全球最大的網路交易平台之一，eBay，於2005年推出一個讓買方可與賣方進行議價的新交易方式”Best Offer”。此機制特色在於買方能以低於立即購買價(“Buy It Now” price，直購價)的價格向賣方進行出價(make an offer)，賣方接收到買方之出價後可接受此價格或進行反出價(counter offer)。此種交易方式有別於傳統拍賣僅由買方單向出價，且交易過程所揭露的訊息亦有差異。雖然文獻上探討買方參與網路拍賣行為的研究很多，但探究eBay的議價機制下影響消費者行為因素之研究卻相對稀少。本研究收集eBay Motors平台上採用Best Offer交易方式的Toyota汽車為樣本，以研究在此議價機制下的消費者行為。
第一篇研究是探討Best Offer機制下所揭露的訊息對買方出價的影響。相較傳統拍賣方式，買方在Best Offer交易方式下，其競價過程中無法觀察到競爭對手的出價，且若各買方的出價無法獲得賣方認同，則刊載時間結束後商品將流標。諸如種種的差異，使得買方勢必在有限的訊息下提出其最適出價以獲得賣方接受，本研究採用一般動差估計法(GMM)估計買方的出價如何受到Best Offer機制下所揭露的訊息所影響，並同時考量賣方設定直購價的內生性問題。實證結果顯示，買方雖然無法觀察競爭者的出價，但其出價仍會受到競爭者人數影響，會隨著先前已出價的競價者人數、以及賣方訂定的直購價增加而提高出價。反之，買方出價會隨著商品刊載時間經過而下降。
第二篇研究是探討資訊不對稱下如何影響買方的出價策略。我們主張出價與出價次數間具有抵換關係(trade-off)，買方的出價策略為「一價到位」或「逐次增價」。本研究採用兩階段最小平方法(2SLS)估計資訊不對稱對買方的出價與出價次數之影響。實證結果顯示，來自產品與賣方的不確定性，對買方出價有負向顯著影響、但對出價次數的影響則是正向顯著，換言之，買方會傾向採用「逐次增價」策略。但若能透過資訊揭露降低不確定性，買方將會採用「一價到位」的策略。另外，本文亦同時加入買方經驗特徵變數共同探究，估計結果顯示，具經驗的買方會傾向採用「逐次增價」策略。

The popular Internet marketplace, eBay, introduced the Best Offer mechanism in 2005. This feature enables a buyer to negotiate with the seller for a price lower than the listed “Buy-It-Now” price. Since difference in disclosed information between auction and the Best Offer mechanism gives us the motivation to examine buyer behavior under such a mechanism, in this example, using data on the sale of Toyota cars on eBay Motors.
The first essay investigates how the information disclosed under the Best Offer mechanism affects the buyer’s offer price. To indicate that the Buy-it-Now (BIN) price chosen by the seller may be correlated with unobserved characteristics in the listing, we construct instrumental variables for the BIN price in order to solve the potential endogeneity problem and use the Generalized Method of Moments (GMM) for parameter estimation. The empirical results indicate that a rational buyer’s offer price increases in relation to the number of buyers who have previously made an offer on the item and the BIN price chosen by the seller. On the other hand, the offer price decreases for items which have been listed on eBay for a longer period of time.
The second essay investigates how asymmetric information affects the buyer’s offer strategy whether to make their highest offer and then leave or to make a lower offer and then increase it. Two-Stage Least Squares (2SLS) is used to estimate the influence of asymmetric information on a buyer’s first offer. The empirical results support the idea that product uncertainty and seller uncertainty have a negative impact on the first offer price and a positive impact on the number of offers. In addition, the number of offers increase with the buyer’s experience, but the effect of the offer price on buyer experience is not consistent. In general, buyers make a lower offer which is then increased under asymmetric information.

Chinese Abstract-i
English Abstract-iii
Table of Contents-iv
List of Tables-v

CHAPTER 1 INTRODUCTION OF THE DISSERTATION-1
1.1 Background-1
1.2 Main Data Source for Investigation of Buyer Behavior-4

CHAPTER 2 MAKING AN OFFER-7
2.1 Introduction-7
2.2 Literature Review-9
2.3 The Best Offer Mechanism on eBay-11
2.3.1 The selling formats-11
2.3.2 Stylized facts about Best Offer listings-12
2.4 Data Description-14
2.5 Empirical Model-17
2.6 Empirical Results-19
2.7 Conclusion-22
References-31

CHAPTER 3 ASYMMETRIC INFORMATION IN A BUYER’S OFFERING STRATEGY-33
3.1 Introduction-33
3.2 Prior Research and Hypotheses-36
3.3 Data Description-38
3.4 Empirical Model-41
3.5 Empirical Results-42
3.6 Conclusion and Discussion-45
References-53
 

