網路拍賣的三個議題

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：71

、訪客IP：18.118.37.85

姓名

林佳慶(Chia-Ching Lin) 查詢紙本館藏

畢業系所

產業經濟研究所

論文名稱

網路拍賣的三個議題
(Three Essays on Internet Auctions)

相關論文

★ 雙邊市場論文集：搜尋、配對與多重據點	★ 網路拍賣的實證研究
★ Three Essays on the Industrial Economics	★ Essays on Online Auction and Fee-shifting Rule in Litigation
★ 畸零定價對成交機率之影響與分析 ─以 YAHOO!相機拍賣為例	★ 雙邊市場在遊樂園及醫療市場的應用以及公營社會企業之最適民營化
★ 領導廠商之市場進入分析-以圖形處理器市場為例

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

[檢視]

[下載]

本電子論文使用權限為同意立即開放。
已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。
請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。

摘要(中)

eBay與Yahoo! Auction分別從1995年與1998年開始在美國的網路平台拍賣商品，在近十年來網路拍賣的交易金額與數量同時快速地成長。由於網路拍賣的交易便利，使得網路拍賣的交易模式以漸取代某些傳統的實體店面的交易。因此，我們有必要去瞭解網路拍賣它的拍賣機制與買家與賣家的拍賣行為。故本文研究將分別討論賣家的拍賣行為、拍賣的競標行為與訊息不對稱下的交易行為。
首先，我們討論eBay的賣家在選擇拍賣機制時所考量因素包括商品的訊息、賣家的訊息與拍賣的訊息，這些因素對於賣家選擇拍賣的機制是否有影響。同時我們也認為賣家的前一期的資訊也有可能這一次拍賣機制的選擇，前一次拍賣的資訊包括前一次拍賣交易成功所累積的次數、前一次交易價格的高低與前一次賣家使用拍賣機制的累積比率等因素。
本文第二部分則是在分析拍賣中出價的競爭程度。過去許多的文獻都只探討拍賣中的買家的出價的策略不受其他競爭拍賣的影響。因此，本文將探討買家出價是否競爭除了受到賣家資訊與商品資訊外，是否也仍受到其他競爭拍賣的特性所影響。研究資料同樣是採用eBay汽車的資料分析。結果發現其他競爭拍賣的立即購買拍賣數量與競標人數的多寡皆會影響出價競爭的程度；而其相同機制的競標拍賣(PA)數量與競標次數則對競爭程度沒有影響。賣家的正評價與總評價也會讓買家出價的次增加；沒有保固的汽車則會降低買家出價的次數；汽車公里數愈高，則買家也傾向於會提高出價次數。另外在買家競標汽車的出價時間，研究結果發現最後出價剩餘時間平均大約在3,000分鐘左右，顯示汽車拍賣不屬於高競爭的拍賣產品。我們也發現競標人數、競標次數、汽車保固都會影響買家愈晚出價，以提高買家的得標機率；商品的上架時間愈長，周圍的競爭拍賣為競拍拍賣或立即購買拍賣愈長，會使買家提早出價，以減少追蹤商品會有成本。本文結果認為買家出價時會考慮不同的資訊，作為其出價時間的策略考量因素，以提高買家的成交機率並降低交易成本。
最後一部分的研究是利用台灣的雅虎拍賣(Yahoo! Auction)拍賣的iPhone 4s探討訊息不對稱下的拍賣交易。訊息不對稱下讓買家產生不確定性，不確定來自於買家對賣家的不信任與產品品質的疑慮。在買家對賣家的不確定方面，可利用賣家的聲譽降低不確定性；在買家對產品的不確定性，則藉由賣家對產品訊息的揭露以降低產品的不確定性。賣家主動揭露照片的數量、照片品質與提供保固對會提高交易的機率，但被動揭露產品品質的資訊則是對交易有負面影響。
本文研究賣家行為、買家競標行為與拍賣的訊息不對稱的問題，主要是藉由實證的分析來瞭解網路平台的買賣交易行為，並冀望將這些研究的結果提供予網路平台的賣家、買家與平台經營者做出最適當的行為決策。

