Bidders In Online Auctions Research Articles

Winner's regret in online C2C Auctions: an automatic thinking perspective

AbstractWhile human beings embody a unique ability for planned behaviour, they also often act automatically. In this study, we draw on the automatic thinking perspective as a meta‐theoretic lens to explain why online auction bidders succumb to both trait impulsiveness and sunk cost, ultimately leading them to experience winner's regret. Based on a survey of 301 online auction participants, we demonstrate that both trait impulsiveness as an emotional trigger and sunk cost as a cognitive trigger promote winner's regret. By grounding our research model in the automatic thinking view, we provide an alternative meta‐theoretical lens from which to view online bidder behaviour, thus bolstering our current understanding of winner's regret. We also investigate the moderating effects of competition intensity on the relationships between the triggers of automatic thinking and winner's regret. Our results show that both trait impulsiveness and sunk cost have significant impacts on winner's regret. We also found that the relationship between these two triggers and winner's regret is moderated by competition intensity.

Multiple overlapping online auction market: Bidder strategic mixture analysis using entropy

Online auction bidders are sensitive to the information revealed from other overlapping auctions as well as the auction they are bidding on. As the Internet Technology enables signaling of auction information across multiple overlapping auctions, it affects overall bidder behavior, more specifically, their entry and bidding strategies. In this paper, we take an approach of entropy to capture essential information regarding the mixture of heterogeneous bidder strategies. While prior research demonstrated the influence of surrounding market forces on various aspects of bidder behavior, we develop a concise yet more integrated proxy that contains comprehensive information about these aspects. We find that entropy state of an auction is endogenous to the overlap of multiple auctions. Given that an auctioneer has limited control over entry and bidding activities of bidders, identification of unique strategic pattern based on entropy state provides an effective estimate of the market revenue.

Finite mixture partial least squares for segmentation and behavioral characterization of auction bidders

The purpose of this study is to demonstrate how to empirically segment, without a priori knowledge, online auction bidders using experimental data and finite mixture models. The proposed method utilizes a finite mixture partial least squares (FIMIX-PLS) approach to examine bidder behaviors and personality characteristics, evaluate bidder differences, and then segment the bidders. The empirical experiment is conducted for two different auction mechanisms — English and Vickrey. Results from both auction mechanisms indicate that FIMIX-PLS is capable of profiling and segmenting the bidders based on their individual characteristics. The post hoc analysis confirms the segmentation scheme and the capability of FIMIX-PLS in segmenting bidders into statistically identifiable homogeneous groups without a priori information of group characteristics. Such advantage is practical for online businesses dealing with increasing amount of data about their customers on a real time basis.

Contrast effects in online auctions

Prior research has shown online auction features can serve as information cues and affect consumers’ willingness-to-pay. We argue that auctions are not only affected by their information cues but also by contrasting, peripheral information cues from adjacent auction listings. Applying contrast effects theory, we examined the moderating effects of time urgency and persuasion intent on the processing of contrasting peripheral information from adjacent auctions. Using two controlled experiments and an empirical field study, we showed that time urgency experienced by bidders in online auctions resulted in increased heuristic processing of contrasting information from adjacent auction listings. Under time pressure, bidders were more likely to be affected by this contrasting peripheral information. We also found that bidders will discount contrasting peripheral information if they perceive salient persuasion intents in advertising presented by the auctioneers. The resulting contrast effects ultimately lead to changes in willingness-to-pay and underscore the importance of peripheral information from adjacent auctions in impacting auction outcomes.

Analyzing the performance of multiple agents with varying bidding behaviors and standard bidders in online auctions

Online auctions have provided an alternative trading method to exchange items without the geographical and time constraints. However, buyers would face difficulties in searching, monitoring, and selecting an auction to participate in. As a consequence, agent technology is introduced to overcome these pitfalls. In this paper, heterogeneous intelligent agents and heterogeneous standard bidders are generated in a simulated auction market and their performances are measured. By doing so, it would further simulate the real online auction marketplace where bidders may have different bidding behaviors or implement different bidder agents. From the simulated results, the average winner's utility, the average number of winning auctions, the average closing price and the average median consumer surplus ratio are used to evaluate the winners' performances. From the results obtained, it is generalized that by using intelligent bidder agents to participate in online auctions, it benefits the bidders. Besides that, market economy is reviewed based on the results obtained.

A Real-Time Self-Adaptive Classifier for Identifying Suspicious Bidders in Online Auctions

With the significant increase of available item listings in popular online auction houses nowadays, it becomes nearly impossible to manually investigate the large amount of auctions and bidders for shill bidding activities, which are a major type of auction fraud in online auctions. Automated mechanisms such as data mining techniques were proved to be necessary to process this type of increasing workload. In this paper, we first present a framework of Real-Time Self-Adaptive Classifier (RT-SAC) for identifying suspicious bidders in online auctions using an incremental neural network approach. Then, we introduce a clustering module that characterizes bidder behaviors in measurable attributes and uses a hierarchical clustering mechanism to create training datasets. The neural network in RT-SAC is initialized with the training datasets, which consist of labeled historical auction data. Once initialized, the network can be trained incrementally to gradually adapt to new bidding data in real time, and thus, it supports efficient detection of suspicious bidders in online auctions. Finally, we utilize a case study to demonstrate how parameters in RT-SAC can be tuned for optimal operations and how our approach can be used to effectively identify suspicious online bidders in real time.

