To disclose or to conceal? Comparison of different disclosure policies in queues with loss-averse customers

IF 7.5 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Jian Cao , Yongjiang Guo
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Abstract

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.
披露还是隐瞒?比较有损失规避型顾客的排队过程中的不同披露政策
在许多服务行业中,披露产品信息可以减轻客户因产品估值不确定而产生的损失厌恶情绪。在本文中,我们考虑了一个单服务器排队系统,在该系统中,私下了解估值信息的经理会策略性地向损失规避型客户披露估值信息。我们研究了客户的损失规避对系统均衡到达率和经理的最优披露策略的影响。我们发现,损失厌恶会抑制客户加入队列。令人惊讶的是,我们发现没有一种披露政策总是优于其他披露政策。具体来说,只有当估值较大且损失厌恶程度适中时,完全披露政策才是最优的。当损失规避程度过大或过小,或者估值较小时,完全不披露政策是最优的。当估值和损失规避程度适中时,阈值披露政策是最优的。此外,在临界披露政策下,损失规避程度的增加会使管理者更不愿意披露估值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Expert Systems with Applications
Expert Systems with Applications 工程技术-工程:电子与电气
CiteScore
13.80
自引率
10.60%
发文量
2045
审稿时长
8.7 months
期刊介绍: Expert Systems With Applications is an international journal dedicated to the exchange of information on expert and intelligent systems used globally in industry, government, and universities. The journal emphasizes original papers covering the design, development, testing, implementation, and management of these systems, offering practical guidelines. It spans various sectors such as finance, engineering, marketing, law, project management, information management, medicine, and more. The journal also welcomes papers on multi-agent systems, knowledge management, neural networks, knowledge discovery, data mining, and other related areas, excluding applications to military/defense systems.
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