Liangjun You

Coppin State University, Management Information Systems, Faculty Member

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Liangjun You is associate professor of Information Systems in the College of Business, Coppin State University. He received his PhD from the University of Texas at Arlington. His research interests include intelligent agent systems, data mining, computer and information security, software development methodologies and teaching/learning using technology. Dr. You’s publications include articles and book chapters in the European Journal of Operational Research, Journal of Organizational Computing and Electronic Commerce, Information Systems and e-Business Management, Journal of Informatics Education and Research, Strategic Advantage of Computing Information Systems in Enterprise Management, etc.

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Jomo Kenyatta University of Agriculture and Technology

Syracuse University

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Papers by Liangjun You

An Adaptive Supplier Selection Mechanism in E-Procurement Marketplace

Journal of International Technology and Information Management, 2017

B2B e-commerce has fundamentally changed the way in which an organization purchases goods/service... more B2B e-commerce has fundamentally changed the way in which an organization purchases goods/service. Nowadays, the adoption of e-procurement, which means the electronic acquisition of goods/service, has been prevalent in supply chain management. A variety of supplier selection models have been developed in supply chain management literature. In this research, an adaptive supplier selection mechanism is proposed to help buyers evaluate suppliers in an e-marketplace. A Multi-Agent System simulation package of Repast is used to create a realistic environment where different kinds of suppliers and buyers equipped with the proposed selection model can interact so as to study the performance of the proposed selection model. We evaluate three supplier selection models and find that our proposed model outperforms the other two in terms of robustness and performance. Journal of International Technology and Information Management Volume 26, Number 2 2017 ©International Information Management As...

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An adaptive reputation-based trust model for intelligent agents in e-marketplace

In the emerging Electronic Marketplace, intelligent agents that are reactive, proactive and socia... more In the emerging Electronic Marketplace, intelligent agents that are reactive, proactive and social will become the major players. These intelligent agents will search for good deals, evaluate trading partners and eventually provide advice on or even make decisions on transactions for their principals. To this end, it is vitally important for the agents to be able to precisely estimate the trust of the business partners to make successful deals. This dissertation proposes an adaptive reputation-based trust model which can be utilized by intelligent agents in an open Electronic Marketplace, where the seller agents and buyer agents can enter and leave the market freely and the service quality and price of goods vary. The trust model is based on an agent's reputation history, witness testimony, and other weighting factors. Learning is integrated to make the trust model adaptive and robust in a dynamic environment. To verify the proposed adaptive reputation-based trust model and the ...

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An adaptive evaluation mechanism for online traders

Economic agents in electronic markets generally consider reputation to be a significant factor in... more Economic agents in electronic markets generally consider reputation to be a significant factor in selecting trading partners. Most traditional online businesses publish reputation profile for traders that reflect average of the ratings received in previous transactions. Because of the importance of these ratings, there is an incentive for traders to partake in strategic behavior (for example shilling) to artificially inflate their rating. It is therefore important for an online business to be able to provide a robust estimate of a trader's reputation that is not easily affected by strategic behavior or noisy ratings. This paper proposes such an adaptive ratings-based reputation model. The model is based on a trader's transaction history, witness testimony, and other weighting factors. Learning is integrated to make the ratings model adaptive and robust in a dynamic environment. To validate the proposed model and to demonstrate the significance of its constructs, a multi-agent system is built to simulate the interactions among buyers and sellers in an electronic marketplace. The performance of the proposed model is compared to that of the reputation model used in most online marketplaces like Amazon, and to Huynh's model proposed in the literature.

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Performance of online reputation mechanisms under the influence of different types of biases

Information Systems and e-Business Management, 2013

ABSTRACT Rating mechanisms are becoming an essential tool for online shoppers. There have been ma... more ABSTRACT Rating mechanisms are becoming an essential tool for online shoppers. There have been many reputation mechanisms proposed in the literature that try to mitigate the sometimes adverse effects of strategic behavior of the traders to alter their true reputation scores. At the same time, several studies have shown the presence of bias (often unintentional) in product ratings and their harmful effect on buyers and product sales. To date there has not been any work done that studies the performance of reputation mechanisms in the presence of various types of biases. This study is a first attempt to fill that gap. We present three reputation mechanism models that have been discussed in the literature, discuss three types of biases that have been studied in the past, and study the performance of the three reputation mechanisms in the presence of these biases by using a multi-agent system that simulates the interactions among buyers and sellers in an electronic marketplace. Using replicator dynamics we also show the effect of these models on the behavior of buyers and sellers as the buyers choose the type of reputation models they prefer to use and the sellers change their strategic behavior in response.

Effect of Reputation Mechanisms and Ratings Biases on Traders’ Behavior in Online Marketplaces

Journal of Organizational Computing and Electronic Commerce, 2014

The accuracy of the Content should not be relied upon and should be independently verified with p... more The accuracy of the Content should not be relied upon and should be independently verified with primary sources of information. Taylor and Francis shall not be liable for any losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoever or howsoever caused arising directly or indirectly in connection with, in relation to or arising out of the use of the Content. This article may be used for research, teaching, and private study purposes. Any substantial or systematic reproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form to anyone is expressly forbidden.

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An Adaptive Supplier Selection Mechanism in E-Procurement Marketplace

Journal of International Technology and Information Management, 2017

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An adaptive reputation-based trust model for intelligent agents in e-marketplace

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An adaptive evaluation mechanism for online traders

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Performance of online reputation mechanisms under the influence of different types of biases

Information Systems and e-Business Management, 2013

Effect of Reputation Mechanisms and Ratings Biases on Traders’ Behavior in Online Marketplaces

Journal of Organizational Computing and Electronic Commerce, 2014

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