Replicating Online Yankee Auctions to Analyze Auctioneers' and Bidders' Strategies
Information Systems Research
Managing Online Auctions: Current Business and Research Issues
Management Science
Partial-Repeat-Bidding in the Name-Your-Own-Price Channel
Marketing Science
Monte Carlo approximation in incomplete information, sequential auction games
Decision Support Systems - Special issue: Decision theory and game theory in agent design
Strategic bidder behavior in sponsored search auctions
Decision Support Systems
Online Haggling at a Name-Your-Own-Price Retailer: Theory and Application
Management Science
Promotional Chat on the Internet
Marketing Science
Dynamic Pricing on the Internet: Importance and Implications for Consumer Behavior
International Journal of Electronic Commerce
Price formation and its dynamics in online auctions
Decision Support Systems
A support system for predicting eBay end prices
Decision Support Systems
Designing online selling mechanisms: Transparency levels and prices
Decision Support Systems
Identification of influencers - Measuring influence in customer networks
Decision Support Systems
Auction Advisor: an agent-based online-auction decision support system
Decision Support Systems
Seller heterogeneity in electronic marketplaces: A study of new and experienced sellers in eBay
Decision Support Systems
A decision support system for stock investment recommendations using collective wisdom
Decision Support Systems
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In Name-Your-Own-Price auctions (NYOP) prospective buyers bid against a secret reserve price set by the seller and only win the auction at the price of their bid if it is equal or higher than the seller's reserve price. Thus, bidders who want to win the auction without too much overbidding have a strong incentive to learn more about the seller's secret reserve price, possibly via their own network of friends, digital networks or online communities. Information sharing and information diffusion in digital networks can change bidding behavior and thus have important implications for sellers in NYOP markets. We develop a decision support system that enables sellers to assess the impact of information diffusion and to analyze the profitability of different seller strategies. We build the system from the bottom up by developing and testing a model of agents' bidding behavior which constitutes the basis for analyzing the effects of different network structures and seller strategies on profit. Sellers can react to information diffusion by setting the secret reserve price optimally, taking into account this strategic element by the provision of a forum and by decreasing the quality of information through forum intervention. Our results show that information diffusion can either decrease or increase seller profit depending on the buyers' initial beliefs about the seller's costs. We also show that the structure of the underlying social network is an important driver for information diffusion and that data about social networks might hence be of value to decision makers.