Repeated use of online auctions: investigating individual seller motivations

Online auctions, connecting sellers and buyers on the Internet, are a specific type of eMarketplaces. eMarketplaces rely on repeat business in order to build scale, yet little is known about the drivers of repeated use by sellers. Based on an empirical investigation of individual, private sellers, this paper identifies the determinants of their repeated use of online auctions. It demonstrates that repeated use is driven by expectancy concerning performance and effort and by the facilitating conditions provided by auction sites. However, repeated use of online auctions is neither directly motivated by social influences, nor is it moderated by sellers’ demographics.     

Inferring preference correlations from social networks

Identifying consumer preferences is a key challenge in customizing electronic commerce sites to individual users. The increasing availability of online social networks provides one approach to this problem: people linked in these networks often share preferences, allowing inference of interest in products based on knowledge of a consumer’s network neighbors and their interests. This paper evaluates the benefits of inference from online social networks in two contexts: a random graph model and a web site allowing people to both express preferences and form distinct social and preference links. We determine conditions on network topology and preference correlations leading to extended clusters of people with similar interests. Knowledge of when such clusters occur improves the usefulness of social network-based inference for identifying products likely to interest consumers based on information from a few people in the network. Such estimates could help sellers design customized bundles of products and improve combinatorial auctions for complementary products.     

MoRVAM: A reverse Vickrey auction system for mobile commerce

Internet auctions bring buyers and sellers together for the purpose of trading goods and services online. In order to get the goods, a buyer must search for items through several auction sites. When the auction starts, the buyer needs to connect to these auction sites frequently so that he/she can monitor the bid states and re-bid. In this paper, we propose an automated negotiation model between two participants, for mobile commerce, using collaborative mobile agents called MoRVAM, which mediates between the buyer and the sellers, and executes bidding asynchronously and autonomously. A new RVT protocol is also implemented to achieve unconditional bid privacy. Advantages of the RVT protocol are addressed as well. All the bidding process can be implemented without revealing losing bid and unnecessary information.     

Online auctions,messaging, communication and social facilitation: a simulation and experimental evidence

This is a study of social facilitation effects in online auctions. We focus on the growth in online auctions, and the emergence of instant messaging and communication availability technologies. These two trends merge to provide a collaborative online social framework in which computer mediated communication may affect the behaviour of participants in online auctions. The interaction between buyers and sellers in traditional, face-to-face markets creates phenomena such as social facilitation, where the presence of others impacts behaviour and performance. In this study we attempt to replicate and measure social facilitation effects under the conditions of virtual presence. Does social facilitation apply to online auctions, and if so, how can it influence the design of online settings? We developed and used a simulated, Java-based Internet Dutch auction. Our findings indicate that social facilitation does indeed occur. In an experimental examination, participants improve their results and stay longer in the auction under conditions of higher virtual presence. Participants also indicate a preference for auction arrangements with higher degrees of virtual presence. Theoretically, this study contributes to the study of social facilitation, adding evidence of the effect when the presence is virtual.     

On the Natural Selection of Market Choice

An evolutionary approach to the problem of economic mechanism choice is presented. It demonstrates the power that a single participant has on the choice of a preferred market mechanism. A population of sellers, each with one of two possible economic mechanisms, facing potential buyers, is presented as a test model. It is shown that if two auctions, such as first- and second-price auctions, are to attract an identical number of buyers, although under the model assumptions their expected revenues are identical, sellers using a first-price auction mechanism will be selected for. However, if a second-price auction attracts one additional buyer, then it will be selected for by the evolutionary process. These results are extended to the choice between an arbitrary k- and l-price auctions.     

Operational efficiency of decentralized Internet auction mechanisms

The recent consumer-to-consumer (C2C) Internet auction boom at eBay, Yahoo, Amazon, and other sites has added new theoretical challenges to the science of auctions. This paper uses experiments with economically-motivated human subjects to measure the operational efficiency of decentralized Internet auction mechanisms as they compare to centralized double auction mechanisms. Subjects are recruited randomly from the undergraduate population of a large urban university to be buyers or sellers in a simulated trading environment. They are randomly assigned costs and values for 10 units of a virtual product. During the experiment subjects trade these units through computer terminals in auctions similar to those held on eBay and generate profits, which subjects receive at the end of the experiment. The paper uses data from this experiment and previous laboratory experiments to compare operational efficiency and convergence pattern of prices to equilibrium levels in continuous double auctions versus online Internet auctions based on two variables: auction size and time. Experimental results suggest that, because of their decentralized nature, Internet auctions require a few more participants and more time to achieve operational efficiency levels than centralized markets which use continuous double auction mechanisms.     

