题目：Optimizing Two-Sided Promotion for Transportation Network Companies:A Structural Model with Conditional Bayesian Learning
主讲人：谭勇 教授 （美国华盛顿大学）
时间：2017年6月13日 （星期二）上午10:00
地点：主楼317会议室
主讲人简介：
Yong Tan is the Neal and Jan Dempsey Professor of Information Systems at the Michael G. Foster School of Business, University of Washington, the Chang Jiang Scholar Visiting Chair at the School of Economics and Management, Tsinghua University, a Distinguished Fellow of the INFORMS Information Systems Society, and the Associate Director of the USTC-UW Institute for Global Business and Finance Innovation. He received his Ph.D. in Physics (advised by 2016 Nobel Laureate Professor David J. Thouless) and Ph.D. in Business Administration, both from the University of Washington. He was a postdoctoral fellow at the University of Strathclyde and a visiting scientist at the Laboratoire de Physique Quantique, Université Paul Sabatier. His research interests include electronic, mobile and social commerce, big data, economics of information systems, social and economic networks, and health IT. He has published in Management Science, Information Systems Research, Operations Research, Management Information Systems Quarterly, Journal of Management Information Systems, INFORMS Journal on Computing, IEEE/ACM Transactions on Networking, IEEE Transactions on Software Engineering, IEEE Transactions on Knowledge and Data Engineering, IIE Transactions, European Journal on Operations Research, Decision Support Systems, among others. He served as an associate editor of Information Systems Research and Management Science, and is now a senior editor of Information Systems Research and Journal of Electronic Commerce Research and on the board of editors of Journal of Management Information Systems. He was a co-chair of Conference on Information Systems and Technology (CIST 2010), the cluster chair of 2012 INFORMS Information Systems Cluster, a track co-chair of International Conference on Information Systems (ICIS 2013), and a co-chair of Workshop on Information Technologies and Systems (WITS 2014). He received Association for Information Systems (AIS) Best Publication of 2012 Award, 2012 Information Systems Research Best Paper Runner-Up Award, Andrew V. Smith Award for Excellence in Research, Dean’s Research Award, Dean’s Junior Research Award, Lex N. Gamble Family Award for Excellence in the Field of E-Commerce, Ph.D. Program Mentoring Award, Faculty Recognition Award for the Master of Science in Information Systems Program, Undergraduate Professor of the Year Award, and Management Science Meritorious Service Award. The doctoral students he advised are now on the faculty of top information systems programs such as Carnegie Mellon University, Purdue University, Indiana University, and Georgia State University.
内容介绍：
The mobile app of a transportation network company (TNC) allows the TNC platform to run aggressive and diverse two-sided sales promotions to help to introduce new products. We examine how two-sided sales promotion affects drivers’ willingness to use the TNC app and how the TNC develops its optimal promotion strategies accordingly. To investigate the effects of sales promotion, we estimate a structural model of drivers’ decisions to accept orders and to cancel generated orders and their perception of passengers’ willingness to utilize a sales promotion. Bayesian learning processes are introduced to account for decisions under uncertainty as the app is introduced. We find measurable evidence of drivers’ learning about the value of the attributes of the transportation network app. The results indicate that the substantial value of early promotion not only encourages current usage but also fosters learning that sustains drivers’ use of the app afterward. Our results also show that revealed tips from passengers signal low quality of service and that platform cashback to passengers has a positive effect on drivers by increasing drivers’ chances of being rewarded. Given the estimated parameters, we run simulations to explicitly measure the indirect effects of the sales promotion, as supported by learning, and show how cashback for passengers affects the decisions of drivers. Finally, our experimental promotion policies show improved performance with regard to drivers’ willingness to use the app as well as its cost effectiveness.
（承办：管理工程系，科研与学术交流中心）
