The Impact of Information on the Performance of an M/M/1 Queueing System

Abstract

Reviews provided by previous customers contain information, which can be used by new customers. This research examines the impact of the user-generated reviews, on the performance of an M/M/1 queueing system. We assume that customers do not know the expected service time and they obtain this information by reading reviews. The results show that reading unbiased reviews can result in either a better or worse performance, depending on the parameters of the system. We also investigate the impact of the number of reviews each customer reads, on the different performance measures. We observe that if each customer reads more reviews, it does not necessarily result in a system which is more similar to a system with full information. Moreover, even with a huge pool of reviews, it may either not converge to the system with full information or converges very slowly. Finally, we show that if reviews consist of the waiting time that customers experience in the system along with the number of people that they observe upon their arrival, the rate of convergence to the system with full information is much faster.