Reputation based Buyer Strategies for Seller Selection in Electronic Markets

Reputation based adaptive buying agents that reason about sellers for purchase decisions have been designed for B2C ecommerce markets. Previous research in the area of buyer agent strategies for choosing seller agents in ecommerce markets has focused on frequent purchases. In this thesis, we present reputation based strategies for buyer agents to choose seller agents in a decentralized multi agent based ecommerce markets for frequent as well as infrequent purchases. We consider a marketplace where the behavior of seller agents and buyer agents can vary, they can enter and leave the market any time, they may be dishonest, and quality of the product can be gauged after actually receiving the product. Buyer agents exchange seller agents' information, which is based on their own experiences, with other buyer agents in the market. However, there is no guarantee that when other buyer agents provide information, they are truthful or share similar opinions. First we present a method for buyer agent to model a seller agent's reputation. The buyer agent computes a seller agent's reputation based on its ability to meet its expectations of product quality and price as compared to its competitors. We show that a buying agent acting alone, utilizing our model of maintaining seller agents' reputation and buying strategy does better than buying agents acting alone employing strategies proposed previously by other researchers for frequent as well as for infrequent purchases. Next we present two methods for buyer agents to identify other trustworthy buyer agent friends who are honest and have similar opinions regarding seller agents, based on sharing of seller agents' information with each other. In the first method, buyer agent utilizes other buyer agents' opinions and ratings of seller agents to identify trustworthy buyer agent friends. Reputation of seller agents provided by trustworthy buyer agent friends is adjusted to account for the differences in the rating systems and combined with its own information on seller agents to choose high quality, low priced seller agent. In the second method, buyer agent only utilizes other buyer agents' opinions of seller agents to identify trustworthy buyer agent friends. Ratings are assigned to seller agents by the buyer agent based on trustworthy friend buyer agents' opinions and combined with its own rating on seller agents to choose a high quality, low priced seller agent to purchase from. We conducted experiments to show that both methods are successful in distinguishing between trustworthy buyer agent friends, whose opinions should be utilized in decision making, and untrustworthy buyer agent friends who are either dishonest, or have different opinions. We also show that buyer agents using our models of identifying trustworthy buyer agent friends have higher performance than a buyer agent acting alone for infrequent purchases and for increasing numbers of sellers in the market. Finally we analyze the performances of buyer agents with risk taking and conservative attitudes. A buyer agent with risk taking attitude considers a new seller agent as reputable initially and tends to purchase from a new seller agent if they are offering the lowest price among reputable seller agents. A buyer agent with conservative attitude is cautious in its approach and explores new seller agents at a rate proportional to the ratio of unexplored seller agents to the all the seller agents who have sent bids. Our results show that, when buyer agents are making decisions based on their own information, a buyer agent with conservative attitude has the best performance. When buyer agents are utilizing information provided by their trusted friends, a buyer agent with risk taking attitude and using only trusted friend buyer agents' opinions of seller agents has the best performance. In summary, the main contributions of this dissertation are: 1.A new reputation based way to model seller agents by buyer agents based on direct interactions. 2.A protocol to exchange reputation information about seller agents with other buyer agent friends based on the friends' direct interaction with seller agents. 3.Two methods of identifying trustworthy buyer agent friends who are honest and share similar opinions, and utilizing the information provided by them to maximize a buyer agent's chances of choosing a high quality, low priced seller agent to purchase from

Agent Based E-Market: Framework, Design, and Implementation

Attempt has been made to design and develop a complete adoptive Multi Agent System pertaining to merchant brokering stage of Customer Buying Behaviour Model with the intent of appropriate framework. Intelligent agents are autonomous entity which observe and act upon an environment. In general, they are software robots and vitally used in variety of e-Business applications. This paper focuses on the discussions on electronic markets and the adoptive role, which agents can play in information transformation for automating e-market transactions. It is proposed to develop a framework for agent-based electronic markets for buyers and sellers totally with the assistance of software agents.Agent Oriented e-Business, Agent Oriented e-Markets, Buyer/Seller Agents, Java, Multi Agent Systems