[1] Amaldoss, W., Jain, S., (2008). Joint Bidding in the Name-Your-Own-Price Channel: A Strategic Analysis. Management Science, 54, 1685-1699.
[2] Ausubel, L.M., Cramton, P., Deneckere, R.J., (2002). Bargaining with Incomplete Information. In: Aumann, R., Hart, S. (Eds.), In: Handbook of Game Theory with Economic Applications, vol. 3. Elsevier, Chapter 50.
[3] Chen, C.-H., (2012). Name Your Own Price at Priceline.Com: Strategic Bidding and Lockout Periods. Review of Economic Studies, 79, 1341-1369.
[4] Chen, J.-R., Chen, K.-P., Chou, C.-F., Huang, C.-I., (2013). A Dynamic Model of Auctions with Buy-It-Now: Theory and Evidence. Journal of Industrial Economics, 61, 393-429.
[5] Chernev, A., (2003). Reverse Pricing and Online Price Elicitation Strategies in Consumer Choice, Journal of Consumer Psychology, 13(1-2), 51-62.
[6] Ding, M., Eliashberg, J., Huber J., and Saini, R., (2005). Emotional Bidders: An Analytical and Experimental Examination of Consumers’ Behavior in Priceline-Like Reverse Auctions, Management Science, 51(3), 352-364.
[7] Fay, S., (2004). Partial Repeat Bidding in the Name-Your-Own-Price Channel, Marketing Science, 23(3), 407-418.
[8] Fay, S., (2009). Competitive Reasons for the Name-Your-Own-Price Channel. Marketing Letters, 20, 277-293.
[9] Fay, S., Laran, J., (2009). Implications of Expected Changes in The Seller’s Price in Name-Your-Own-Price Auctions. Management Science, 55, 1783–1796.
[10] Hahn, J., and Hausman, J., (2003). Weak Instruments: Diagnosis and Cures in Empirical Econometrics, American Economic Review Papers and Proceedings, 93: 118-125.
[11] Hann, I. H. and Terwiesch, C., (2003). Measuring the Frictional Cost of Online Transactions: The Case of a Name-Your-Own-Price Channel, Management Science, 49:11, 1563-1579.
[12] Hansen, L. P., (1982). Large Sample Properties of Generalized Method of Moments Estimators, Econometrica, 50: 1029-1054.
[13] Hinz, O., and Spann, M., (2008). The Impact of Information Diffusion on Bidding Behavior in Secret Reserve Price Auction. Information Systems Research 19, 351-368.
[14] Hinz, O., and Spann, M., (2010). Managing Information Diffusion in Name-Your-Own-Price Auctions. Decision Support Systems, 49, 474-485.
[15] Houser, D. and Wooders, J., (2006). Reputation in Auctions: Theory, and Evidence from eBay, Journal of Economics and Management Strategy, 15, 353-69.
[16] 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 & Organization, 94, 11-33.
[17] Kuo, C.-W., Ahn, H.-S., and Aydın, G., (2011). Dynamic Pricing of Limited Inventories When Consumers Negotiate. Operations Research, 59, 882–897.
[18] Lewis, G., (2009). Asymmetric Information, Adverse Selection and Online Disclosure: The Case of eBay Motors, Working paper, Harvard University.
[19] Livingston, J. A., (2005), How Valuable is A Good Reputation? A Sample Selection Model of Internet Auctions, Review of Economics and Statistics, 87, 453-65.
[20] Melnik, M. I. and Alm, J., (2002). Does a Seller's Ecommerce Reputation Matter? Evidence from eBay Auctions, Journal of Industrial Economics, 50, 337-49.
[21] Ockenfels, A., Reiley, D., and Sadrieh, A., (2006). Online Auctions, in Handbook of Economics and Information Systems, Vol. 1 (Ed.) Terrence J. Hendershott, Elsevier Science, Amsterdam, 571-628.
[22] Spann, M. and Tellis, G.J., (2006). Does the Internet Promote Better Consumer Decision? The Case of Name-Your-Own-Price Auctions. Journal of Marketing 70, 65-78.
[23] Spann, M., Zeithammer, R., and Haubl, G., (2010). Optimal Reverse-Pricing Mechanisms. Marketing Science, 29, 1058–1070.
[24] Terwiesch, C., Savin, S., and Hann, I.-H., (2005). Online Haggling at A Name-Your-Own-Price Retailer: Theory and Application. Management Science, 51, 339-351.
[25] Toklu, K.Y., (2012). A Structural Model for “Best Offer” Sales on eBay, mimeo. Rice University.
[26] Wang, T., Gal-Or, and E., Chatterjee, R., (2009). The Name-Your-Own-Price Channel in the Travel Industry: an Analytical Exploration. Management Science, 55, 968-979.