Should we collude? Analyzing the benefits of bidder cooperation in online group-buying auctions

Cooperation among bidders in traditional auctions is typically forbidden. This is because it is viewed as being harmful to the interests of sellers, who hope to obtain fair prices for their sale items. It also may be harmful to other bidders who are not able to take advantage of any cooperation that is occurring. In online group-buying auctions, in contrast to traditional auctions, cooperation results in higher welfare, leading to market expansion that benefits buyers and sellers, as well as the auction intermediary. This has not been well understood in prior research, however. In this article, we show how the online group-buying auction mechanism on the Internet can be effectively enhanced to produce higher welfare for the participants. The key to achieving this, we find, is for the auction intermediary to provide a means for bidders to cooperate, so as to collectively express greater demand. Such cooperation, it turns out, permits the group-buying auction mechanism to dominate the fixed-price mechanism from the seller’s point of view under some circumstances. Through an analytical modeling analysis, we offer insights into how sellers can set their group-buying auction price curves more effectively, so as to take advantage of bidder cooperation to improve auction performance. We further argue that the goal of the auction intermediary should be to offer an information sharing mechanism to facilitate bidding ring formation, as a means to maximize the value of this market mechanism.     

An options-based solution to the sequential auction problem

The sequential auction problem is commonplace in open, electronic marketplaces such as eBay. This is the problem where a buyer has no dominant strategy in bidding across multiple auctions when the buyer would have a simple, truth-revealing strategy if there was but a single auction event. Our model allows for multiple, distinct goods and market dynamics with buyers and sellers that arrive over time. Sellers each bring a single unit of a good to the market while buyers can have values on bundles of goods. We model each individual auction as a second-price (Vickrey) auction and propose an options-based, proxied solution to provide price and winner-determination coordination across auctions. While still allowing for temporally uncoordinated market participation, this options-based approach solves the sequential auction problem and provides truthful bidding as a weakly dominant strategy for buyers. An empirical study suggests that this coordination can enable a significant efficiency and revenue improvement over the current eBay market design, and highlights the effect on performance of complex buyer valuations (buyers with substitutes and complements valuations) and varying the market liquidity.     

Implications of a Reserve Price in an Agent-Based Common-Value Auction

Auction sellers can use a reserve price to require a minimum bid before items are sold. Theoretical and experimental research has tested the influence of a reserve price in an independent private values auction, but little focus has been given to the influence of a reserve price in a first-price common-value auction. We establish an agent-based first-price common-value auction to determine the impact of the reserve price with two buyers and with three buyers. An agent-based approach to this problem is both a unique contribution to the literature and appropriate since finding analytical solutions with common-value auctions is difficult. The agent-based model approach also allows us to consider buyers that have non-symmetric bid functions. Furthermore, we introduce a combination of numerical integration techniques with a new particle swarm learning algorithm. The buyers in the model choose their expected-net-revenue-maximizing bid price, and sellers choose their expected-revenue-maximizing reserve price. In the two-buyer and three-buyer auction, a reserve price increases the equilibrium winning bid price, decreases the probability that the item is sold, and increases the seller’s expected revenue. A reserve price in a two-buyer auction increases the winning bid price more than including an additional buyer in the auction with no reserve price. However, due to only receiving a salvage value when the item does not sell in the auction, the seller’s expected revenue is higher in the three-buyer first-price common-value auction without a reserve price than in the two-buyer auction with a reserve price.     

The impact of perceived ease of use on Internet service adoption: The moderating effects of temporal distance and perceived risk

Perceived ease of use is found to affect consumer’s intention toward using an Internet-based service. However, to protect online transaction security, more security verification mechanisms are established which in turn increase the complexity and difficulty of using online services. This study proposed that the importance of perceived ease of use is depending on short- vs. long-term transaction expectation, product type, and whether security concern information is presented. In certain situations buyers or sellers of an Internet service may tolerate the inconvenience of using the Internet-based service. A 2 (verification requirement) × 2 (network externality) × 2 (short vs. long term) between-subject design was conducted on sellers of an auction site and a 2 (verification requirement) × 2 (product type) × 2 (with vs. without security concern information) between-subject design was conducted on buyers of an auction site. The results of two studies suggest that perceived ease of use increases the intention toward using online service when sellers expect that the Internet service usage is only for a short-term transaction or when buyers have no access of the security concern information on the website. In contrast, sellers prefer using an online service which requires a relatively high verification requirement when the purpose of using Internet service is for long-term transaction or when buyers have access of security concern information. The results also showed that perceived network externality positively affect sellers’ intention toward using an auction website.     