The Dimensions of Reputation in Electronic Markets

We analyze how di erent dimensions of a seller's reputation a ect pricing power in electronic markets. We do so by using text mining techniques to identify and structure dimensions of importance from feedback posted on reputation systems, by aggregating and scoring these dimensions based on the sentiment they contain, and using them to estimate a series of econometric models associating reputation with price premiums. We nd that di erent dimensions do indeed a ect pricing power di erentially, and that a negative reputation hurts more than a positive one helps on some dimensions but not on others. We provide the rst evidence that sellers of identical products in electronic markets di erentiate themselves based on a distinguishing dimension of strength, and that buyers vary in the relative importance they place on di erent ful lment characteristics. We highlight the importance of textual reputation feedback further by demonstrating it substantially improves the performance of a classi er we have trained to predict future sales. This paper is the rst study that integrates econometric, text mining and predictive modeling techniques toward a more complete analysis of the information captured by reputation systems, and it presents new evidence of the importance of their e ective and judicious design.NYU, Stern School of Business, IOMS Department, Center for Digital Economy Researc

The Dimensions of Reputation in Electronic Markets

We present a framework for identifying the different dimensions of online reputation and characterizing their influence on the pricing power of sellers. Our theory predicts that sellers with better recorded online reputation can successfully charge higher prices than competing sellers of identical products, and that their pricing power increases with their recorded level of experience. We develop and implement a new text mining technique that identities and quantitatively assesses dimensions of importance in reputation profiles, and use this technique to create a new data set containing detailed reputation profiles and prices for sellers in over 9,500 transactions for consumer software on Amazon.com's online secondary marketplace. The estimation of a set of econometric models on this data set validates the predictions of our theory, and further, ranks these dimensions of reputation based on their effect on measured seller value, identifying those that have the most significant impact on reputation. This paper is the first study that integrates econometric and text mining techniques toward a more complete analysis of the information captured by reputation systems, and it presents new evidence of the importance of their effective and judicious design.Information Systems Working Papers Serie

Exploring Information Disclosure In Online Auctions

This research examines how a seller’s reputation score and auction pre-configuration affects people’s participation in communication within online auction communities. A leading horizontal intermediary auction platform is used to conduct this research. Its seller “feedback” mechanism and “ask seller a question” forum are chosen as representatives of post- and intra-transactional information disclosure. A self-developed classification approach is used to classify the buyer-initiated questions. The results of multinomial logistic regression indicate that product quality, shipment and payment issues are aspects that concern buyers the most in the early stages of an auction. Subsequently, their attention is likely to shift to seller credibility and price negotiations as listing durations get longer. In terms of the influ- ence of seller feedback ratings, our findings suggest that lower-rated traders are more likely to be asked questions about product description and seller credibility. Buyer concern about seller uncertainty is only alleviated if the seller has a good reputation. Even medium-rated sellers are suspected of being opportunistic. Moreover, buyers are more willing to discuss transaction-related issues and raise negotiation-associated questions with sellers who have already achieved high reputation scores. Finally, the theoretical and managerial implications are elaborated

Factors affecting the adoption of online auctions by internet users in Hong Kong

This is an exploratory empirical study with the aim to identify the factors that affect the adoption of online auctions by Internet users in Hong Kong. The frameworks used were the TAM (Technology Acceptance Model), TCE (Transaction Cost Economics) and SERVQUAL (Service Quality). It was found that the dimensions that affected the customer’s perceived value of the online auction are benefits, costs, risks and service quality. Data was collected from four pilot focus groups, one online survey and a final focus group. The subjects in the focus groups were 21 undergraduates, whereas the subjects in the online survey were 152 internet users. The results of the pilot focus groups guided the design of the online survey. The results of the survey was analysed using the Kruskal-Wallis test. The final focus group was used to seek explanations to some issues arose from the online survey. It was found that the factors in the benefit dimension were liquidity, enjoyment, and price transparency. The factors in the cost dimension were time, effort, service charge and reputation of the user. The factor in the risk dimension was financial risk. The factors in the service quality dimension were efficiency and system availability. The final focus group revealed that the auctioneer’s role in policing the auction web site was important. For differences among the subjects, it was also found that the adult users consider their reputation in auction website, young adults are worried about financial risks, and female users are more concerned about financial risks than male users. The implications of these differences are discussed. The main academic contribution was the development of a questionnaire and a model which can be used in further research about other forms of auction