[27] Alba, J., Lynch, J., Weitz, B., Janishewski, C., Lutz, R., Sawyer, A., and Wood, S., (1997). Interactive Home Shopping: Consumer, 35 Retailer, and Manufacturer Incentives to Participate in Electronic Marketplaces, Journal of Marketing, 61(3), 38-53.
[28] Andrews, T., and Benzing, C., (2007). The Determinants of Price in Internet Auctions for Used Cars, Atlantic Economic Journal, 35, 43-57.
[29] Ariely, D., and Simonson, I., (2003). Buying, Bidding, Playing or Competing? Value Assessment and Decision Dynamics in Online Auctions, Journal of Consumer Psychology, 13(1), 113-123.
[30] 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.
[31] Bagwell, K., and Riordan, M., (1991). High and Declining Prices Signal Product Quality, American Economic Review, 81, 224-239.
[32] Bajari, A., and Hortaçsu, A., (2003). Winner’s Curse, Reserve Prices and Endogenous Entry: Empirical Insights from eBay Auctions, Rand Journal of Economics, 34(2), 329-355.
[33] Bapna, R., Jank, W., and Shmueli, G., (2008). Consumer Surplus in Online Auctions, Information Systems Research, 19(4), 400-416.
[34] Bettman, J. R., Johnson, E. J., and Payne, J. W., (1991). Consumer Decision Making, in Handbook of Consumer Behavior, T. S. Robertson and H. H. Kassarjian (eds.), Englewood Cliffs, NJ: Prentice Hall, 50-79.
[35] Dimoka, A., Hong, Y. and Pavlou, P. A., (2012). On Product Uncertainty in Online Markets: Theory and Evidence. MIS Quarterly, 36(2), 395-426.
[36] Einav, L., Farronato, C., Levin, J. D., and Sundaresan, N., (2013). Sales Mechanisms in Online Markets: What Happened to Internet Auctions? NBER Working Paper.
[37] Haubl, G., and Trifts, V., (2000). Consumer Decision Making in Online Shopping Environments: The Effects of Interactive Decision Aids, Marketing Science, 19(1), 4-21.
[38] Jindal, P., Newberry, P., (2014). Price Negotiations and Bargaining Costs. NBER Working Paper.
[39] Kim, Y., (2005). The Effects of Buyer and Product Traits with Seller Reputation on Price Premiums in e-Auction, Journal of Computer Information Systems 46(1), 79-91.
[40] Lewis, G., (2011). Asymmetric Information, Adverse Selection and Online Disclosure: The Case of eBay Motors, The American Economic Review, 101(4), 1535-1546.
[41] Lucking-Reiley, D., Bryan, D., Prasad, N., and Reeves, D., (2007). Pennies from eBay: The Determinants of Price in Online Auctions, Journal of Industrial Economics, 55(2), 223-233.
[42] Milgrom, P., and Roberts, J., (1986). Price and Advertising Signals of Product Quality, Journal of Political Economy, 94(4), 796-821.
[43] Park, Y-H., and Bradlow, E. T., (2005). An Integrated Model for Bidding Behavior in Internet Auctions: Whether, Who, When, and How Much, Journal of Marketing Research, 37(4), 470-482.
[44] Pavlou, P. A., and Gefen, D., (2004). Building Effective Online Marketplaces with Institution-Based Trust, Information Systems Research, 15(1), 37-59.
[45] Payne, J. W., (1982). Contingent Decision Behavior, Psychological Bulletin, 92(2), 382-402.
[46] Shapiro, C., (1982). Consumer Information, Product Quality, and Seller Reputation, Bell Journal of Economics, 13(1), 20-35.
[47] Wang, X., Montgomery A. L., and Srinivasan K., (2004). When Auction Meets Fixed Price: A Theoretical and Empirical Examination of Buy-It-Now Auctions, Quantitative Marketing and Economics, 6(4), 339-370.
[48] Wilcox, R. T., (2000). Experts and Amateurs: The Role of Experience in Internet Auctions, Marketing Letters, 11(4), 363-374.
[49] Wolf, J., and Muhanna, W., (2005). Adverse Selection and Reputation Systems in Online Auctions: Evidence from eBay Motors, Proceedings of the 26th International Conference on Information Systems, Las Vegas, NV, December, 11-14.
[50] Wolf, J., and Muhanna, W., (2005). Adverse Selection and Reputation Systems in Online Auctions: Evidence from eBay Motors, in Proceedings of the 26th International Conference on Information Systems, D. Avison, D. Galletta, and J. I. DeGross (eds.), Las Vegas, NV, December 11-14, 847-858.
[51] Zeithammer, R., (2006). Forward Looking Bidding in Online Auctions, Journal of Marketing Research, 43(3), 462-476.