摘要(英)

This dissertation is mainly discussed about the seller’s and the buyer’s behavior in online auction marketplace, including the seller’s choice of auction formats, the buyer’s bidding strategies. I study the relationship between the seller’s auction-format choice and the characteristics of the product, the seller’s information, and auction information. Furthermore, I also investigate the effects of product uncertainty for the transaction if iPhone 4s.
The first part empirically studies the auction behavior of the sellers. Firstly, we will discuss what kind of information the sellers on eBay would consider before the auction mechanism such as product information, sellers’ information, and the auction information and learn if these said factors would have any impacts on the sellers’ auction mechanism. In the meantime, we also consider how the sellers’ previous information might influence the current selection of auction formats. Previous information includes the accumulative experiences of the successful rate, the last transaction price and the accumulative rate of the sellers’ auction mechanism, etc.
As to the second part, I analyze how competitive the bidding is in online auction. Several articles in the past have discussed that the buyers’ bidding strategy would not be influenced by the competition of other auctions. Thus, this article will not only focus on the impact of the competition of other auctions on the buyers’ bidding strategy but also further discuss the impact of the characteristics of other auctions on the buyers’ bidding strategy. This data is also derived from the data of motor auctions on eBay. We discovered the number of Buy-it-Now auctions and bidders from other competitive auctions have an effect on the number and time of buyers’ bidding whereas the number of Pure Auction auctions and its bidders do not have the same effect as Buy-it-Now Auctions on the bidding competition. Seller’s feedback and their scores would also encourage the number of bidding from buyers. Meanwhile, we also learn that the vehicles without warranty will decrease the number of bidding while the high mileage of the vehicles would also escalate the number of bidding. Furthermore, regarding bidding time, we discover that the average of the remaining time before ending of the auction is approximately 3000 minutes, which also implies vehicle auction is not a highly competitive product on eBay auctions. Besides, the number of bidders and number of bidding as well as vehicle warranty would also increase the chance of late bidding by the buyers in order to win the product. However, the longer launching duration of the product, which also means the longer duration of competition with Pure Auctions and Buy-it-Now Auctions from other auctions, would also force the buyers to bid early in order to decrease the cost of tracing products. The result of the study suggests that the buyers would consider every possible aspect of the auctions before they make a decision of bidding in order to increase the rate of closing the deal as well as lower the cost of the transaction.
The last part investigates asymmetric information of the transaction between the buyers and sellers in online auctions to further discuss the transaction of iPhone 4s on Taiwan Yahoo! Auction in the fourth chapter. The information asymmetry of the auction would increase the sense of uncertainty of the buyers, which deep down is caused by the distrust in the sellers and the products’ quality. The buyers’ distrust in sellers might also be eased by the seller’s credibility of the transaction, and the revealing of the product information might also minimize the buyers’ distrust in the product quality. For the seller’s transaction, a warranty, the quantity of product photos, and quality of product photos are important factors. However, this information may lead to a fruitless result if product quality revealed is not up to standards.
This article is mainly addressing the relation between the sellers’ auction behavior, the buyers’ bidding decision and the information asymmetry of the auctions. We also would like to find out the result to the buyers and the sellers on the internet platforms via our verification and analysis of the study in order further to provide them a solution to making the most appropriate and precise strategies during the auctions.

