Willingness to Pay with Competition in Online Auctions

We model how to measure consumer willingness to pay (WTP) from an English or ascending first-price auction based on two general bidding premises: no bidder bids more than her WTP, and no bidder allows a rival bidder to win at a price that she is willing to beat. We thus impose no structural assumptions on rationality of bidders in a competitive auction context. Our approach is also based on the notion that auction market environment influences WTP. For instance, WTP is likely to be influenced by bidder experience, seller reputation, and competition among bidders and among items. We thus model consumer WTP as having a pure product-feature component and a component based on auction market environment. The proposed model is general enough to include " buy it now " (BIN) auction mechanism, and to provide controls for competition among bidders including those who do not participate in an auction and competition among various items up for sale concurrently. We use data of notebook auctions from one of the largest Internet auction sites in Korea. We find that most product characteristics matter in the expected ways. Our other primary findings are as follows: (1) The magnitude of the estimate of the number of months the laptop has been used indicates a lack of lemons problem; (2) Compared to neutral seller reputation, positive seller reputation matters more for WTP than negative seller reputation; (3) More extensive site-surfing and bidding histories lead to lower WTP, implying that search costs and experience matter in bidding; (4) WTP declines as more similar items are concurrently listed with the focal item; there is an additional effect if these similar items also belong to the same brand. Therefore, market thickness matters for consumer WTP. We also find that the highest bid is very close to the estimated second-highest WTP (the former is 0.7% higher), implying that the winner forms an accurate forecast of the second-highest WTP and therefore appears to be a savvy economic agent. As specific substantive benefits, we demonstrate how sellers can calculate changes in WTP, and hence the expected revenue as the auction setting varies. For items with BIN option, we show that how the probability of the option being exercised and the expected revenue vary with the level of BIN prices and market thickness.