1,357 research outputs found

    Economic Insights from Internet Auctions: A Survey

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    This paper surveys recent studies of Internet auctions. Four main areas of research are summarized. First, economists have documented strategic bidding in these markets and attempted to understand why sniping, or bidding at the last second, occurs. Second, some researchers have measured distortions from asymmetric information due, for instance, to the winner's curse. Third, we explore research about the role of reputation in online auctions. Finally, we discuss what Internet auctions have to teach us about auction design.

    Decision support and data mining for direct consumer-to-consumer trading

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    This paper describes a decision support system that integrates a hybrid neighborhood search algorithm for determining the price of sale item when it is placed for trading in the Internet. The seller would provide the condition and number of years of usage of the used item, and the intelligent system would provide real-time search on related items in the marketplace and suggest a price for trading. Data mining techniques are explored for efficient processing of a vast amount of information in the database tables. In addition, the trading system would also have the intelligence of recommending items or products to a potential buyer given the previous purchase patterns. Related items to a recently purchased item would also be suggested with an aim of providing friendly reminders and recommendations so that the user of the website would obtain a pleasant trading experience. © 2014 Infonomics Society.published_or_final_versio

    Design intelligence of web application for internet direct consumer-to-consumer trading

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    An online web application called Student-Trade has been developed. It is a state-of-the-art platform for direct consumer-to-consumer trading in the Internet. The platform is targeted for direct consumer-to-consumer trading among university students. The items for trading include books, household items, electronics, housing rental, sports equipment and tutoring services. This paper is on the design intelligence of the Student-Trade web application. One objective is to help the user to decide on the selling price of his item when the item is being posted in the web application. The system integrates a hybrid neighborhood search algorithm for determining the price of sale item when it is placed for trading in the Internet. Data mining techniques are explored for efficient processing of a vast amount of information in the database tables. In addition, the trading system would also have the intelligence of recommending items or products to a potential buyer given the previous purchase patterns. The aim is to provide a pleasant trading experience for the user. © 2015 IEEE.published_or_final_versio

    Country-Specific Effects of Reputation and Information: A Comparison of Online Auctions in Germany, the UK, and the US

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    Empirical studies on the effect of sellers’ reputation on closing prices in online auctions present mixed results. A large number of studies addresses reputational effects in one country, especially in the US. Only a small number of cross-country studies inspect the moderating role of institutional frameworks on bidder behavior. The purpose of this paper is to examine if country-specific differences in the formal and informal institutional framework influence the effects of reputation and information signals on final prices in online auctions. From the perspective of the New Institutional Economics, management decisions and individuals’ characteristics are affected by the institutional framework, which consists of cultural aspects as well as a set of social and legal rules and regulations. Therefore, bidders that are influenced by one institutional framework have different preferences, expectations, and perceptions about reputation and information in online auctions than individuals socialized by another institutional framework. In order to examine the effects of reputation and information on prices as well as to asses cross-country similarities and differences in these effects, a sample of 6,166 homogenous online auctions, conducted on the respective eBay websites in Germany, the UK, and the US, is analyzed. The results suggest that either the effects of reputation and product information variables vary significantly across countries or that different variables have an impact on prices in different countries. It can be concluded that country-specific institutional frameworks influence bidder behavior in international online auction markets.reputation, information, online auctions, cross-country studies

    A Comparison of Bidding Strategies for Online Auctions Using Fuzzy Reasoning and Negotiation Decision Functions

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    © 1993-2012 IEEE. Bidders often feel challenged when looking for the best bidding strategies to excel in the competitive environment of multiple and simultaneous online auctions for same or similar items. Bidders face complicated issues for deciding which auction to participate in, whether to bid early or late, and how much to bid. In this paper, we present the design of bidding strategies, which aim to forecast the bid amounts for buyers at a particular moment in time based on their bidding behavior and their valuation of an auctioned item. The agent develops a comprehensive methodology for final price estimation, which designs bidding strategies to address buyers' different bidding behaviors using two approaches: Mamdani method with regression analysis and negotiation decision functions. The experimental results show that the agents who follow fuzzy reasoning with a regression approach outperform other existing agents in most settings in terms of their success rate and expected utility

    Intimidation or Impatience? Jump Bidding in On-line Ascending Automobile Auctions

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    We run a large field experiment with an online company specializing in selling used automobiles via ascending auctions. We manipulate experimentally the maximum amount which bidders can bid above the current standing price, thus affecting the ease with which bidders can engage in jump bidding. We test between the intimidation vs. costly bidding hypotheses of jump bidding by looking at the effect of these jump-bidding restrictions on average seller revenue. We find evidence consistent with costly bidding in one market (Texas), but intimidation in the other market (New York). This difference in findings between the two markets appears partly attributable to the more prominent presence of sellers who are car dealers in the Texas market.

    Money Talks? An Experimental Study of Rebate in Reputation System Design

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    Reputation systems that rely on feedback from traders are important institutions for helping sustain trust in markets, while feedback information is usually considered a public good. We apply both theoretical models and experiments to study how raters' feedback behavior responds to different reporting costs and how to improve market efficiency by introducing a pre-commitment device for sellers in reputation systems. In particular, the pre-commitment device we study here allows sellers to provide rebates to cover buyers' reporting costs before buyers make purchasing decisions. Using a buyer-seller trust game with a unilateral feedback scheme, we find that a buyer’s propensity to leave feedback is more sensitive to reporting costs when the seller cooperates than when the seller defects. The seller’s decision on whether to provide a rebate significantly affects the buyer’s decision to leave feedback by compensating for the feedback costs. More importantly, the rebate decision has a significant impact on the buyer's purchasing decision via signaling the seller's cooperative type. The experimental results show that the rebate mechanism improves the market efficiency.reputation, trust, feedback mechanism, asymmetric information, public goods, experimental economics

    Pricing analysis in online auctions using clustering and regression tree approach

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    Auctions can be characterized by distinct nature of their feature space. This feature space may include opening price, closing price, average bid rate, bid history, seller and buyer reputation, number of bids and many more. In this paper, a price forecasting agent (PFA) is proposed using data mining techniques to forecast the end-price of an online auction for autonomous agent based system. In the proposed model, the input auction space is partitioned into groups of similar auctions by k-means clustering algorithm. The recurrent problem of finding the value of k in k-means algorithm is solved by employing elbow method using one way analysis of variance (ANOVA). Based on the transformed data after clustering, bid selector nominates the cluster for the current auction whose price is to be forecasted. Regression trees are employed to predict the end-price and designing the optimal bidding strategies for the current auction. Our results show the improvements in the end price prediction using clustering and regression tree approach
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