關鍵字(中)

★ 網路拍賣
★ 出價競爭
★ 資訊不對稱

關鍵字(英)

★ Internet Auction
★ Bidding Competition
★ Asymmetric Information

論文目次

Chapter 1. Introduction of the Dissertation 1
Chapter 2. Selling Behavior in Dynamic Online Auction: Evidence from eBay Motors 3
2.1 Introduction 3
2.2 Literature Review 7
2.3 The Data and Descriptive Statistics 10
2.4 Empirical Model 16
2.4.1 Selection Model of Auction Formats 16
2.4.2 Change of Models on Auction Format 18
2.5 Results 20
2.6 Conclusion 28
2.7 Reference 30
Chapter 3. The Analysis of the Buyer’s Strategies on Internet Auction Marketplace – Empirical Study of eBay Motors 33
3.1 Introduction 33
3.2 Literature review 38
3.3 The Data and Descriptive Statistics 40
3.4 Empirical Model 46
3.4.1 Competition Model 46
3.4.2 Bidding Model for Time 47
3.5 Result 50
3.5.1 The factors which impact the competition of the auction 50
3.5.1 Time of bidding 53
3.6 Conclusion 59
3.7 Reference 61
Chapter 4: Information Asymmetry in Online Auction – Evidence from iPhone 4s in Taiwan Yahoo! Auctions 63
4.1 Introduction 63
4.2 Literature review 66
4.3 The Data and Descriptive Statistics 68
4.4 Empirical models 74
4.4.1 Transaction models 74
4.4.2 Empirical Analysis 74
4.4.3 The Instrumental Variables and Endogenous Test 75
4.5 Result 77
4.6 Conclusion 82
4.7 Reference 84

