IDEAS home Printed from https://ideas.repec.org/a/spr/metcap/v25y2023i3d10.1007_s11009-023-10047-w.html
   My bibliography  Save this article

The Effect of Loss Preference on Queueing with Information Disclosure Policy

Author

Listed:
  • Jian Cao

    (Beijing Univeristy of Posts and Telecommunications
    Ministry of Education)

  • Yongjiang Guo

    (Beijing Univeristy of Posts and Telecommunications
    Ministry of Education)

  • Zhongxin Hu

    (Beijing Univeristy of Posts and Telecommunications
    Ministry of Education)

Abstract

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 are assumed to be fully rational, whereas uninformed customers are assumed to have loss preference. Uninformed customers with loss preference are categorized into two types according to their asymmetry perceptions, which anchor on the difference between gain and loss: loss neutrality and loss aversion. We firstly determine customers’ equilibrium decisions, and then derive the idle stationary probability at equilibrium. We find that loss preference reduces the customers’ joining probability, and results in a higher idle stationary probability. Furthermore, we find that for the uninformed customers with stronger loss aversion, the system manager should lower the threshold of disclosure to maintain a stable demand of uninformed customers. In addition, in the case of mixed-strategy at equilibrium, with the increase of the threshold of disclosure, the idle stationary probability increases for an underloaded queue. However, for an overloaded queue, the idle stationary probability decreases with increasing the threshold of disclosure.

