Abstract
This study examines the Name-Your-Own-Price (NYOP) retailer’s information revelation strategy when competing with list-price channel. We propose an integrated economic framework focusing on the comparison of expected consumer surplus from bidding at NYOP auction and guaranteed consumer surplus from buying at a list price. We then conduct an empirical study to examine the effects of seller-supplied price information on NYOP bidding outcome (especially on expected winning probability and the number of bidders). The results of our study strongly indicate the effects of seller-supplied information on expected winning probability (as well as the expected consumer surplus) in a NYOP auction. We also illustrate the strategic implications of seller-supplied price information via a revenue simulation for the NYOP seller. Our results suggest that NYOP seller may increase his expected revenue by (1) provide only the upper bound of its threshold price when list price is high (low expected consumer surplus from buying at list price); (2) provide only the lower bound of its threshold price when list price is low (high expected consumer surplus from buying at list price); (3) provide both the upper and lower bound of its threshold price when consumer surplus of buying at list price is unknown.
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Notes
The threshold price range is similar to Ding et al (2005)’s product cost, which is assumed to be uniformly distributed over a range.
Guaranteed Consumer Surplus is the difference between consumer’s perceived value and the list price of the item, or \( GCS = v - LP \).
The expected consumer surplus of her optimal bid is the product of winning probability of optimal bid and the difference between consumer’s perceived value and the optimal bid, or \( ECS = ({v_i} - {B_i})\Pr ({B_i} \geqslant P) \), where \( \Pr ({B_i} \geqslant P) \)is the expected winning probability of bid B i .
The bidding cost was estimated to be somewhere between EUR 3.54 and 6.08 in Hann and Terwiesch (2003). Since this paper follows the single-bid policy of Priceline.com, the effects of bidding cost for one bid on consumers’ surplus is assumed to be ignorable; especially for college students whose opportunity cost of bidding time is much lower than consumers with higher income or less spare time.
A similar concept of price range mapped to probability can be found from the buyer side when they are uncertainty about their own reservation price in Wang et al. (2007).
Perceived value is defined as perceived monetary value of a product/service and is equivalent to Willingness-to-pay in Simonson and Drolet (2004). Simonson and Drolet found Willingness-to-pay of consumer products were significantly influenced by arbitrary anchors.
We used stated value instead of induced value. Although non-incentive compatible stated value may contain hypothetical bias, such a bias exists for all bidders in our study. Moreover, the focus of this study is not on optimal pricing, which requires more accurate measures of willingness-to-pay. Our primal interest is to investigate the information revelation strategy of a NYOP seller by incorporating bidder’s expected winning probability. Induced value will likely to reduce some hypothetical bias but is unlikely to change our results.
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Wang, T., Hu, M.Y. & Hao, A.W. Name-Your-Own-Price seller’s information revelation strategy with the presence of list-price channel. Electron Markets 20, 119–129 (2010). https://doi.org/10.1007/s12525-010-0033-z
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DOI: https://doi.org/10.1007/s12525-010-0033-z