參考文獻

1. Andrews, T., and Benzing, C. (2007), “The Determinants of Price in Internet Auctions of Used Cars,” Atlantic Economic Journal, 35, 43-57
2. Bajari, P., & Hortaçsu, A. (2004), “Economic Insights from Internet Auctions,” Journal of Economic Literature, 42, 457-486.
3. Bauner, Christoph (2015), “Mechanism Choice and the Buy-it-now Auction: A Structural Model of Competing Buyers and Sellers,” International Journal of Industrial Organization, 38, 19-13
4. Bose, S. and A. Daripa (2009), “Optimal Sale across Venues and Auctions with a Buy-it-Now Option,” Economic Theory, 38, 137-168
5. Budish, E. B. and L. N. Takeyama (2001), “Buy Price in Online Auctions: Irrationality on the Internet?” Economics Letters, 72, 325-33.
6. Che, Xiao-Gang (2010) “Internet Auctions with a Temporary Buyout Option,” working paper, the University of Sydney.
7. Chen, J. R., K. P. Chen, C. F. Chou and C. I. Huang (2013), “A Dynamic Model of Auctions with Buy-it-Now: Theory and Evidence,” Journal of Industrial Economics, 61(2), 393-429.
8. K. P. Chen, Y. S. Liu and Y. T. Yu (2013), “The Seller’s Listing Strategy in Online Auctions: Evidence from eBay”, mimeo.
9. Etzion, H., E. Pinker and A. Seidmann (2006), “Analyzing the Simultaneous Use of Auctions and Posted Prices for On-line Selling,” Manufacturing and Service Operations Management, 8, 68-91.
10. Greene, William H., “Econometric Analysis,” 7th,
11. Hammond, R.G. (2010), “Comparing Revenue from Auctions and Posted Price,” International Journal of Industrial Organization, 28, 1-9.
12. Hasker and Sickles (2010), “eBay in the Economic Literature: Analysis of an Auction Marketplace,” Review of Industrial Organization, 37, 3-42
13. Huang, C. I, Chen, J. R. and Lee, C, Y. (2013), “Buyer Behavior under the Best Offer Mechanism: A Theoretical Model and Empirical Evidence from eBay Motors,” Journal of Economic Behavior and Organization, 94, 11-33.
14. Jafar Olimov (2013), “Auctions as Appraisal Mechanisms: Seller Behavior in eBay Auctions,” working paper, Ohio State University.
15. Klaus Kultti (1999), “Equivalence of Auctions and Posted Prices,” Games and Economic Behavior, 27(1), 106-113
16. Ku, Gillian, Adam D. Galinsky, and J. Keith Murnighan (2006) “Starting Low but Ending High: A Reversal of the Anchoring Effect in Auctions”, Journal of Personality and Social Psychology,” 90(6), 975-986.
17. Mathews, T. (2004), “The Impact of Discounting on an Auction with a Buy-out Option: a Theoretical Analysis Motivated by eBay’s Buy-It-Now Feature,” Journal of Economics, 81, 25-52.
18. Mathews, T., and B. Katzman (2006) “The Role of Varying Risk Attitudes in an Auction with a Buyout Option,” Economic Theory, 27, 597–613.
19. McAfee, R. P. (1993), “Mechanism Design by Competing Sellers,” Econometrica, 61, 1281-1312.
20. Milgrom, P. R., and Weber, R. J. (1982), “A Theory of Auctions and Competitive Bidding” Econometrica, 50(5), 1089-1122.
21. Myerson, R. B. (1981), “Optimal Auction Design,” Mathematics of Operations Research, 6, 58-73.
22. Ockenfels, A. D. Reiley, and A. Sadrieh (2007), “Online Auctions”, Economics and Information System, Volume 1.
23. Riley, J. G. and Zeckhauser, R., (1983) “Optimal Selling Strategies: When to Haggle When to Hold Firm” The Quarterly Journal of Economics, 98, 267-289.
24. Simonsohn, Uri, and Dan Ariely (2008) “When Rational Sellers Face Non-Rational Consumers: Evidence from Herding on eBay,” Management Science, 54(9), 1624-1637
25. Wang, R. (1993) “Auction versus Posted-price Selling,” The American Economic Review, 83, 838-851.
26. Zeithammer, R. and Liu, P. (2006) “When Is Auctioning Preferred to Posting a Fixed Selling Price?” working paper, University of Chicago.
1. Anwar, S., McMillan, R., and Zheng, M. (2006), “Bidding Behavior in Competing Auctions: Evidence from eBay”, European Economic Reviews, 50,307-322
2. Bajari, P. and Hortacsu, A. (2004), “Economic Insights from Internet Auctions,” Journal of Economic Literature, 42, 457-486.
3. Bapna, R., Chang, S. A., and Goes, P. (2009), “Overlapping Online Auctions: Empirical Characterization of Bidder Strategies and Auction Prices” MIS Quarterly, 33(4), 763-783.
4. Bapna, R., Goes, P., and Gupta, A. (2003), “Replicating Online Yankee Auctions to Analyze Auctioneers’ and Bidders’ Strategies,” Information Systems Research, 14(3), 244-268
5. Engelbrecht-Wiggans, R., and Menezes, F., (2001), “Why Do Bidders Drop Out from a Sequential Auction”, Revista Brasileira de Economia, 55(1), 33-51
