Reducing buyer search costs: implications for electronic marketplaces
Management Science - Special issue: Frontier research on information systems and economics
Information rules: a strategic guide to the network economy
Information rules: a strategic guide to the network economy
Antecedents of B2C Channel Satisfaction and Preference: Validating e-Commerce Metrics
Information Systems Research
When snipers become predators: can mechanism design save online auctions?
Communications of the ACM - Mobile computing opportunities and challenges
Frictionless Commerce? A Comparison of Internet and Conventional Retailers
Management Science
Analysis and Design of Business-to-Consumer Online Auctions
Management Science
Partial-Repeat-Bidding in the Name-Your-Own-Price Channel
Marketing Science
Toward Comprehensive Real-Time Bidder Support in Iterative Combinatorial Auctions
Information Systems Research
Online Haggling at a Name-Your-Own-Price Retailer: Theory and Application
Management Science
Conditioning Prices on Purchase History
Marketing Science
Beyond the Hype of Frictionless Markets: Evidence of Heterogeneity in Price Rigidity on the Internet
Journal of Management Information Systems
Market Segmentation Within Consolidated E-Markets: A Generalized Combinatorial Auction Approach
Journal of Management Information Systems
Journal of Management Information Systems
Journal of Management Information Systems
Measuring Risk Aversion in a Name-Your-Own-Price Channel
Decision Analysis
Trust and TAM in online shopping: an integrated model
MIS Quarterly
Measuring Risk Aversion in a Name-Your-Own-Price Channel
Decision Analysis
Managing information diffusion in Name-Your-Own-Price auctions
Decision Support Systems
How Iranian SMEs managers' perceive the electronic pricing process?
International Journal of Electronic Finance
Information Systems Research
Retailers' Use of Shipping Cost Strategies: Free Shipping or Partitioned Prices?
International Journal of Electronic Commerce
Efficient Risk Hedging by Dynamic Forward Pricing: A Study in Cloud Computing
INFORMS Journal on Computing
A randomized pricing decision support system in electronic commerce
Decision Support Systems
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The enhanced abilities of online retailers to learn about their customers' shopping behaviors have increased fears of dynamic pricing, a practice in which a seller sets prices based on the estimated buyer's willingness-to-pay. However, among online retailers, a deviation from a one-price-for-all policy is the exception. When price discrimination is observed, it is often in the context of customer outrage about unfair pricing. One setting where pricing varies is the name-your-own-price (NYOP) mechanism. In contrast to a typical retail setting, in NYOP markets, it is the buyer who places an initial offer. This offer is accepted if it is above some threshold price set by the seller. If the initial offer is rejected, the buyer can update her offer in subsequent rounds. By design, the final purchase price is opaque to the public; the price paid depends on the individual buyer's willingness-to-pay and offer strategy. Further, most forms of NYOP employ a fixed threshold price policy. In this paper, we compare a fixed threshold price setting with an adaptive threshold price setting. A seller who considers an adaptive threshold price has to weigh potentially greater profits against customer objections about the perceived fairness of such a policy. We first derive the optimal strategy for the seller. We analyze the effectiveness of an adaptive threshold price vis-à-vis a fixed threshold price on seller profit and customer satisfaction. Further, we evaluate the moderating effect of revealing the threshold price policy (adaptive versus fixed) to buyers. We test our model in a series of laboratory experiments and in a large field experiment at a prominent NYOP seller involving real purchases. Our results show that revealing the usage of an adaptive mechanism yields higher profits and more transactions than not revealing this information. In the field experiment, we find that applying a revealed adaptive threshold price can increase profits by over 20 percent without lowering customer satisfaction.