COMBINING TRUST MODELING AND MECHANISM DESIGN FOR PROMOTING HONESTY IN E‐MARKETPLACES

In this paper, we propose a novel incentive mechanism for promoting honesty in electronic marketplaces that is based on trust modeling. In our mechanism, buyers model other buyers and select the most trustworthy ones as their neighbors to form a social network which can be used to ask advice about sellers. In addition, however, sellers model the reputation of buyers based on the social network. Reputable buyers provide truthful ratings for sellers, and are likely to be neighbors of many other buyers. Sellers will provide more attractive products to reputable buyer to build their own reputation. We theoretically prove that a marketplace operating with our mechanism leads to greater profit both for honest buyers and honest sellers. We emphasize the value of our approach through a series of illustrative examples and in direct contrast to other frameworks for addressing agent trustworthiness. In all, we offer an effective approach for the design of e‐marketplaces that is attractive to users, through its promotion of honesty.     

面向资源最优分配的深度学习双边拍卖算法

针对拍卖过程中计算效率低和利益分配不合理等问题, 本文提出了一种基于深度学习的迭代双边拍卖算法. 该算法通过买卖双方的初始报价数据训练基于神经网络的资源最优分配模型, 调用训练好的模型对实时报价数据快速响应, 直接求解经纪人最优分配问题(BAP)以实现计算资源分配, 显著地减小了计算代价, 提高了算法的计算效率. 进一步, 针对利益分配不合理等问题, 在迭代双边拍卖框架的支出规则和收入规则中引入调节因子用于调节买卖双方的利益, 解决已有算法在实现社会福利最大化过程中利益分配不合理的问题. 实验结果验证了该算法的有效性和优越性, 在运行时间、社会福利、买家利益、卖家利益和经纪人利益等多项指标均明显优于已有的迭代双边拍卖算法     

Bidding behavior in dynamic auction settings: An empirical analysis of eBay

We study the impact of dynamic environment of eBay auctions on bidding behavior. Due to high-speed Internet and practically costless search possibilities, bidding behavior is no longer a function of characteristics of a single auction but depends on auctions running simultaneously, completed auctions, available Buy-It-Now prices as well as various outside options. We study how this dynamic market affects a bidder’s choice of participating in an auction or leaving eBay for an outside alternative. We analyze Texas Instruments (TI-83) Graphing Calculator auctions presented on eBay. We estimate a random-effects probit model to study bidders’ probability of staying in eBay, while controlling for unobservable individual-specific heterogeneity. Our main result shows that Market Tightness – the ratio of bidders to sellers – has a negative and significant effect on bidders’ decision to remain in eBay. Moreover, variables containing information from other eBay auctions significantly affect bidders’ participation decision, thus emphasizing the importance of the dynamic, multi-auction environment in eBay marketplace for potential buyers.     

Improving the Performance of Online Auctions Through Server-side Activity-based Caching

Online auction sites have very specific workloads and user behavior characteristics. Previous studies on workload characterization conducted by the authors showed that (1) bidding activity on auctions increases considerably after 90% of an auction’s life time has elapsed, (2) a very large percentage of auctions have a relatively low number of bids and bidders and a very small percentage of auctions have a high number of bids and bidders, (3) prices rise very fast after an auction has lasted more than 90% of its life time. Thus, if bidders are not able to successfully bid at the very last moments of an auction because of site overload, the final price may not be as high as it could be and sellers, and consequently the auction site, may lose revenue. In this paper, we propose server-side caching strategies in which cache placement and replacement policies are based on auction-related parameters such as number of bids placed or percent remaining time till closing time. A main-memory auction cache at the application server can be used to reduce accesses to the back-end database server. Trace-based simulations were used to evaluate these caching strategies in terms of cache hit ratio and cache efficiency. The performance characteristics of the best policies were then evaluated through experiments conducted on a benchmark online auction system.     

DESIGN OF AN ITERATIVE MULTI-ITEM DOUBLE-AUCTION MECHANISM

There have been many multi-item auction mechanisms. As it is not allowed for both sellers and buyers to submit bids and offers in nearly all existing multi-item auction mechanisms, the impartial profit distribution for both sellers and buyers cannot be realized in those mechanisms. Although it is possible for both sellers and buyers to submit bids and offers in a combinatorial exchange, the impartial profit distribution for all individual participants cannot be realized due to its pricing mechanism that focuses only on enforcing budget balance. This paper proposes an iterative multi-item unit-demand and unit-supply double-auction mechanism with a new winner determination and pricing mechanism for distributing profit impartially from the viewpoints of individual participants regardless of whether they are buyers or sellers. It also analyzes the theoretical features of the proposed auction mechanism, including those related to its strategic behavior and efficiency.     

