报告人简介：

姜正瑞(ZHENGRUI JIANG)，管理科学博士，美国爱荷华州立大学（Iowa State University）商学院的信息系统与商业分析教授和Thome讲席教授。在国际顶尖期刊如 Management Science, MIS Quarterly, Information Systems Research, IEEE Transactions on Knowledge and Data Engineering 等发表十多篇论文。主持过多个国际和地区性的学术会议，作为项目主持人和主要参与人受到国家自然科学基金和美国国际开发署的资助。 

报告简介：

In the presence of successive product generations, most customers are repeat buyers, who may decide to purchase a future product generation before its release. As a result, after the new product generation enters the market, its sales often exhibit a declining pattern, thus rendering traditional diffusion models unsuitable for characterizing customers’ time to product upgrades. In this study, we propose an Exponential-Decay proportional hazard model (Expo-Decay model) to predict customers’ time to product upgrade. Compared with existing proportional hazard models, the Expo-Decay model is parsimonious and easy to interpret. In addition, our empirical test shows that the Expo-Decay model performs better than or as well as existing parametric models in prediction accuracy. Furthermore, we develop and test three extensions of the Expo-Decay model. We apply the Expo-Decay model as well as the three extensions to study customers’ update behaviors for a sports video game series produced by a major U.S. firm. Empirical results reveal that customers’ previous adoption and usage patterns can help predict their timing to upgrade to a new product generation.