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![Uninformed customers’ PPE under different thresholds and degrees of loss aversion. Notes. In Fig. 1, R=10\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R=10$$\end{document} and μ=0.8\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mu =0.8$$\end{document} for the panel (a), R=10\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R=10$$\end{document} and μ=1.8\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mu =1.8$$\end{document} for the panel (b). The horizontal axis represents the threshold D and the vertical axis represents the degree α\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\alpha $$\end{document} of loss aversion. The yellow area represents that customers’ PPE is the mixed-strategy “joining with probability”. The blue area represents that customers’ PPE is the pure-strategy “balking”. The green area represents that customers’ PPE is the pure-strategy “joining”](publication/371510544/figure/fig1/AS:11431281190868246@1695521060558/Uninformed-customers-PPE-under-different-thresholds-and-degrees-of-loss-aversion-Notes.png)
Uninformed customers’ PPE under different thresholds and degrees of loss aversion. Notes. In Fig. 1, R=10\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R=10$$\end{document} and μ=0.8\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mu =0.8$$\end{document} for the panel (a), R=10\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R=10$$\end{document} and μ=1.8\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mu =1.8$$\end{document} for the panel (b). The horizontal axis represents the threshold D and the vertical axis represents the degree α\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\alpha $$\end{document} of loss aversion. The yellow area represents that customers’ PPE is the mixed-strategy “joining with probability”. The blue area represents that customers’ PPE is the pure-strategy “balking”. The green area represents that customers’ PPE is the pure-strategy “joining”
Source publication
In this paper, we incorporate loss preference into an M/M/1 queueing with a threshold disclosure policy and analyze its impact on the customers' queueing strategies and the queueing system's idle stationary probability. In the queueing system, customers are strategic and divided into two groups: the informed and the uninformed. Informed customers a...
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Citations
In social interaction, customers can observe the number of other customers receiving service and select queues with more customers. Simultaneously, customers in the queue anticipate waiting times and worry about utility loss. This study explores the impact of loss aversion psychology on customer queuing strategies, service provider pricing, and revenue in the context of social interaction. Firstly, we consider homogeneous customers and analyze the influence of loss aversion psychology on their queuing decisions and service choices in social interaction. Subsequently, we extend our investigation to heterogeneous customers, considering differences in customers’ sensitivity to social interaction. Social interaction and loss aversion are crucial in customer queuing decisions, affecting their perception of service utility and equilibrium decision-making. Social interaction and loss aversion also influence service providers’ revenue, necessitating tailored service pricing and strategies. This research provides profound insights into customer loss aversion behavior in the context of social interaction and offers practical service strategies for service providers.
In many service industries, information disclosure about the product can alleviate customers' loss aversion induced by uncertain product valuation. In this paper, we consider a single-server queueing system in which the manager who privately learns the valuation information discloses the valuation information strategically to loss-averse customers. We investigate the impact of the customers' loss aversion on the system's equilibrium arrival rate and the manager's optimal disclosure policy. We find that loss aversion restrains customers from joining the queue. Surprisingly, we find that there is no one disclosure policy that always prevails over other disclosure policies. Specifically, the full disclosure policy is optimal only when the valuation is large and the degree of loss aversion is moderate. The full non-disclosure policy is optimal when the degree of loss aversion is too large or too small, or the valuation is small. The threshold disclosure policy is optimal when the valuation and the degree of loss aversion are moderate. Furthermore, under the threshold disclosure policy, the increasing degree of loss aversion makes managers be more reluctant to disclose the valuation.
Purpose
Customers will develop a stronger desire to purchase when more people are waiting in line for service due to the herding effect. However, this also leads to longer queue times, causing customers to experience a waiting patience time. This study examines these two psychological aspects of delay-sensitive customers in service systems, considering both homogeneous and heterogeneous customer scenarios to explore the optimal pricing strategy for service providers.
Design/methodology/approach
Using queueing theory, we construct and optimally solve the customer's service utility function and the service provider's service revenue function. Further, the model is extended to account for heterogeneous customers, solving the utility and revenue functions accordingly.
Findings
Results show that service revenue increases with the intensity of herding behavior and the length of patience time. If customers have low herding intensity and short patience time, the service provider only needs to serve a portion of the customers. For heterogeneous customers, if a large proportion exhibits high herding intensity, the service provider should focus on serving them. Otherwise, the service provider should serve all high-intensity herding customers while striving to attract low-intensity herding customers.
Originality/value
This paper considers the combined utility of multiple customer psychology and examines homogeneous and heterogeneous customers. The findings provide valuable managerial insights for service providers' pricing and service strategies.