Online double auction mechanism for perishable goods

One-sided auctions are used for market clearing in the spot markets for perishable goods because production cost in spot markets is already “sunk.” Moreover, the promptness and simplicity of one-sided auctions are beneficial for trading in perishable goods. However, sellers cannot participate in the price-making process in these auctions. A standard double auction market collects bids from traders and matches the higher bids of buyers and lower bids of sellers to find the most efficient allocation, assuming that the value of unsold items remains unchanged. Nevertheless, in the market for perishable goods, sellers suffer a loss when they fail to sell their goods, because their salvage values are lost when the goods perish. To solve this problem, we investigate the suitable design of an online double auction for perishable goods, where bids arrive dynamically with their time limits. Our market mechanism aims at improving the profitability of traders by reducing trade failures in the face of uncertainty of incoming/departing bids. We develop a heuristic market mechanism with an allocation policy that prioritizes bids of traders based on their time-criticality, and evaluate its performance experimentally using multi-agent simulation. We find out that our market mechanism realizes efficient and fair allocations among traders with approximately truthful behavior in different market situations.     

Assessing the benefits of group-buying-based combinatorial reverse auctions

Combinatorial reverse auctions represent a popular business model in procurement. For multiple buyers, different procurement models based on combinatorial reverse auctions may be applied. For example, each buyer may hold one combinatorial reverse auction independently. Alternatively, the buyers may delegate the auction to a group-buyer and let the group-buyer hold only one combinatorial reverse auction on behalf of all the buyers. A combination of a combinatorial reverse auctions with the group-buying model makes it possible to reduce the overall cost to acquire the required items significantly due to complementarities between items. However, combinatorial reverse auctions suffer from high computational complexity. To assess the advantage of combining group-buying with combinatorial reverse auctions, three issues must be addressed, including performance, computational efficiency and the scheme to reward the buyers. This motivates us to compare the performance and efficiency of the aforementioned two different combinatorial reverse auction models and to study the possible schemes to reward the buyers. To achieve these objectives, we first illustrate the advantage of group-buying-based combinatorial reverse auctions over multiple independent combinatorial reverse auctions. We then formulate the problems for these two combinatorial reverse auction models and propose solution algorithms for them. We compare performance and computational efficiency for these two combinatorial reverse auction models. Our analysis indicates that a group-buying-based combinatorial reverse auction not only outperforms multiple independent combinatorial reverse auctions but also is more efficient than multiple independent combinatorial reverse auctions. We also propose a non-uniform scheme to reward the buyers in group-buying based combinatorial reverse auctions.     

Market power and efficiency in a computational electricity marketwith discriminatory double-auction pricing

This study reports experimental market power and efficiency outcomes for a computational wholesale electricity market operating in the short run under systematically varied concentration and capacity conditions. The pricing of electricity is determined by means of a clearinghouse double auction with discriminatory midpoint pricing. Buyers and sellers use a modified Roth-Erev individual reinforcement learning algorithm (1995) to determine their price and quantity offers in each auction round. It is shown that high market efficiency is generally attained and that market microstructure is strongly predictive for the relative market power of buyers and sellers, independently of the values set for the reinforcement learning parameters. Results are briefly compared against results from an earlier study in which buyers and sellers instead engage in social mimicry learning via genetic algorithms     

Web crawling and filtering for on-line auctions from a social network perspective

The on-line auction is one of the most successful types of electronic marketplace and has been the subject of many academic studies. In recent years, empirical research on on-line auctions has been flourishing because of the availability of large amounts of high-quality bid data from on-line auction sites. However, the increasingly large volumes of bid data have made data collection ever more complex and time consuming, and there are no effective resources that can adequately support this work. So this study focuses on the parallel crawling and filtering of on-line auctions from the social network perspective to help researchers collect and analyze auction data more effectively. The issues raised in this study include parallel crawling architecture, crawling strategies, content filtering strategies, prototype system implementation, and a pilot test of social network analysis. Finally we conduct an empirical experiment on eBay US and Ruten Taiwan to evaluate the performance of our crawling architecture and to understand auction customers?? bidding behavior characteristics. The results of this study show that our parallel crawling and filtering methods are able to work in the real world, and are significantly more effective than manual web crawling. The collected data are useful for drawing social network maps and analyzing bidding problems.     

Interaktive Preisfindung als zwischenbetriebliche Prozess-integration auf Basis von Webservices

Interactive pricing mechanisms such as auctions or negotiations allow buyers to participate in the pricing process and enable sellers to price-discriminate. This article depicts a service-oriented architecture based on web services to enable a seamless integration of interactive pricing functionality into existing online-shops. Thereby, we grant online-retailers access to complex pricing mechanisms while we reduce both the cost and the time needed to implement such functionality. Illustrating the case of Pricing Systems (www.pricing-systems.com), we show that such integration is feasible and currently in use in up-and-running Electronic Commerce retailer sites.