Agent technology has been widely applied in today's electronic business, such as mobile agents, multi-agent information systems, etc. In particular, multi-agent systems have been applied as powerful simulation tools to study complex business networks composed of various self-interested trading firms and/or human beings. In this paper, we build an integrated model that consists of a multi-agent B2C market model and a B2B trade network model, and incorporate more reality than much of prior work. Then with this model, we carry out experimental studies on two different strategies that are common in electronic business - 'loyal' strategy (retailers try to build stable cooperation with suppiers to ensure material supply) and 'cost-saving' strategy (retailers try to reduce cost by choosing suppliers with lower wholesale price).

Collaborative filtering is one of the most common methods that e-commerce sites and Internet information services use to personalize recommendations. Collaborative filtering has the advantage of being able to use even sparse evaluation data to predict preference scores for new products. To date, however, no in-depth investigation has been conducted on how the data sparsity effect in customers' evaluation data affects collaborative filtering-based recommendation performance. In this study, we analyzed the sparsity effect and used a hybrid method based on customers' evaluations and purchases collected from an online bookstore. Results indicated that recommendation performance decreased monotonically as sparsity increased, and that performance was more sensitive to sparsity in evaluation data rather than in purchase data. Results also indicated that the hybrid use of two different types of data (customers' evaluations and purchases) helped to improve the recommendation performance when evaluation data were highly sparse.

Well-known e-CRM strategy is to focus on profitable customers and pay less attention to unprofitable ones. Moreover, some researchers recommend not serving unprofitable ones any more. However, it often neglects customers indirect value. Deselecting unprofitable customers can raise the issue of bad word-of-mouth publicity especially in the age of the Internet. Some studies pointed out that a customers decision to buy a product or service is often strongly influenced by others. In this paper, we consider customers' word-of-mouth effect on quality learning of inexperienced customers. We show that firms implementing e-CRM must take the effect into the consideration when deselecting unprofitable customers.

The aim of this paper is to identify the barriers to the B2B e-commerce development in China's pharmaceutical industry and to devise an effective strategy for its future development. Built on a detailed investigation of the market structure and recent development of electronic commerce in China's pharmaceutical industry, this paper proposes that the key issue in the development of effective B2B e-commerce business models is the successful transformation of the inter- organizational information systems into electronic marketplaces. In order to ensure the success of such electronic marketplaces, a government driven approach will be needed. In the process, designing an incentive compatible mechanism of coordinating the interest of all the market players will be the prerequisite.

Despite the fact that the sourcing process in B2B e-Marketplaces is one of the most important tasks, the evaluation and selection process of suppliers have been ad-hoc based and mainly dependent on the experience of sourcing managers' subjective knowledge. To remedy the problem, we developed a decision support System (called Wise - I) that helps sourcing managers evaluate suppliers in a more systematic way. The system reflects company's strategy and know-how by adopting company enforced weighted scores for different factors and employing a more scientific method of considering factors other than price and on-time delivery rate, utilizing the AHP method. This paper reports the effectiveness of the system as well as the detailed description of the system. To investigate the effectiveness of the system, we collected information through interview and questionnaire survey. The information was also augmented through the firm key index system, which monitors average delivery lead time and on-time delivery rate. The result indicates that the system leads to the efficiency of purchasing section and the transparency of buying process, therefore reduces delivery time and cost.

As online shopping malls have emerged as a substantial shopping channel, they have used various sales promotion strategies to acquire new customers. Most of these strategies have been applied by offline malls for years. One, loss-leader pricing, is a type of promotional pricing in which stores sell well known products below their marginal cost, in order to attract customers and induce them to purchase more goods through impulse buying. This strategy is based on the expectation that customers will factor transaction costs into their purchasing decisions. However, its application to online malls fails to recognize that transaction costs are lower online, and that customers will behave differently as a result. Our study predicts that loss-leader pricing will not work online because online malls entail lower searching and moving costs than offline malls The study examines the effectiveness of loss-leader pricing with empirical data from a survey as well as log data from a Korean online shopping mall. The results show that while loss-leader pricing does attract customers to online shopping malls, it encourages cherry-picking rather than impulse purchases of regular-price goods.

The study on the critical success factors (CSF) for electronic commerce systems has been a hot topic in both academe and industry. On the basis of reviewing papers on CSF and analyzing their problems, this paper designs the initial assessment indictors and website features and functions influencing EC success. Using Delphi survey and data analysis, we get the five important assessment indictors and seven important web features and functions. Furthermore, the hypothesis of CSF model is proposed. Finally, we conduct a survey on the Chinese Publishing Industry to test the hypothesis. The result shows that the hypothesis is partly supported, which means useful and understandable information, complete and timely information, credible and accurate information, all product-related information are the critical success factors for EC publishing industry. This research not only impels EC research in China, but also has instructional effect on the implementation of EC for enterprises to increase the success rate of EC.

As the importance of time-based competition is increasing, information systems for supporting the immediate decision making is strongly required. Especially high -tech product firms are under extreme pressure of rapid response to the demand side due to relatively short life cycle of the product. Therefore, the objective of our research is proposing a framework of estimating demand curve based on e-auction data, which is extremely easy to access and well reflect the limited demand curve in that channel. Firstly, we identify the advantages of using e-auction data for full demand curve estimation and then verify it using Agent-Eased-Modeling and Tobin's censored regression model.

Collaborative filtering-based recommendation systems make personalized recommendations based on users' ratings on products. Recommender systems must collect sufficient rating information from users to provide relevant recommendations because less user rating information results in poorer performance of recommender systems. To learn about new users, recommendation systems must first present users with an initial item list. In this study, we designed and analyzed seven selection strategies including the popularity, favorite, clustering, genre, and entropy methods. We investigated how these strategies performed using MovieLens, a public dataset. While the favorite and popularity methods tended to produce the highest average score and greatest average number of ratings, respectively, a hybrid of both favorite and popularity methods or a hybrid of demographic, favorite, and popularity methods also performed within acceptable ranges for both rating scores and numbers of ratings.