Time：2017.11.6  14:00—15:00 p.m

Venue：Room 1004

Speaker：Yimin  Yu, City University of Hong Kong

Host：Wei Zhang,School of Management ZJU

Topic: Optimal Dynamic Salesforce Compensation with Bayesian Learning

Abstract：

We consider agent ability or market condition uncertainty in sales force compensation. We build a simple dynamic model and study optimal dynamic contracting with Bayesian learning. Under a dynamic setting,the agent's shirking on one hand potentially reduces the current output, on the other hand increases expectedagent ability/market condition due to the belief calibration under the Bayesian rule. Interestingly, we find that to motivate the agent to exert high effort, the firm is required to pay more bonus aggregately to theagent for high output if the volatility of the prior belief is higher. The belief calibration leads to the following inter-temporal balance effect: the firm should encourage the agent to be productive by rewarding low (high) output more (less) generously if her last period output is low (high) as well. Essentially, the sales growth based compensation is recommended. Moreover, with agent ability uncertainty, the volatility of the priorbelief of the last period has opposite effects on the bonus of the current period high output: if the last periodoutput is high, then the bonus is increasing in the volatility; vice versa. We also provide managerial and policy implications of our results. 

About the speaker：

Yimin Yu is an assistant professor of Management Sciences at City University of Hong Kong. He received his PhD degree in Industrial Engineering from the University of Minnesota, Twin Cities. He conducts research in different areas of operations management, with an emphasis on inventory management, revenue management, and the marketing-operations interface. His papers have been published in journals such as Marketing Science, Production and Operations Management, etc.