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eBay business all-in-one for dummies
Everything you need to know to start and run a successful eBay business. eBay now has 100 million active users and just keeps growing. And they have turned to --For Dummies books and bestselling eBay author Marsha Collier to help guide them through buying and selling on eBay for over a decade. This nine-books-in-one guide has now been updated to cover all the newest eBay seller tools, new techniques to drive sales, new ways to enhance an eBay business using social media, and more.
Book
Internet Auction Features as Quality Signals
Internet auction companies have developed innovative tools that enable sellers to reveal more information about their credibility and product quality to avoid the \"lemons\" problem. On the basis of signaling and auction theories, the authors propose a typology of Internet auction quality and credibility indicators, adopt and modify Park and Bradlow's (2005) model, and use eBay as an example to examine empirically how different types of indicators help alleviate uncertainty. This empirical evidence demonstrates how Internet auction features affect consumer participation and bidding decisions, what modifies the credibility of quality indicators, and how different buyers react to indicators. The signaling-based hypotheses provide coherent explanations of consumers' bidding behavior. As the first empirical study to evaluate the signaling role of comprehensive Internet auction institutional features in mitigating the adverse selection problem, this research provides evidence to clarify the economic foundation behind innovative Internet auction designs.
Journal Article
Autonomous Bidding Agents
E-commerce increasingly provides opportunities for autonomous bidding agents: computer programs that bid in electronic markets without direct human intervention. Automated bidding strategies for an auction of a single good with a known valuation are fairly straightforward; designing strategies for simultaneous auctions with interdependent valuations is a more complex undertaking. This book presents algorithmic advances and strategy ideas within an integrated bidding agent architecture that have emerged from recent work in this fast-growing area of research in academia and industry. The authors analyze several novel bidding approaches that developed from the Trading Agent Competition (TAC), held annually since 2000. The benchmark challenge for competing agents--to buy and sell multiple goods with interdependent valuations in simultaneous auctions of different types--encourages competitors to apply innovative techniques to a common task. The book traces the evolution of TAC and follows selected agents from conception through several competitions, presenting and analyzing detailed algorithms developed for autonomous bidding. Autonomous Bidding Agents provides the first integrated treatment of methods in this rapidly developing domain of AI. The authors--who introduced TAC and created some of its most successful agents--offer both an overview of current research and new results. Michael P. Wellman is Professor of Computer Science and Engineering and member of the Artificial Intelligence Laboratory at the University of Michigan, Ann Arbor. Amy Greenwald is Assistant Professor of Computer Science at Brown University. Peter Stone is Assistant Professor of Computer Sciences, Alfred P. Sloan Research Fellow, and Director of the Learning Agents Group at the University of Texas, Austin. He is the recipient of the International Joint Conference on Artificial Intelligence (IJCAI) 2007 Computers and Thought Award.
eBook