Systematic Measurement of Centralized Online Reputation Systems

Background: Centralized online reputation systems, which collect users' opinions on products, transactions and events as reputation information then aggregate and publish it, have been widely adopted by Internet companies. These systems can help users build trust, reduce information asymmetry and lter information. Aim: Much research in the area has focused on analyzing single type systems and the cross-type evaluation usually concentrates on one aspect of the system. This research proposes a systematic evaluation model (SERS) that can measure different types of reputation system. Method: From system perspective, all reputation systems can be divided into five underlying components. Input refers to the collection of ratings and reviews; Processing is the aggregation of ratings. Output publishes the information. Feedback Loop is the collection of the feedback of the review, which can be seen as the `review of the review'; Finally, Storage stores all the information. Therefore, based on each component's characteristics, a series of benchmark criteria can be dened and incorporated into the model. Results: The SERS has dened 29 criteria, which can compare and measure different aspects of reputation systems. The model was theoretically assessed on its coverage of the successful factors of reputation systems and the technical dimensions of information systems. The model has also been empirically assessed by applying it to 15 commercial sites. Conclusion: The results obtained indicated that the SERS model has identified most important characteristics that have been proposed by reputation systems literature. In addition the SERS has covered most dimensions of the two basic technical information system measurements: information quality and system quality. The empirical assessment has shown that the SERS can evaluate dierent types of reputation systems and is capable of identifying the weakness of current systems

RECIPROCITY IN ONLINE MARKETS: EMPIRICAL STUDIES OF AUCTION AND BARTER MARKETS

My dissertation seeks to understand how reciprocity affects transaction outcomes and mechanism design in online markets. The first essay examines negative reciprocity illustrated as feedback-revoking behavior in the eBay auction market, focusing on its impact and implications for reputation system design. I utilize the biggest policy change of eBay's reputation system in its history as a natural experiment setting to infer the causal impact of the reputation system on seller behavior. I find that strategic engagement in negative reciprocity enables low quality sellers to manipulate their reputations and masquerade as high-quality sellers. I further show that these sellers react strongly to eBay's announcement of a ban on revoking. Interestingly, disallowing negative reciprocity motivates these sellers to significantly improve their service quality. The second essay examines positive reciprocity in one of the leading online barter markets for books, focusing on participants' different reciprocity strategies and their impacts on transaction outcomes. I find that, whereas market participants who use the immediate reciprocity strategy are able to motivate higher service quality for the current transaction from the other partner, participants who use the delayed reciprocity strategy derive more benefits for future transactions by fulfilling their wishlists sooner. I further show that the market participants can be segmented into different reciprocity strategies based on their book avidness, breadth of interest, and psychographic profiles. Overall, the two studies provide important theoretical and practical implications for the design and regulation of online markets

Web Elements and Strategies for Success in Online Marketplaces: An Exploratory Analysis

Among the most prominent and fastest-growing markets on the Internet are online marketplaces. The leader and main exemplar of this type of market is eBay. In this paper, we provide a comprehensive examination of the salient website elements and strategies as success factor in online marketplaces. In this exploratory analysis, we report on the behavior of different types of sellers and their distinct approaches for achieving their desired goals. The conceptual framework for this examination is based on marketing mix theory and its synthesis with competitive heterogeneity theory, allowing us to formulate a success model for sellers operating in this market. The conceptual model is empirically tested by the random collection of over 2000 auction listings from eBay’s Motors Division spread over a period of six months. Our results bring to light the presence of different types of sellers in this market, and the differences in website designs and strategies they use for success in this market