With the Internet connecting every part of the world, electronic marketplaces provide a world-wide transaction platform for global businesses. However, consumers from different countries demonstrate distinct purchasin...With the Internet connecting every part of the world, electronic marketplaces provide a world-wide transaction platform for global businesses. However, consumers from different countries demonstrate distinct purchasing behaviors online. This study uses eBay as the consumer-to-consumer electronic marketplace to investigate the eBay mechanisms and purchasing habits of consumers from the United States, France, and China to evaluate the effects of cultural and national differences on purchasing habits. This study uses data on national transactions and reputation profiles of sellers gathered from eBay.com, eBay.fr, and eBay.com.cn, including both cross-sectional data and panel data to provide a comprehensive comparison of cultural behavior in the trading process as well as the influence of eBay＇s reputation management system on online pricing in these three countries. Significant differences confirm that purchasing behavior differs in various national consumer-to-consumer electronic marketplaces and the importance of cultural behavioral patterns.展开更多
In a distributed eMarketplace, recommended product ontologies are required for trading between buyers and sellers. Conceptual clustering can be employed to build dynamic recommended product ontologies. Traditional met...In a distributed eMarketplace, recommended product ontologies are required for trading between buyers and sellers. Conceptual clustering can be employed to build dynamic recommended product ontologies. Traditional methods of conceptual clustering (e.g. COBWEB or Cluster/2) do not take heterogeneous attributes of a concept into account. Moreover, the result of these methods is clusters other than recommended concepts. A center recommendation clustering algorithm is provided. According to the values of heterogeneous attributes, recommended product names can be selected at the clusters, which are produced by this algorithm. This algorithm can also create the hierarchical relations between product names. The definitions of product names given by all participants are collected in a distributed eMarketplace. Recommended product ontologies are built. These ontologies include relations and definitions of product names, which come from different participants in the distributed eMarketplace. Finally a case is given to illustrate this method. The result shows that this method is feasible.展开更多
基金Supported by the National Natural Science Foundation of China(No. 70872059)
文摘With the Internet connecting every part of the world, electronic marketplaces provide a world-wide transaction platform for global businesses. However, consumers from different countries demonstrate distinct purchasing behaviors online. This study uses eBay as the consumer-to-consumer electronic marketplace to investigate the eBay mechanisms and purchasing habits of consumers from the United States, France, and China to evaluate the effects of cultural and national differences on purchasing habits. This study uses data on national transactions and reputation profiles of sellers gathered from eBay.com, eBay.fr, and eBay.com.cn, including both cross-sectional data and panel data to provide a comprehensive comparison of cultural behavior in the trading process as well as the influence of eBay＇s reputation management system on online pricing in these three countries. Significant differences confirm that purchasing behavior differs in various national consumer-to-consumer electronic marketplaces and the importance of cultural behavioral patterns.
文摘In a distributed eMarketplace, recommended product ontologies are required for trading between buyers and sellers. Conceptual clustering can be employed to build dynamic recommended product ontologies. Traditional methods of conceptual clustering (e.g. COBWEB or Cluster/2) do not take heterogeneous attributes of a concept into account. Moreover, the result of these methods is clusters other than recommended concepts. A center recommendation clustering algorithm is provided. According to the values of heterogeneous attributes, recommended product names can be selected at the clusters, which are produced by this algorithm. This algorithm can also create the hierarchical relations between product names. The definitions of product names given by all participants are collected in a distributed eMarketplace. Recommended product ontologies are built. These ontologies include relations and definitions of product names, which come from different participants in the distributed eMarketplace. Finally a case is given to illustrate this method. The result shows that this method is feasible.