Suggested Citation

  • Jian Cao & Yongjiang Guo & Zhongxin Hu, 2023. "The Effect of Loss Preference on Queueing with Information Disclosure Policy," Methodology and Computing in Applied Probability, Springer, vol. 25(3), pages 1-25, September.
  • Handle: RePEc:spr:metcap:v:25:y:2023:i:3:d:10.1007_s11009-023-10047-w
    DOI: 10.1007/s11009-023-10047-w
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11009-023-10047-w
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s11009-023-10047-w?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Lindsey, Robin, 2011. "State-dependent congestion pricing with reference-dependent preferences," Transportation Research Part B: Methodological, Elsevier, vol. 45(10), pages 1501-1526.
    2. Botond Kőszegi & Matthew Rabin, 2006. "A Model of Reference-Dependent Preferences," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 121(4), pages 1133-1165.
    3. Botond Kőszegi & Paul Heidhues, 2008. "Competition and Price Variation When Consumers Are Loss Averse," American Economic Review, American Economic Association, vol. 98(4), pages 1245-1268, September.
    4. Jonathan Shalev, 2000. "Loss aversion equilibrium," International Journal of Game Theory, Springer;Game Theory Society, vol. 29(2), pages 269-287.
    5. Enrico G. De Giorgi & Thierry Post, 2011. "Loss Aversion with a State-Dependent Reference Point," Management Science, INFORMS, vol. 57(6), pages 1094-1110, June.
    6. Eugene W. Anderson & Mary W. Sullivan, 1993. "The Antecedents and Consequences of Customer Satisfaction for Firms," Marketing Science, INFORMS, vol. 12(2), pages 125-143.
    7. Ming Hu & Yang Li & Jianfu Wang, 2018. "Efficient Ignorance: Information Heterogeneity in a Queue," Management Science, INFORMS, vol. 64(6), pages 2650-2671, June.
    8. , & ,, 2014. "Regular prices and sales," Theoretical Economics, Econometric Society, vol. 9(1), January.
    9. Edelson, Noel M & Hildebrand, David K, 1975. "Congestion Tolls for Poisson Queuing Processes," Econometrica, Econometric Society, vol. 43(1), pages 81-92, January.
    10. Ping Cao & Yaolei Wang & Jingui Xie, 2019. "Priority Service Pricing with Heterogeneous Customers: Impact of Delay Cost Distribution," Production and Operations Management, Production and Operations Management Society, vol. 28(11), pages 2854-2876, November.
    11. Daniel Kahneman & Amos Tversky, 2013. "Prospect Theory: An Analysis of Decision Under Risk," World Scientific Book Chapters, in: Leonard C MacLean & William T Ziemba (ed.), HANDBOOK OF THE FUNDAMENTALS OF FINANCIAL DECISION MAKING Part I, chapter 6, pages 99-127, World Scientific Publishing Co. Pte. Ltd..
    12. Pengfei Guo & Moshe Haviv & Zhenwei Luo & Yulan Wang, 2022. "Optimal queue length information disclosure when service quality is uncertain," Production and Operations Management, Production and Operations Management Society, vol. 31(5), pages 1912-1927, May.
    13. Laurens G. Debo & Christine Parlour & Uday Rajan, 2012. "Signaling Quality via Queues," Management Science, INFORMS, vol. 58(5), pages 876-891, May.
    14. Moshe Haviv & Ramandeep S. Randhawa, 2014. "Pricing in Queues Without Demand Information," Manufacturing & Service Operations Management, INFORMS, vol. 16(3), pages 401-411, July.
    15. Wenhui Zhou & Dongmei Wang & Weixiang Huang & Pengfei Guo, 2021. "To Pool or Not to Pool? The Effect of Loss Aversion on Queue Configurations," Production and Operations Management, Production and Operations Management Society, vol. 30(11), pages 4258-4272, November.
    16. Pascal Courty & Javad Nasiry, 2018. "Loss aversion and the uniform pricing puzzle for media and entertainment products," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 66(1), pages 105-140, July.
    17. De Borger, Bruno & Glazer, Amihai, 2017. "Support and opposition to a Pigovian tax: Road pricing with reference-dependent preferences," Journal of Urban Economics, Elsevier, vol. 99(C), pages 31-47.
    18. Han Zhu & Yimin Yu & Saibal Ray, 2021. "Quality Disclosure Strategy under Customer Learning Opportunities," Production and Operations Management, Production and Operations Management Society, vol. 30(4), pages 1136-1153, April.
    19. Naor, P, 1969. "The Regulation of Queue Size by Levying Tolls," Econometrica, Econometric Society, vol. 37(1), pages 15-24, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jong-Hee Hahn & Jinwoo Kim & Sang-Hyun Kim & Jihong Lee, 2018. "Price discrimination with loss averse consumers," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 65(3), pages 681-728, May.
    2. Karle, Heiko & Schumacher, Heiner & Vølund, Rune, 2023. "Consumer loss aversion and scale-dependent psychological switching costs," Games and Economic Behavior, Elsevier, vol. 138(C), pages 214-237.
    3. Dato, Simon & Grunewald, Andreas & Müller, Daniel & Strack, Philipp, 2017. "Expectation-based loss aversion and strategic interaction," Games and Economic Behavior, Elsevier, vol. 104(C), pages 681-705.
    4. Benjamin Balzer & Antonio Rosato, 2021. "Expectations-Based Loss Aversion in Auctions with Interdependent Values: Extensive vs. Intensive Risk," Management Science, INFORMS, vol. 67(2), pages 1056-1074, February.
    5. Botond Kőszegi & Matthew Rabin, 2006. "A Model of Reference-Dependent Preferences," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 121(4), pages 1133-1165.
    6. Heiko Karle & Heiner Schumacher & Rune Vølund, 2020. "Consumer search and the uncertainty effect," Working Papers of Department of Economics, Leuven 657766, KU Leuven, Faculty of Economics and Business (FEB), Department of Economics, Leuven.
    7. Aldo Pignataro, 2019. "The effects of loss aversion on deceptive advertising policies," Theory and Decision, Springer, vol. 87(4), pages 451-472, November.
    8. Wenner, Lukas M., 2015. "Expected prices as reference points—Theory and experiments," European Economic Review, Elsevier, vol. 75(C), pages 60-79.
    9. Ahrens, Steffen & Pirschel, Inske & Snower, Dennis J., 2017. "A theory of price adjustment under loss aversion," Journal of Economic Behavior & Organization, Elsevier, vol. 134(C), pages 78-95.
    10. Colombo, Luca & Labrecciosa, Paola, 2021. "Dynamic oligopoly pricing with reference-price effects," European Journal of Operational Research, Elsevier, vol. 288(3), pages 1006-1016.
    11. Pagel, Michaela, 2013. "Expectations-Based Reference-Dependent Life-Cycle Consumption," MPRA Paper 47138, University Library of Munich, Germany.
    12. Kohei Daido & Takeshi Murooka, 2016. "Team Incentives and Reference‐Dependent Preferences," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 25(4), pages 958-989, December.
    13. Pesendorfer, Martin & Gentry, Matthew, 2018. "Price Reference Effects in Consumer Demand," CEPR Discussion Papers 13382, C.E.P.R. Discussion Papers.
    14. Johannes Abeler & Armin Falk & Lorenz Goette & David Huffman, 2011. "Reference Points and Effort Provision," American Economic Review, American Economic Association, vol. 101(2), pages 470-492, April.
    15. Liu Yang & Francis de Véricourt & Peng Sun, 2014. "Time-Based Competition with Benchmark Effects," Manufacturing & Service Operations Management, INFORMS, vol. 16(1), pages 119-132, February.
    16. Xingyi Liu, 2018. "Disclosing information to a loss-averse audience," Economic Theory Bulletin, Springer;Society for the Advancement of Economic Theory (SAET), vol. 6(1), pages 63-79, April.
    17. Pagel, Michaela, 2019. "Prospective gain-loss utility: Ordered versus separated comparison," Journal of Economic Behavior & Organization, Elsevier, vol. 168(C), pages 62-75.
    18. Meisner, Vincent & von Wangenheim, Jonas, 2019. "School Choice and Loss Aversion," Rationality and Competition Discussion Paper Series 208, CRC TRR 190 Rationality and Competition.
    19. David B. Brown & Melvyn Sim, 2009. "Satisficing Measures for Analysis of Risky Positions," Management Science, INFORMS, vol. 55(1), pages 71-84, January.
    20. Deng, Yiting & Staelin, Richard & Wang, Wei & Boulding, William, 2018. "Consumer sophistication, word-of-mouth and “False” promotions," Journal of Economic Behavior & Organization, Elsevier, vol. 152(C), pages 98-123.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:metcap:v:25:y:2023:i:3:d:10.1007_s11009-023-10047-w. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.