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Psychological Heterogeneity in a Queue: The Impact of Loss Aversion on Service Pricing

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Abstract

The authors consider an M/M/1 queue with two types of customers, where customers are classified into two categories according to their psychological feelings when facing uncertainty about queue information. In the unobservable queue, experienced customers could accurately calculate their expected utilities, while first-time customers are loss-averse and the psychological feelings could incur additional gain-loss utilities. By defining customers’ willingness to pay, the authors derive the equilibrium joining-balking behaviors for each type of customer and obtain the service provider’s optimal pricing decision. The authors also classify the implications of the obtained results.

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References

  1. Jiang B and Yang B, Quality and pricing decisions in a market with consumer information sharing, Management Sci., 2018, 65(1): 272–285.

    Article  Google Scholar 

  2. Kœszegi B and Rabin M, A model of reference-dependent preferences, Quart. J. Econom., 2006, 121(4): 1133–1165.

    MATH  Google Scholar 

  3. Abdellaoui M and Kemel E, Eliciting prospect theory when consequences are measured in time units: Time is not money, Management Sci., 2014, 60(7): 1844–1859.

    Article  Google Scholar 

  4. Tereyağoğlu N, Fader P, and Veeraraghavan S, Multi-attribute loss aversion and reference dependence: Evidence from the performing arts industry, Management Sci., 2018, 64(1): 421–436.

    Article  Google Scholar 

  5. Zhou W, Wang D, Huang W, et al., To pool or not to pool? The effect of loss aversion on queue confgurations, Prod. Oper. Manag., 2021, 30(11): 4258–4272.

    Article  Google Scholar 

  6. Ni G, Xu Y, and Dong Y, Price and speed decisions in customer-intensive services with two classes of customers, Eur. J. Oper. Res., 2013, 228(2): 427–436.

    Article  MATH  Google Scholar 

  7. Zhou W, Chao X, and Gong X, Optimal uniform pricing strategy of a service firm when facing two classes of customers, Prod. Oper. Manag., 2014, 23(4): 676–688.

    Article  Google Scholar 

  8. Zhou W, Lian Z, and Wu J, When should service firms provide free experience service? Eur. J. Oper. Res., 2014, 234: 830–838.

    Article  MathSciNet  MATH  Google Scholar 

  9. Tang Y, Guo P, and Wang Y, Equilibrium queueing strategies of two types of customers in a two-server queue, Oper. Res. Lett., 2018, 46: 99–102.

    Article  MathSciNet  MATH  Google Scholar 

  10. Hu M, Li Y, and Wang J, Efficient ignorance: Information heterogeneity in a queue, Management Sci., 2018, 64(6): 2650–2671.

    Article  Google Scholar 

  11. Wang Z and Wang J, Information heterogeneity in a retrial queue: Throughput and social welfare maximization, Queueing Syst., 2019, 92: 131–172.

    Article  MathSciNet  MATH  Google Scholar 

  12. Huang F, Guo P, and Wang Y, Cyclic pricing when customers queue with rating information, Prod. Oper. Manag., 2019, 28(10): 2471–2485.

    Article  Google Scholar 

  13. Wang J and Sun K, Optimal pricing and capacity sizing for online service systems with free trials, OR Spectrum, 2022, 44: 57–86.

    Article  MathSciNet  MATH  Google Scholar 

  14. Ibrahim R, Sharing delay information in service systems: A literature survey, Queueing Syst., 2018, 89(1): 49–79.

    Article  MathSciNet  MATH  Google Scholar 

  15. Economou A, The impact of information structure on strategic behavior in queueing systems, Queueing Theory 2: Advanced Trends, Eds. by Anisimov V and Limnios N, Wiley/ISTE, New York, 2021.

    Google Scholar 

  16. Wang Z and Fang L, The effect of customer awareness on priority queues, Nav. Res. Log., 2022, 69(5): 801–805.

    Article  MathSciNet  MATH  Google Scholar 

  17. Kahneman D and Tversky A, Prospect theory: An analysis of decision under risk, Econometrica, 1979, 47(2): 263–291.

    Article  MathSciNet  MATH  Google Scholar 

  18. Gill D and Prowse V, A structural analysis of disappointment aversion in a real effort competition, Amer. Econom. Rev., 2012, 102(1): 469–503.

    Article  Google Scholar 

  19. Karle H, Kirchsteiger G, and Peitz M, Loss aversion and consumption choice: Theory and experimental evidence, Amer. Econom. J.: Microeconom, 2015, 7(2): 101–120.

    Google Scholar 

  20. Nasiry J and Popescu I, Dynamic pricing with loss-averse consumers and peak-end anchoring, Oper. Res., 2011, 59(6): 1361–1368.

    Article  MathSciNet  MATH  Google Scholar 

  21. Chen X, Hu P, Shum S, et al., Dynamic stochastic inventory management with reference price efects, Oper. Res., 2016, 64(6): 1529–1536.

    Article  MathSciNet  MATH  Google Scholar 

  22. Zhang J and Li K J, Quality disclosure under consumer loss aversion, Management Sci., 2021, 67(8): 5052–5069.

    Article  Google Scholar 

  23. Yang L, Guo P, and Wang Y, Service pricing with loss-averse customers, Oper. Res., 2018, 66(3): 761–777.

    Article  MathSciNet  MATH  Google Scholar 

  24. Jiang T, Chai X, Liu L, et al., Optimal pricing and service capacity management for a matching queue problem with loss-averse customers, Optimization, 2021, 70(10): 2169–2192.

    Article  MathSciNet  MATH  Google Scholar 

  25. Ülkü S, Hydock C, and Cui S, Making the wait worthwhile: Experiments on the effect of queueing on consumption, Management Sci., 2020, 66(3): 1149–1171.

    Article  Google Scholar 

  26. Buell R W, Last-place aversion in queues, Management Sci., 2021, 67(3): 1329–1992.

    Google Scholar 

  27. Wang Z, Yang L, Cui S, et al., Pooling agents for customer-intensive services, Oper. Res., 2022, 71(3): 860–875.

    Article  MathSciNet  Google Scholar 

Download references

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Correspondence to Tao Jiang.

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The authors declare no conflict of interest.

Additional information

This research was supported by the National Natural Science Foundation of China under Grant No. 12001329, Shandong Provincial Natural Science Foundation under Grant No. ZR2019BG014, Scientific Research Foundation of Anhui Polytechnic University under Grant No. 2022YQQ026, Scientific Research Foundation of Shandong University of Science and Technology for Recruited Talents under Grant No. 2019RCJJ016.

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Jiang, T., Gao, L., Chai, X. et al. Psychological Heterogeneity in a Queue: The Impact of Loss Aversion on Service Pricing. J Syst Sci Complex 36, 2536–2558 (2023). https://doi.org/10.1007/s11424-023-2117-9

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  • DOI: https://doi.org/10.1007/s11424-023-2117-9

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