The desire to win: The effects of competitive arousal on motivation and behavior

The paper theoretically elaborates and empirically investigates the “competitive arousal” model of decision making, which argues that elements of the strategic environment (e.g., head-to-head rivalry and time pressure) can fuel competitive motivations and behavior. Study 1 measures real-time motivations of online auction bidders and finds that the “desire to win” (even when winning is costly and will provide no strategic upside) is heightened when rivalry and time pressure coincide. Study 2 is a field experiment which alters the text of email alerts sent to bidders who have been outbid; the text makes competitive (vs. non-competitive) motivations salient. Making the desire to win salient triggers additional bidding, but only when rivalry and time pressure coincide. Study 3, a laboratory study, demonstrates that the desire to win mediates the effect of rivalry and time pressure on over-bidding.

Effects of e‐service quality on loyalty intention: an empirical study in online auction

PurposeThe purpose of this study is to draw on expectancy disconfirmation theory (EDT) to explore e‐service quality and the factors influencing an individual's loyalty intention towards online auctions.Design/methodology/approachThe authors conducted an empirical study and data were collected from a total of 619 bidders in online auctions. A structural equation modeling (SEM) is used to assess the relationships of the research model.FindingsThe findings show that e‐service quality dimensions including efficiency, privacy protection, contact, fulfillment, and responsiveness have statistically significant influences on buyer's disconfirmation. Besides, buyers' disconfirmation of online auctions is positively associated with their satisfaction, and their satisfaction is positively associated with loyalty intentions.Practical implicationsThe auctioneer should focus on purchase efficiency and protect the buyer's privacy. The seller should actively make contact with the buyer, fulfill order delivery, and respond to problems. Thus, the performances of auctioneer and seller are the true reassurance to buyers.Originality/valueThis study, which aims to shed light on e‐service quality in online auctions, is the first study that has applied an EDT‐based model to investigate the determinants of buyer's loyalty intention in online auctions.

Collusion in Second‐Price Auctions under Minimax Regret Criterion

Collusion in auctions, with different assumptions on distributions of bidders' private valuation, has been studied extensively over the years. With the recent development of on‐line markets, auctions are becoming an increasingly popular procurement method. The emergence of Internet marketplaces makes auction participation much easier and more convenient, since no physical presence of bidders is required. In addition, bidders in on‐line auctions can easily switch their identities. Thus, it may very well happen that the bidders in an auction have very little, if any, prior knowledge about the distributions of other bidders' valuations. We are proposing an efficient distribution of collusive profit for second‐price sealed bid auctions in such an environment. Unlike some known mechanism, which balance the budget only in expectation, our approach (which we call Random k) balances the budget ex‐post. While truth‐telling is not a dominant strategy for Random k, it is a minimax regret equilibrium.

Winner's curse and parallel sales channels—Online auctions linked within e-tail websites

A sample of 416 online auctions was examined to determine the extent of overpayment (winner's curse) where online auctions and e-tail websites were linked together to form a parallel sales channel. The results indicated that 8.7% of the highest winning online auction bidders exceeded e-tail posted reference prices of identical retail merchandise found at the same website. Significantly, such bids exceeded the reference prices by a mean percentage dollar amount of 14.1% thus suggesting the existence of a winner's curse. The results also indicated that (1) there was a significant negative association between reference price and mean percentage dollar amount overbid; (2) there was a significant negative association between auction lot size and mean percentage dollar amount overbid; and (3) there was no significant association between overtime auctions and mean percentage dollar amount overbid. While manipulation of reference price and auction lot size might minimize winner's curse, erratic or irrational behavior (by online auction and/or e-tail websites) may lead to disinformation.

The roles of players and reputation: Evidence from eBay online auctions

The uncertainty about product quality and the traders' credibility may impede the liquidity and efficiency of online auctions. In contrast to the literature on online feedback mechanisms that evaluate sellers' reputation without distinguishing their roles in the transactions, we claim that “not all reputation is created equal” — bidders in online auctions assess sellers' reputation based on their feedback history gained from sales solely. This paper explores the impact of the consumers' above behavior on the reputation system with an analytical model and empirical evidence from a popular online auction website eBay.

What Attracts Bidders to Online Auctions and What is Their Incremental Price Impact?

Based on data from eBay auctions of classic comic books, we explore the determinants of the number of bidders in online auctions and the impact of additional bidders on the auction price. We test for winner's curse and examine how secret reserve prices impact both bidding activity and the auction price. In our data set, the number of bidders varies widely from zero in 20.4% of the observed auctions to ten or more in 16.5% but 70% of this variation in bidder numbers is predictable based on the comic being auctioned and attributes of the seller and auction such as third party certification of the comic's condition, the seller's reputation, and the length of the auction. Each additional bidder tends to increase the realized auction price about 2.4%. A minor part of this increase is apparently either because the auctions have a private value component or because some bidders fail to fully adjust for winners curse. However, most appears due to the inability of bidders to accurately determine the number of bidders they are bidding against. The presence of a secret reserve price sharply reduces the number of bidders in an auction but has little impact on what individual bidders are willing to bid.

An Empirical Investigation Into the Use of Heuristics and Information Cues by Bidders in Online Auctions

Bidders in online auctions can choose from among thousands of listed products that vary in quality, quantity and condition. Each listing varies in the amount and quality of information provided by the seller. Prior research suggests that when faced with complex and uncertain situations, individuals tend to use simple heuristics and cues in a bounded rational decision‐making process. The current study explores the differences in listings, and the choice and impact of varying information cues on bidding behaviour. Empirical evidence is presented of bidders using the sellers' minimum (initial) price as a prescreening signal, and being attracted to auctions with low initial prices while avoiding auctions that have a higher initial price. The research also explores the impact of auction attractiveness on final values, and finds a significant impact of vivid information signals on the number of bidders and the final values of competing auctions.