【主讲】张中举美国康涅狄格大学商学院信息系统方向助理教授

【主题】在线零售价格差异化的竞争模型

【时间】2007-6-15 10:00-12:00

【地点】伟伦楼401

【语言】中文

【主办】管理科学与工程系

【目标听众】

【简介】

Zhongju (John) Zhang is an Assistant Professor of Information Systems in theSchoolofBusiness,UniversityofConnecticut. He received his Ph.D. in Management Science (with minors in Economics and Operations Management) from University of Washington Business School. He also holds a B.Sc. (with honors) in Computer Science from Xi’an Jiaotong University and a M.Sc. in Computer Science & Information Systems from National University of Singapore.

Zhang's main research area is in the area of e-business/e-commerce. His research interests include pricing of data communication services, economics of information technology/systems, product differentiation, technology adoption and diffusion, data warehousing and data mining. His research has been published in INFORMS Journal on Computing, European Journal of Operational Research, International Journal on Human Computer Studies, Communications of the ACM, Electronic Commerce Research and Applications, as well as in leading international conference proceedings. He currently serves on the editorial board of Journal of Database Management.

Abstract The Internet has changed the nature of doing business as well as the nature of competition in many industries. Consumers are more empowered than ever with valuable information such as prices, products, and store ratings. Because of this, some researchers even predicted, during the early stage of e-commerce, a "frictionless economy" in which online prices would be driven down to marginal costs. However, many studies have subsequently observed the wide price dispersion online, and its existence and persistence has now been well documented. Possible explanations of this price dispersion, derived mainly using hedonic price models, have seen only modest success. In this paper, we propose an alternative competitive model, based on online retailers' differentiation, to explain price dispersion. We empirically test the predictions of this model and find that the model is a viable alternative to the hedonic price model. In addition, our competitive model is able to predict and explain observations that are seemingly inconsistent with a hedonic model. Practically, our model yields important recommendations for the online retailing industry and can help an e-tailer to choose a desirable position in the competitive market.