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title page
Abstract
Contents
Chapter 1. Introduction 13
1.1. Background and Motivation 13
1.2. Purpose and Scope of the Study 16
Chapter 2. Overview of Online Auctions and the Marketplaces 21
2.1. History of Online Auction Marketplaces 21
2.1.1. Worldwide 21
2.1.2. Korea 28
2.2. Characteristics of C2C Online Auctions 31
2.2.1. Expanded Realm of Auctions 31
2.2.2. Auction Mechanisms 36
Chapter 3. Effect of the Number of Bidders: When Part of Bidders find Posted-price Goods Identical to Auctioned Items 42
3.1. Literature Review 42
3.2. The Model 44
3.2.1. Model Setting 44
3.2.2. Equilibrium Bidding Strategy 46
3.3. Revenue of Online Auction Sellers 48
3.4. Actual Auction Data 54
3.5. Discussion 61
3.6. Appendix 64
Chapter 4. Benefits of Buy-price Options to Sellers: When All the Bidders Find Posted-price Goods Identical to Auctioned Goods 67
4.1. Introduction to Buy-price Options 67
4.2. Literature Review 68
4.3. Bidders' Strategies 70
4.3.1. The Model 70
4.3.2. No Buy-Price Options 72
4.3.3. Permanent Buy-Price Options 75
4.4. Auction Outcomes under Buy-Price Options 82
4.5. Empirical Analysis Using Actual auction Data 88
4.5.1. Empirical Models and Online Auction Data 88
4.5.2. Results 93
4.6. Discussion 96
4.7. Appendices 99
Chapter 5. Conclusion 106
요약문 111
Reference 115
감사의 글 125
이력서 127
Table 2.1. eBay's key financial and operating metrics (third quarter of 2004 and 2005) 26
Table 3.1. List of data sets 57
Table 3.2. Number of auctions with a duration of 7 days or 4 days or less 59
Table 3.3. Average final prices and p-values of 7-day auctions or 4-days-or-less auctions 60
Table 3.4. Data sets where H0 is rejected in favor of Ha 61
Table 4.1. Auction Outcomes by Model and by Simulations (pв＝pm) 85
Table 4.2. Revenue and proportion of actual bidders versus buy-prices close to pL(이미지 참조) 86
Table 4.3. Percentage of Exercised Buy-Price Options in Simulation with pв＝pL(이미지 참조) 89
Table 4.4. Definition of the variables used in the empirical models 92
Table 4.5. Descriptive statistics of independent variables 93
Table 4.6. Regression estimation results: (a) In(Revenue), (b) Duration 94
Table 4.7. Regression estimation results (gift certificate auctions) 104
Figure 2.1. www.ebay.com (front page) 23
Figure 2.2. Total revenue and net profit of eBay (1997~2004) 25
Figure 2.3. The number of total registered users on eBay platforms (1997~2004) 25
Figure 2.4. www.auction.co.kr(front page) 28
Figure 2.5. Composition of Korean electronic commerce markets (value of sales) (2004) 30
Figure 2.6. Proportions of "Collectibles" and "Practicals" in Gross Mechandise Volume of eBay 35
Figure 3.1. Examples of the distribution of bidders' values with different multipliers 52
Figure 3.2. Expected revenue with respect to different bidder-traffic multipliers (n＝10, l＝6) 53
Figure 3.3. Categorization of data sets into three groups 56
Figure 3.4. Expected revenue with and without promotional options (n＝10, l＝6) 63
Figure 4.1. Expected utility without a buy-price option 75
Figure 4.2. Expected utility with a permanent buy-price option 81
Figure 4.3. (a) Revenue versus buy-price with pL＝0.7 (CARA utility function)(b) Number of actual bidders versus buy-price with pL＝0.7 (CARA utility function)(이미지 참조) 84
Figure 4.4. Buy-Price Option Prices versus Lowest Retail Prices of Samsung 256M DDR RAM(266MHz)in2004 95
Figure 4.5. Revenue from single-unit gift certificate auctions and the market interest rate 105
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