective to examine product prices and marketplaces performance construct’s relationship. And using cross validation to make sure its external validity. We found out from sellers’ perspective marketplaces overall ratings is an important and positive influential factor. From products’ perspective, we found out product price is an important but low influential factor to marketplaces performance.
【英文文獻】:
1. Ba, S. and Pavlou, P. A. (2002), “Evidence of the Effect of Trust Building Technology in Electronic Markets: Price Premiums and Buyer Behavior,” MIS Quarterly, 26(3), 243-268.
2. Bajari, P. and Hortacsu, A. (2004), “Economic Insights from Internet Auctions,” Journal of Economic Literature, 42(2), 457-486.
3. Bajari, P. and Hortaçsu, A. (2004), “Economic Insights from Internet Auctions,” Journal of Economic Literature, 42(2), 457-486.
4. Bapna, R., Goes, P., Gupta, A. and Jin, Y. (2004), “User Heterogeneity and Its Impact on Electronic Auction Market Design: An Empirical Exploration,” MIS Quarterly, 28(1), 21-43.
5. Baron, R.M. and Kenny, D.A. (1986), “The Moderator-Mediator Variable Distinction in Social Psychological Research: Conceptual, Strategic, and Statistical Considerations,” Journal of Personality and Social Psychology, 51(6), 1173-1181.
6. Cohen, J., (1988),“Statistical power analysis for the behavioral sciences”, Routledge 5th ed.
7. Cooley, R., Mobasher, B. and Srivastava, J. (1997), “Web Mining: Information and Pattern Discovery on the World Wide Web,” Tools with Artificial Intelligence. Proceedings., Ninth IEEE International Conference on Tools with Artificial Intelligence, 558-567.
8. Dellarocas, C. (2003), “The Digitization of Word-of-Mouth: Promise and Challenges of Online Feedback Mechanisms,” Management Science, 49(10), 1407-1424.
9. Elliott, R. K. and Jacobson, P. D. (1994), “Costs and Benefits of Business Information Disclosure,” Accounting Horizons, 8(4), 80-96.
10. Ferdous, Md. S. (2008), “Implementing e-Auctions with Sharemind”, MTAT.07.006 Research Seminar in Cryptography.
11. Gregg, D. G. and Walczak, S. (2008), “Dressing Your Online Auction Business for Success: An Experiment Comparing Two eBay Businesses,” MIS Quarterly, 32(3), 653-670.
12. Hair, J. Jr., Anderson, R.,Tatham, R. and Black, W. (1998), “Multivariate Data Analysis”, Prentice-Hill, 5th ed, 18-19.
13. Kinnear, T. and Taylor, J. (1996), “Marketing Research”, McGraw-Hill, 644-645.
14. Kobayashi, M. and Takeda, K. (2000), “Information Retrieval on the Web,” ACM Computer Surveys, 32(2), 144-173.
15. Kauffman, R. J. and Wood, C.A. (2004), “Doing Their Bidding: An Empirical Examination of Factors that Affect a Buyer’s Utility in Internet Auctions,” Information Technology and Management, 7(3), 171-190.
16. Lawrence, S. and Giles, C. L., (1999), “Accessibility of Information on the Web,” Nature, 400(6740), 107-109.
17. Lucking-Reiley, D., Bryan, D., Prasad, N. & Reeves, D. (2007), “Pennies from eBay: the Determinants of Price in Online Auctions,” Journal of Industrial Economics, 55(2), 223-233.
18. Lucking-Reiley, D. (2000), “Auctions on the Internet: What’s Being Auctioned, and How?” The Journal of Industrial Economics, 48(3), 227-252.
19. Matzat, U., Utz, S., and Snijders, C. (2009), “On-line Reputation Systems: The Effects of Feedback Comments and Reactions on Building and Rebuilding Trust in On-line Auctions,” International Journal of Electronic Commerce, 13(3), 95-118.
20. Melnick, M. and Alm, J. (2002), “Does a Seller’s e-Commerce Reputation Matter? Evidence from eBay Auctions,” Journal of Industrial Economics, 50(3), 337-349.
21. Pedhazur, E. J., (1997), “Multiple regression in behavioral research”, Wadsworth 7th ed Publishing.
22. Rao, A. R. and Monroe, K.B. (1996), “Causes and Consequences of Price Premiums,”Journal of Business, 69(4), 511-545.
23. Sharma, S. (1996), “Applied Multivariate Techniques”, John Wiley.
24. Yu, C. and Lin, S. (2008), “Parallel Crawling and Capturing for On-Line Auction,” Proceedings of the IEEE ISI 2008 PAISI, PACCF, and SOCO international workshops on Intelligence and Security Informatics. Taipei, Taiwan: Springer-Verlag, 455-466.
【中文文獻】:
25. 呂金河,多變量分析,滄海書局(2005)
26. 黃俊英,多變量分析,中國經濟企業研究所(2000)。
27. 陳正昌、程炳林、陳新豐、劉子鍵,多變量分析方法-統計軟體應用四版,五南圖書出版公司(2007)。
28. 吳明隆、涂金堂,SPSS與統計應用分析,五南圖書出版公司(2009)。