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eBay presentation
eBay presentation
eBay presentation
eBay presentation
eBay presentation
eBay presentation
eBay presentation
eBay presentation
eBay presentation
eBay presentation
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eBay presentation

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  1. An Online Consumer-to-Consumer Trading Community Presentation is based on Melnik research: “ Does a Seller’s eCommerce Reputation Really Matter? Evidence from eBay Auctions” (with James Alm). Journal of Industrial Economics , September 2002. “ Reputation, Information Signals, and Willingness to Pay for Heterogeneous Goods in Online Auctions”, (with James Alm). Southern Economic Journal , October 2005.
  2. eBay: A True Success Story <ul><li>From a simple website in 1995 to being </li></ul><ul><li>synonymous with online auctions! </li></ul><ul><li>1.9 billion listings in 2005 </li></ul><ul><li>4.552 billion in revenues </li></ul><ul><li>71.8 million active users </li></ul><ul><li>96.2 million accounts listed with PayPal* </li></ul><ul><li>But what about the economics..... </li></ul>* All information is taken from QIV05 eBay Financial Results report
  3. Asymmetry of Information <ul><li>Akerlof, 1970 </li></ul><ul><li>Asymmetry of Information on eBay </li></ul><ul><ul><li>Buyer’s problem </li></ul></ul><ul><ul><ul><li>Uncertainty about delivery of the item (general compliance with the terms of transaction) </li></ul></ul></ul><ul><ul><ul><li>Uncertainty about the accuracy in the description of the item </li></ul></ul></ul><ul><ul><li>Seller’s problem </li></ul></ul><ul><ul><ul><li>Payment/return </li></ul></ul></ul><ul><li>Past Reputation as a Signal of Current and Future Behavior </li></ul><ul><ul><li>Theoretical support </li></ul></ul><ul><ul><ul><li>Klein and Leffler, 1981; Shapiro, 1983; Allen, 1984; Houser and Wooders, 2000 </li></ul></ul></ul><ul><ul><li>Experimental support </li></ul></ul><ul><ul><ul><li>Miller and Plott, 1985; DeJong, Forsythe, and Lundholm, 1985; Camerer and Weigelt, 1988; Holt and Sherman, 1990 </li></ul></ul></ul>
  4. Reputational Mechanism on eBay <ul><li>Structure of the mechanism </li></ul><ul><ul><ul><li>Quantitative </li></ul></ul></ul><ul><ul><ul><ul><li>Positive, negative, neutral rating choices only </li></ul></ul></ul></ul><ul><ul><ul><li>Difficult to manipulate through collusive behavior </li></ul></ul></ul><ul><ul><ul><ul><li>Rating left by unique registered eBay users </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Feedback score = unique positives – unique negatives </li></ul></ul></ul></ul><ul><ul><ul><li>Informative </li></ul></ul></ul><ul><ul><ul><ul><li>Overall eBay experience of the seller </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Past complain history </li></ul></ul></ul></ul><ul><li>Does the reputational measure help overcome asymmetries of information? </li></ul><ul><ul><li>Is it valued by members of the community? </li></ul></ul><ul><ul><li>Is it valued by competing communities? </li></ul></ul>SIMPLE * MEASURABLE * DIFFICULT TO MANIPULATE
  5. Choice of Data <ul><li>2002: Homogeneous good study </li></ul><ul><ul><li>US $5 1999 Gold Coin in Mint Condition </li></ul></ul><ul><ul><ul><li>Possibility of encountering a fraudulent seller </li></ul></ul></ul><ul><li>2005: Heterogeneous good study </li></ul><ul><ul><li>US Morgan Dollars in Almost Uncirculated Condition </li></ul></ul><ul><ul><ul><li>Accuracy in the description of item-specific characteristics </li></ul></ul></ul><ul><ul><ul><li>Possibility of encountering a fraudulent seller </li></ul></ul></ul>
  6. Modeling Reputation <ul><li>P = f (seller’s reputation, X) </li></ul><ul><ul><li>X – a set of auction specific variables </li></ul></ul><ul><ul><ul><li>Transaction costs (shipping, insurance) </li></ul></ul></ul><ul><ul><ul><li>Time exposure, closing (duration, closing time/date, day of the week) </li></ul></ul></ul><ul><ul><ul><li>Supply characteristics (number of available items) </li></ul></ul></ul><ul><ul><ul><li>Payment methods </li></ul></ul></ul>
  7. Empirical formulation <ul><li>Censored observations and the use of Tobit model </li></ul><ul><ul><li>Fixed price auctions and no-bid auctions </li></ul></ul><ul><li>105 price distributions: Huber-White estimation of </li></ul><ul><li>robust standard errors </li></ul>
  8. Estimation Results Mean prices: Certified: $327.50; Non-certified: $58.08 <ul><ul><li>Seller’s reputation impacts buyer’s willingness to pay </li></ul></ul><ul><ul><li>In heterogeneous goods: A reduction in available information increases the premium to positive reputation and the penalty to negative reputation. </li></ul></ul><ul><ul><li>Negative feedback effect increases with the value of the item </li></ul></ul><ul><ul><li>Substantial penalty is imposed on new sellers in non-certified coins auctions </li></ul></ul>
  9. Some Previous Findings <ul><li>- Lucking-Reiley et al. (1999): 1% increase in rating -> 0.03% increase in willingness to pay </li></ul><ul><li>- Houser and Wooders (2002): 10% increase in rating -> 0.17% increase in willingness to pay </li></ul><ul><li>- Melnik and Alm (2002): Doubling in rating -> 0.55% increase in willingness to pay </li></ul>
  10. Conclusions <ul><li>Non-transferable across communities reputational mechanism in online consumer to consumer communities acts as a club good </li></ul><ul><ul><li>Valued by buyers and sellers </li></ul></ul><ul><ul><li>Enables a community to overcome asymmetries of information problem </li></ul></ul><ul><ul><li>Establishes a barrier to entry for a competing community </li></ul></ul>

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