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中国管理科学 ›› 2026, Vol. 34 ›› Issue (9): 237-247.doi: 10.16381/j.cnki.issn1003-207x.2023.1707

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基于付费型等待区增值服务的服务商运营决策研究

詹文韬1, 姜明辉1, 袁绪川2, 赵逸3, 李城璋4(), 林陈粲1   

  1. 1.哈尔滨工业大学经济与管理学院,黑龙江 哈尔滨 150001
    2.新加坡社科大学商学院,新加坡 599494
    3.中车资产管理有限公司,上海 200000
    4.深圳担保集团有限公司,广东 深圳 518057
  • 收稿日期:2023-10-23 修回日期:2025-01-09 出版日期:2026-09-25 发布日期:2026-09-01
  • 通讯作者: 李城璋 E-mail:lcz_grown@163.com
  • 基金资助:
    国家自然科学基金重点项目(71831005);国家自然科学基金青年基金(71502044)

The Operation Decision of Service Providers Based on Extra Waiting Area Value-added Service

Wentao Zhan1, Minghui Jiang1, Xuchuan Yuan2, Yi Zhao3, Chengzhang Li4(), Chencan Lin1   

  1. 1.School of Management,Harbin Institute of Technology,Harbin 150001,China
    2.School of Business,Singapore University of Social Sciences,Singapore 599494
    3.CRRC Asset Management Co. ,Ltd. ,Shanghai 200000,China
    4.Shenzhen Credit Guarantee Group Co. ,Ltd. ,Shenzhen 518057,China
  • Received:2023-10-23 Revised:2025-01-09 Online:2026-09-25 Published:2026-09-01
  • Contact: Chengzhang Li E-mail:lcz_grown@163.com

摘要:

付费型等待区增值服务(extra waiting area value-added service, EWS)是一种需要顾客额外付费购买的服务,它能够有效降低顾客等待成本并提升顾客获得的效用。本文对基本服务与付费型等待区增值服务进行了区分,构建了服务商同时提供两种服务的集中式模型,以及由第三方负责提供付费型等待区增值服务的分散式模型,得出了不同模型下的最优服务水平与定价策略,并分析了顾客消费观念转变对最优决策的影响。研究表明,在短期集中式运营中,付费型等待区增值服务并不影响基本服务的定价;但从长期来看,由于顾客需求的转移,付费型等待区增值服务会导致基本服务价格的上涨,采取集中式运营模式的服务商能够通过提高服务水平和降低基本服务价格,以实现更高利润。在分散式运营模式下,服务商与第三方能否在长期获得比短期更高的利润,取决于顾客转化率。研究还发现,无论从长期还是短期来看,对于有限资金约束下的服务商而言,分散式运营模式相比于集中式运营模式,是一种更为理想的选择。

关键词: 等待区增值服务, 集中式运营, 分散式运营, 排队论, 最优决策

Abstract:

The rapid development of the service industry plays a significant role in driving economic growth. To attract customers, many service providers not only offer base services but also introduce premium paid services such as Extra Waiting area value-added Service (EWS). These extra value-added services have become critical in the current service industry landscape. Not only do service providers offer EWS themselves, but third-party service companies also participate through collaborative modes. This raises challenges for service providers, such as how to make optimal decisions when operating both base services and EWS, and how to collaborate with third-party companies to maximize overall benefits.Therefore, it focuses on the following questions in this study: First, in a centralized operational mode, how should service providers determine the optimal EWS level and its pricing? Second, when EWS is provided by third-party companies, how should the optimal EWS level be set, and how should service providers adjust the pricing strategy for base services? Finally, what are the differences in EWS levels, pricing strategies, and profit performance between centralized and decentralized modes?Based on customer utility in service, the M/M/1 queuing model is used to characterize customer behavior in queues. On this basis, both a centralized mode for service providers and a collaborative mode involving third-party companies are constructed, allowing us to analyze the optimal decisions and profit performance of service providers and third-party companies. The findings reveal that (1) As customers place greater importance on EWS, indicated by increased sensitivity, the provider of EWS does not need to continuously raise the EWS level to achieve optimization. (2) In the short term, under centralized operations, EWS has an insignificant impact on the pricing of base services. However, in the long term, service providers need to moderately lower the price of base services to achieve higher profits. (3) Under decentralized operations, whether service providers and third-party companies can achieve higher unit profits in the long term compared to the short term depends on customer conversion rates. (4) Whether in short-term or long-term operations, centralized operations enable service providers to achieve higher profits from both base services and EWS. However, for service providers with limited funds and difficulties in independently operating EWS, decentralized operations may be a better strategic choice.

Key words: waiting area service, centralized operation, decentralized operation, queuing theory, optimal decision

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