Managing Price and Service Rate in Customer-Intensive Services Under Social Interactions

Abstract

This paper investigates the price and service rate decisions in a customer-intensive service in an M/M/1 queue system under the influence of social interactions, where a higher value of the service is perceived if more customers purchase the service. The customer-intensive nature of the service requires a low service speed to maintain its quality, which may increase the congestion of the system. Two cases where customers are either homogeneous or heterogeneous in terms of the customer intensity are considered. It is found that social interactions can always benefit the service provider as more expected revenue can be achieved, and potential profits would be lost if the influence of social interactions is ignored. For the case with heterogeneous customers, the optimal price and service rate decisions are solved with or without considering social interaction effect. The study finds the proportions of high and low sensitive customers and the social interaction intensity are critical to the operational decisions and the market coverage strategies. These results offer a better understanding on the interplay between the quality-speed conundrum and the influence of social interactions in customers’ purchase behaviour in managing customer-intensive services.

Optimal Decision Making for Customer-Intensive Services Based on Queuing System Considering the Heterogeneity of Customer Advertising Perception

“…In this case, the service provider should change the service decision of serving all customers (L customers or H customers) as soon as possible, follow the relationship between the market demand and the upper limit of the service system, and make the service decision to serve a part of customers. This coincides with the conclusion of Li et al [12] on the study of the heterogeneous waiting cost model. This reflects that in customer-intensive services, when there are differences in customers, with the increase in total market demand, serving a part of customers can often have more benefits.…”

“…Based on the study of anxiety cost, Zhan et al [2] conclude through their study that advertisements of service providers can reduce customers' anxiety costs and tap more potential customers by increasing the visibility of service providers. Furthermore, since service time is inversely proportional to the service rate in customer-intensive services, changes in the service rate can affect the effectiveness of corresponding strategies formulated by service providers [11,12]. Li et al [13] assume that customers' purchase decisions are forwardlooking in customer-intensive services and find that customer heterogeneity would affect optimal decisions, market coverage, and expected returns.…”

“…Therefore, the number of customer categories is not the content of this study. Therefore, in order to simplify the analysis, and according to the classification of customer heterogeneity by Nazerzadeh and Randhawa [30] and Li et al [12], this paper assumes that there are two types of customers in the market. One type is customers who are highly sensitive to advertisements, called H customers, where their perception coefficient of advertisements is k H and their proportion is q; the other type is customers who are less sensitive to advertisements, called L customers, where their perception coefficient is k L and their proportion is (1 − q).…”

“…When 0 < Λ ≤ λL2 , it is situation S L1 . According to formula(12),µ * L1 = Λ + c β can be obtained, and µ * L1 is substituted into P L1 (µ) = V(µ) + k L a − c µ−Λ , so P * L1 (µ * ) = m + k L a − βΛ − 2 βc can be obtained. When λL2 < Λ < λL2q , it is situation S L2 .…”

Customer defection due to service elimination and post-elimination customer behavior: An empirical investigation in telecommunications

“…Accordingly, interaction intensity has implications for pricing. For customer-intensive services, customers' perceived value tends to increase with more social interactions, so service providers can charge higher prices or increase the speed of their provision, or both, which should increase their revenues (Li, Jiang, & Yuan, 2019). Through intense, personal contacts and interactions, competitive influences also might be mitigated, because customers enjoy the reduced ambiguity associated with competitive parameters such as price and quality (which is hard to evaluate) (Alvesson, 2001).…”