Seller Ratings Research Articles

The Impact of a Seller's Ebay Reputation on Price

eBay provides a large amount of data – enough data to investigate how markets address the problem of asymmetric information. This study analyzes 183 auctions of Apple iPod Touches on eBay.com to find the impact of a seller's rating on price. I found that a seller's percent of negative rating significantly impacts the price they ultimately receive.

Electronic Word-of-Mouth in B2C Virtual Communities: An Empirical Study from CTrip.com

Abstract Virtual communities (VCs) have developed rapidly, with more and more people participating in them to exchange information and opinions. A virtual community is a group of people who may or may not meet one another face to face, and who exchange words and ideas through the mediation of computer bulletin boards and networks. A business-to-consumer virtual community (B2CVC) is a commercial group that creates a trustworthy environment intended to motivate consumers to be more willing to buy from an online store. B2CVCs create a social atmosphere through information contribution such as recommendations, reviews, and ratings of buyers and sellers. Although the importance of B2CVCs has been recognized, few studies have been conducted to examine members' word-of-mouth behavior within these communities. This study proposes a model of involvement, statistics, trust, “stickiness,” and word-of-mouth in a B2CVC and explores the relationships among these elements based on empirical data. The objectives are thre...

Designing aggregation mechanisms for reputation systems in online marketplaces

A seller in an online marketplace with an effective reputation mechanism should expect that dishonest behavior results in higher payments now, while honest behavior results in higher reputation---and thus higher payments---in the future. We briefly survey recent results on the Window Aggregation Mechanism, a widely used class of mechanisms that shows the average value of the seller's ratings within some fixed window of past transactions. We suggest approaches for choosing the window size that maximizes the range of parameters for which it is optimal for the seller to be truthful. We show that mechanisms that use information from a larger number of past transactions tend to provide incentives for patient sellers to be more truthful, but for higher quality sellers to be less truthful. We then discuss a broader class of aggregation mechanisms that weight recent ratings more heavily and show that the same insight applies.

Optimal Windows for Aggregating Ratings in Electronic Marketplaces

Aseller in an online marketplace with an effective reputation mechanism should expect that dishonest behavior results in higher payments now whereas honest behavior results in a better reputation—and thus higher payments—in the future. We study the Window Aggregation Mechanism, a widely used class of mechanisms that shows the average value of the seller's ratings within some fixed window of past transactions. We suggest approaches for choosing the window size that maximizes the range of parameters for which it is optimal for the seller to be truthful. We show that mechanisms that use information from a larger number of past transactions tend to provide incentives for patient sellers to be more truthful but for higher-quality sellers to be less truthful.

Identifying buyers with similar seller rating models and using their opinions to choose sellers in electronic markets

In this paper, we provide a model for designing buyers that can learn to identify trustworthy friends that are honest and share similar opinions in a decentralised electronic market. The buyer rates a seller after having purchased goods from it. It also evaluates friends who provide seller information when requested, to determine their truthfulness, similarity in opinions regarding product expectations, and to identify the differences between its own and its friends' seller rating mechanisms. Trustworthy friends are identified, and the seller ratings provided by them are adjusted to account for the differences in rating systems and then utilised to evaluate sellers. We conducted experiments to confirm that a buyer using the proposed model is able to accurately identify trustworthy friends, accurately adjust the seller reputation ratings provided by them and has higher gains than a buyer acting alone.

Understanding effects of seller’s and bidder’s characteristics on Internet auction applications

This paper discusses the effects of factors on Internet auctions. The anonymity of online auctions is convenient and stressless. However, trust becomes an important issue. Bidders need to feel safe about the reliability of the seller for placing bids on an auction. Seller’s characteristics could affect the evaluation and the deal of the online auctions. This study employs collected eBay data set to analyze the effects of factors, bidders’ and sellers’ characteristics on the final price and the duration of the auction. Important factors are recognized by a probit model. Seller’s items for sale (SIFS), bidder’s lifetime positive feedback (BLPF), and seller’s lifetime positive feedback (SLPF) are the most important factors identified by the probit analysis. Furthermore, impacts of important factors and their interactions are studied by factorial design approach. The results of the study reveal that SLPF plays a major role in affecting the final price (51.2%) and both SIFS and BLPF are the critical roles (20.1% and 28.1%, respectively). Furthermore, the interaction of BLPF and SLPF also are important to affect the final price (4.5%). Consequently, the final price heavily depends on the bidder and seller ratings. For the duration of the auction, SLPF explains most of the variations (62.8%). Seller’s reputation is decisive no matter in performance on the duration of the auction or final price.

Modeling Price Dynamics in eBay Auctions Using Differential Equations

Empirical research of online auctions has grown dramatically in recent years. Studies using publicly available bid data from such websites as eBay.com have found many divergences of bidding behavior and auction outcomes compared with ordinary offline auctions and auction theory. Among the main differences between online and offline auctions are the former's longer duration, anonymity of bidders and sellers, and low barriers of entry. All of these factors lead to dynamics in the bid arrival and price process that change throughout the auction. In this work we examine the price process in a large and diverse set of eBay auctions, for both low-and high-valued items, in terms of item, auction, bidder, and seller characteristics. We propose a family of differential equation models that captures online auction dynamics. In particular, we show that a second-order linear differential equation well approximates the dynamics that occur in our diverse set of auctions. We also introduce a multiple-comparisons test for comparing dynamic models of auction subpopulations, which we use to compare subpopulations of auctions grouped by characteristics of the auction, item, seller, and bidders. We find that price dynamics change throughout the auction and are influenced mostly by factors that affect the level of uncertainty about the outcome (e.g., seller rating, item condition) and the level of competitiveness (e.g., early bidding, number of bids). We accomplish the modeling tasks within the framework of principal differential analysis and functional data models.

Evaluating the trustworthiness of advice about seller agents in e-marketplaces: A personalized approach

In this paper, we present a model for evaluating the trustworthiness of advice about seller agents in electronic marketplaces. In particular, we propose a novel personalized approach for effectively handling unfair ratings of sellers provided to buyer agents from other buyers (called advisors). Our approach offers flexibility for buyers to weight their value for private and public knowledge about advisors. A personalized approach is proposed as well for buyers to model the trustworthiness of sellers, based on the advice provided. Experimental results demonstrate that our approach can effectively model trustworthiness for both advisors and sellers, even when there are large numbers of unfair ratings.

Price formation and its dynamics in online auctions

This research uses functional data modeling to study the price formation process in online auctions. It conceptualizes the price evolution and its first and second derivatives (velocity and acceleration respectively) as the primary objects of interest. Together these three functional objects permit us to talk about the dynamics of an auction, and how the influence of different factors vary throughout the auction. For instance, we find that the incremental impact of an additional bidder's arrival on the rate of price increase is smaller towards the end of the auction. Our analysis suggests that “stakes” do matter and that the rate of price increase is faster for more expensive items, especially at the start and the end of an auction. We observe that higher seller ratings (which correlate with experience) positively influence the price dynamics, but the effect is weaker in auctions with longer durations. Interestingly, we find that the price level is negatively related to auction duration when the seller has low rating whereas in auctions with high-rated sellers longer auctions achieve higher price levels throughout the auction, and especially at the start and end. Our methodological contributions include the introduction of functional data analysis as a useful toolkit for exploring the structural characteristics of electronic markets.

Exploring auction databases through interactive visualization

We introduce AuctionExplorer, a suite of tools for exploring databases of online auctions. The suite combines tools for collecting, processing, and interactively exploring auction attributes (e.g., seller rating), and the bid history (price evolution represented as a time series). Part of AuctionExplorer's power comes from its coupling of the two information structures, thereby allowing exploration of relationships between them. Exploration can be directed by hypothesis testing or exploratory data analysis. We propose a process for visual data analysis and illustrate AuctionExplorer's operations with a dataset of eBay auctions. Insights may improve seller, bidder, auction house, and other vendors' understanding of the market, thereby assisting their decision making process.

Seller rating, price, and default in online auctions

Whereas in brick and mortar stores quality and seller reliability can be effectively communicated through a variety of quality cues, in the online world different mechanisms help sellers establish reputation and trust. Among the commonly used mechanisms by infomediaries that screen and rate sellers are customer-feedback reputation mechanisms where buyers provide seller ratings and feedback. The success of such mechanisms and their impact on prices in electronic markets are described and empirically investigated in this work. Specifically, we investigate both theoretically and empirically (1) whether seller ratings affect consumer valuations, particularly in the presence of insurance; (2) whether seller ratings are indicative of future default likelihood; and (3) whether a seller who is terminal (about to go out of business) is more likely to default prior to exiting.

Manifestations of interpersonal trust in online interaction

As the internet continues to expand globally, the understanding of the micro-level connections between culture and online interaction is vital from a scientific perspective. This article explores the effects of societal values of interpersonal trust on online interactions. Using data from the World Values Survey and Inglehart’s (1997) scores on interpersonal trust, the study compares the effect of seller feedback ratings on online auction participation in three economically similar but culturally distinct countries, Canada, France, and Germany. The results indicate a significant interaction between culture, interpersonal trust levels, and seller ratings on bidder participants. Cultures that exhibit high levels of interpersonal trust tend to participate in online auctions irrespective of the sellers’ feedback ratings. However, in low trust cultures, seller ratings have a significant effect on bidders. The extent of the effect seems to depend on the degree of trust and the variation in seller ratings.

Price Formation and its Dynamics in Online Auctions

This research uses functional data modelling to study the price formation process of online auctions. It conceptualizes the price curve and its first and second derivatives(velocity and acceleration respectively) as the primary objects of interest. Together these three functional objects permit us to talk about the energy of an auction, and how the influence of its determinants vary as a function of auction time. For instance, we find that the incremental impact of an additional bidder's arrival on the rate of price increase is smaller towards the end of the auction. Our analysis suggests that stakes do matter and that the rate of price increase is faster for more expensive items, especially at the start and the end of an auction. It is observed that higher seller ratings (which correlate with experience) positively influence the price dynamics, but the effect is weaker in auctions with longer durations. Interestingly, we find that the price level is negatively related to auction duration when the seller has low rating whereas in auctions with high-rated sellers longer auctions achieve higher price levels throughout the auction, and especially at the start and end. Our methodological contributions include the introduction of functional data analysis as a viable toolkit for exploring the structural characteristics of electronic markets.