主管:中国科学院
主办:中国优选法统筹法与经济数学研究会
   中国科学院科技战略咨询研究院

Chinese Journal of Management Science ›› 2024, Vol. 32 ›› Issue (10): 123-132.doi: 10.16381/j.cnki.issn1003-207x.2021.2127

Previous Articles     Next Articles

Information Heterogeneity in a Queue: Pricing Decision in Waiting Service Systems with Word-of-mouth

Tao Jiang1(),Li Gao1,Lu Liu1,Xudong Chai2   

  1. 1.College of Economics and Management, Shandong University of Science and Technology, Qingdao 266590, China
    2.School of Mathematics-Physics and Finance, Anhui Polytechnic University, Wuhu 241000, China
  • Received:2021-10-18 Revised:2022-06-20 Online:2024-10-25 Published:2024-11-09
  • Contact: Tao Jiang E-mail:jtao0728@163.com

Abstract:

In today's information service society, there are various channels for customers to obtain service information. On one hand, some customers can easily grasp service information and make rational decisions according to relevant service information. On the other hand, due to the influence of various factors, it is difficult for some customers to obtain service information. In order to avoid blind decision, they can use the word of mouth (WOM) information to confirm whether they can trust such service, that is, WOM information can provide more reliable guarantee for their decision making.On the basis of this phenomenon, in this paper, it aims to deal with the related following questions: Q1. When the queue information obtained is heterogeneous, how do the two types of delay-sensitive customers make their own queuing decisions, how do the decisions of non-member customers affect the member customers’ equilibrium queuing strategies? Q2. As the market size of member customers changes, how should the service provider make the optimal pricing decision to maximize his revenue without changing service capacity? Q3. As non-member customers collect more WOM information, how should the service provider adjust the optimal service price?To address these research questions, this paper considers a non-preemptive M/M/1 priority queuing model, which includes two types of customers, namely, member and non-member customers. The service information obtained by the two types of customers is heterogeneous, where member customers can obtain sufficient service-related information and make rational decisions, while non-member customers have no relevant service information and can only rely on WOM ways to make decisions, such as interpersonal channels or service rating websites, that is, they can obtain relevant experience information from relatives and friends who have purchased service, or use review websites to collect relevant rating information on the service value to know the service. The equilibrium strategic behavior of member customers is described and the influence of the number of ratings on the service value obtained by non-member customers on the queuing behavior of member customers is studied. Then the revenue function of service provider is constructed and the optimal service strategy of service provider is obtained.The main results show that, first, the decision-making behavior of non-member customers affects the queuing strategy of member customers as well as the optimal service pricing decision of the service provider. Therefore, it is very important to grasp the decision-making behavior of non-member customers to maximize the revenue of the service provider. Second, the smaller market size of member customers may improve the optimal pricing of service provider. In addition, when the market size of member customers is large, the number of WOM information and the market size of member customers have a weak impact on the optimal service pricing. Third, when the market size of member customers exceeds a certain value, and the service rate of member customers is greater than that of non-member customers, then the high overall rating (low overall rating) provider should decrease (increase) the service price as the number of rating on the service value received by non-member customers increases, and vice versa.

Key words: information heterogeneity, word of mouth information, waiting service systems, service pricing, optimization

CLC Number: