Queueing Models and Service Management

From Service Confirmation to Continuance Intention: An Expectation Confirmation Perspective on Customer-Perceived Relationship Management and Perceived Value in the Beauty Industry

Shu-Hui Chuang — 2026-09-01

Grounded in Expectation Confirmation Theory (ECT), this study develops and empirically tests an integrated model explaining customers’ continuance intention in the beauty service industry. Extending the traditional confirmation–satisfaction–continuance framework, the model incorporates customer-perceived relationship management (CPRM) and perceived value as relational and evaluative mechanisms linking service confirmation to sustained behavioral intention. Using survey data from 202 beauty service customers and structural equation modeling, the results indicate that service confirmation significantly enhances CPRM, perceived value, and service satisfaction. CPRM and perceived value further influence continuance intention both directly and indirectly through satisfaction. Service satisfaction emerges as a key mediator in translating confirmed service experiences into long-term patronage. By integrating relational and value-based perspectives into ECT, this study provides a comprehensive explanation of customer retention in high-contact service contexts. The findings offer theoretical contributions to service management research and managerial insights for strengthening customer retention strategies in experiential service industries.

Analytical, Simulation, and Learning-Based Approaches for Decision Support in Stochastic Service Systems: A Comparative Study

Zhe George Zhang — 2026-09-01

This paper presents a comparative analysis of three major methodologies for modeling stochastic service systems: analytical modeling (AM), simulation modeling (SM), and learning-based modeling (LM). While these approaches have been widely studied in isolation, relatively little research has systematically examined their respective roles and complementarities in supporting decision making for complex service systems. Using representative applications from international border-crossing operations and healthcare service systems, this study evaluates the strengths and limitations of the three methodologies in terms of modeling assumptions, interpretability, computational requirements, and predictive capability. The results show that analytical models provide valuable structural insights and prescriptive guidance under simplifying assumptions. Simulation models offer flexible and realistic representations of system dynamics for policy evaluation, and learning-based models deliver strong predictive performance in data-rich and highly dynamic environments. In addition to comparing these approaches, the paper proposes an integrated decision-support framework in which learning-based models generate short-term forecasts, analytical models design operational policies, and simulation models evaluate policy performance under realistic conditions. The findings highlight the complementary nature of these methodologies and provide practical guidance for selecting and integrating modeling approaches to support decision making in stochastic service systems.

A Queueing Model with Two Types of MAPs Service During the Positive Inventory Using (s,S) Inventory Policy Management Method, Vacation, Setup, and Balking

V. Ganesan — 2026-09-01

In this paper we consider a retrial inventory queueing model with two types of arrivals according to a Markovian arrival process (MAP), with a single-server retrial inventory, phase-type service during the positive inventory in all forms of services, balking, close down, single vacation, and use of the (s, S) inventory policy management method, where the reorder point (s) is set below the desired stock level (S) to ensure that inventory is replenished as needed. An arriving item when the server is busy will join an infinite capacity orbit or balks. The server closes the serving process, and goes on a limited vacation time, if there is no item in orbit and it is being idle. Upon return from vacation, the server will proceed with a set up period before proceeding to serve if there is any item waiting. Accordingly, we will find the steady-state probability vector, the busy period, performance measures, cost analysis, and finally, some numerical examples.