6. Goes, P. B., Karuga, G. G, and Tripathi, A. K. (2012), “Bidding Behavior Evolution in Sequential Auctions: Characterization and Analysis,” MIS Quarterly, 36(4), 1021-1042
7. Hasker, K. and Sickles,, R. (2010), “eBay in the Economic Literature: Analysis of an Auction Marketplace”, Review of Industrial Organization, 37, 3-42.
8. Jeitschko, T. D. (1998), “Learning in Sequential Auctions,” Southern Economic Journal, 65(1), 98-112
9. Mathews T., and Katzman, B. (2006), “The Role of Varying Risk Attitudes in an Auction with a Boyout option,” Economic Theory, 27, 597-613
10. McAfee, R. P. (1993), “Mechanism Design by Competing Sellers,” Econometrica, 61, 1281-1312
11. Menezes, F. M., and Monteiro, P. K.(1997), “Sequential Asymetric Auctions with Endogenous Participation”, Theory and Decision, 43(2), 187-202
12. Nekipelov, D. (2007), “Entry Deterrence and Learning Prevention on eBay. Dissertation, Duke University.
13. Ockenfels, A., Roth, A. E., (2004), “Late and Multiple Bidding in Second price Internet Auctions: Theory and Evidence Concerning Different Rules for Ending an Auction,” Games and Economic Behavior, 55, 297-320
14. Peters, M and Severinov, S. (2006), “Internet Auctions with Many Traders,” Journal of Economic Theory, 130, 220-245.
15. Reynolds, S. S., and Wooders, J. (2009), “Auctions with a Buy Price”, Economic Theory, 38(1), 9-39
16. Rothkopf, M. H., and Hastad, R. M. (1994), “Modeling Competitive Bidding: A Critical Essay,” Management Science, 40(3), 364-384
17. Roth, A. E., and Ockenfels, A. (2002), “Last minute bidding and the rules for ending second-price auctions: Evidence from eBay and Amazon auctions on the internet,” American Economic Reviews, 92(4), 1093-1103
18. Peters, M. and Severinov, S. (2006), “Internet Auctions with Many Traders”, Journal of Economic Theory, 130, 220-245.
19. Wilcox, R. T. (2000), “Experts and Amateurs: The Role of Experience in Internet Auctions,” Marketing Letters, 11(4), 363-374
20. Wooldridge, J. (2010), “Econometric Analysis of Cross Section and Panel Data”
21. Zeithammer, R. (2006), “Forward-Looking Bidding in Online Auctions,” Journal of Marketing Research”, 43(3), 462-476
22. Zhao, J., and Kling, C. L. (2004), “Willingness to Pay, Compensation Variation and the Cost of Commitment,” Economic Inquiry, 22(3), 503-517
1. Akelof, George A. (1970), “The Market for Lemons: Quality Uncertainty and the Market Mechanism,” Quarterly Journal of Economics, 84, 488-500.
2. Andrews, Thomas and Benzing, Cynthia (2007), “The Determinants of Price in Internet Auction of Used Cars”, Atlantic Economic Journal, 35, 43-57
3. Arrow, Kenneth J. (1963), “Uncertainty and the Welfare Economics of Medical Care” The American Economic Review, 5, 941-973.
4. Ba, Sulin and Paul A. Pavlou (2002), “Evidence of the Effect of Trust Building Technology in Electronic Markets: Price Premiums and Buyer Behavior,” MIS Quarterly, 26, 243268
5. Ching-I Huang and Min-Jie Su (2011), “The Effect of Information Disclosure on Product Demand: Evidence from Yahoo! Auctions Taiwan,” Taiwan Economic Review, 39(3), 299-324
6. Dewally, Michael and Ederington, Lious (2006), “Reputation, Certification, Warranties, and Information as remedies for seller-buyer information asymmetries: Lessons from the online comic book market,” Journal of Business, 79, 693-729
7. Ghose, Anindya (2009), “Internet Exchanges for Used Goods: An Empirical Analysis of Trade Patterns and Adverse Selection” MIS Quarterly, 33(2), 263-291
8. Ghose, A., P. Iperiotis, and A. Sundararajan (2010), “The Dimensions or Reputation in Electronic Markets”, Working paper.
9. Houser, Danel and John Wooders (2006), “Reputation in Auctions: Theory and Evidence from eBay,” Journal of Economics and Management Strategy, 15, 353-369
10. Jin, Ginger Zhe and Kato, Andrew (2006), “Price, Quality, and Reputation: Evidence from an Online Field Experiment”, RAND Journal of Economics, 37, 983-1004
11. Lucking Reiley, David (2000), “Auctions on the Internet: What’s Being Auctioned, and How?” Journal of Industrial Economics, 48, 227-252
12. Melink, Mikhail I. and James Alm (2002), “Does a Seller’s E-Commerce Reputation Matter? Evidence from eBay Auctions,” Journal of Industrial Economics, 50, 337-349
13. Melink, Mikhail I. and James Alm (2005), “Seller Reputation Signals, and Price for Heterogeneous Coins on eBay”, Southern Economic Journal, 72(2), 305-328
14. Milgrom, Paul R. and Robert J. Weber (1982), “A Theory of Auctions and Competitive Bidding”, Econometrica, 50(5), 1089-1122

指導教授

陳恭平(Kong-Pin Chen)

審核日期

2016-7-22

推文