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Implications of Hyperbolic Discounting for Optimal Pricing and Scheduling of Unpleasant Services That Generate Future Benefits

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  • Erica L. Plambeck

    (Operations, Information and Technology, Graduate School of Business, Stanford University, Stanford, California 94305)

  • Qiong Wang

    (Industrial and Enterprise Systems Engineering, University of Illinois at Urbana--Champaign, Urbana, Illinois 61801)

Abstract

People tend to lack the self-control to undergo an unpleasant service that would generate future benefits. This paper derives a tractable quasi-hyperbolic discounting model of that behavioral tendency (for a queueing system in which service time is short relative to the time horizon for its benefits). Planning in advance, people may naively overestimate their self-control. This paper shows how customers' lack of self-control and naivete affect optimal pricing and scheduling. The welfare-maximizing usage fee and the revenue-maximizing usage fee decrease with customers' lack of self-control. Charging for subscription, in addition to or instead of per use, increases revenue, especially when subscribers are naive. If the manager can charge for subscription or per use, subscription is optimal for revenue maximization, whereas usage-based pricing is optimal for welfare maximization. If customers are heterogeneous in their self-control and naivete, priority scheduling can dramatically increase welfare and revenue. This paper was accepted by Christian Terwiesch, operations management.

Suggested Citation

  • Erica L. Plambeck & Qiong Wang, 2013. "Implications of Hyperbolic Discounting for Optimal Pricing and Scheduling of Unpleasant Services That Generate Future Benefits," Management Science, INFORMS, vol. 59(8), pages 1927-1946, August.
  • Handle: RePEc:inm:ormnsc:v:59:y:2013:i:8:p:1927-1946
    DOI: 10.1287/mnsc.1120.1673
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    References listed on IDEAS

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    Cited by:

    1. Jordan Tong & Daniel Feiler, 2017. "A Behavioral Model of Forecasting: Naive Statistics on Mental Samples," Management Science, INFORMS, vol. 63(11), pages 3609-3627, November.
    2. Zhou Yongwu & Lin Zhaozhan, 2016. "Impacts of Hyperbolic Discounting on Inventory Replenishment Policy Under Inflation," Journal of Systems Science and Information, De Gruyter, vol. 4(1), pages 24-39, February.
    3. Ming Hu & Yang Li & Jianfu Wang, 2018. "Efficient Ignorance: Information Heterogeneity in a Queue," Management Science, INFORMS, vol. 64(6), pages 2650-2671, June.
    4. Shiliang Cui & Xuanming Su & Senthil Veeraraghavan, 2019. "A Model of Rational Retrials in Queues," Operations Research, INFORMS, vol. 67(6), pages 1699-1718, November.
    5. Robert J. Batt & Christian Terwiesch, 2015. "Waiting Patiently: An Empirical Study of Queue Abandonment in an Emergency Department," Management Science, INFORMS, vol. 61(1), pages 39-59, January.
    6. Vishal V. Agrawal & Stylianos Kavadias & L. Beril Toktay, 2016. "The Limits of Planned Obsolescence for Conspicuous Durable Goods," Manufacturing & Service Operations Management, INFORMS, vol. 18(2), pages 216-226, May.
    7. Wang, Xinchun, 2024. "Does CEO temporal myopia always lead to firm short-termism? The critical role of CEO optimism and perceived opportunity costs," Journal of Business Research, Elsevier, vol. 180(C).
    8. Robert J. Batt & Jordan D. Tong, 2020. "Mean Service Metrics: Biased Quality Judgment and the Customer–Server Quality Gap," Manufacturing & Service Operations Management, INFORMS, vol. 22(5), pages 975-995, September.
    9. Małgorzata Niklewicz-Pijaczyńska & Elżbieta Stańczyk & Anna Gardocka-Jałowiec & Zofia Gródek-Szostak & Agata Niemczyk & Katarzyna Szalonka & Magdalena Homa, 2021. "A Strategy for Planned Product Aging in View of Sustainable Development Challenges," Energies, MDPI, vol. 14(22), pages 1-20, November.
    10. Nicholas G. Hall & Zhixin Liu, 2023. "Scheduling with present bias," Production and Operations Management, Production and Operations Management Society, vol. 32(6), pages 1743-1759, June.
    11. Philipp Afèche & Opher Baron & Joseph Milner & Ricky Roet-Green, 2019. "Pricing and Prioritizing Time-Sensitive Customers with Heterogeneous Demand Rates," Operations Research, INFORMS, vol. 67(4), pages 1184-1208, July.
    12. Pnina Feldman & Ella Segev, 2022. "The Important Role of Time Limits When Consumers Choose Their Time in Service," Management Science, INFORMS, vol. 68(9), pages 6666-6686, September.
    13. Yang, Daojian & Qi, Ershi & Li, Yajiao, 2015. "Quick response and supply chain structure with strategic consumers," Omega, Elsevier, vol. 52(C), pages 1-14.
    14. Aparupa Das Gupta & Uday S. Karmarkar & Guillaume Roels, 2016. "The Design of Experiential Services with Acclimation and Memory Decay: Optimal Sequence and Duration," Management Science, INFORMS, vol. 62(5), pages 1278-1296, May.
    15. Sezer Ülkü & Chris Hydock & Shiliang Cui, 2020. "Making the Wait Worthwhile: Experiments on the Effect of Queueing on Consumption," Management Science, INFORMS, vol. 66(3), pages 1149-1171, March.
    16. Gregory DeCroix & Xiaoyang Long & Jordan Tong, 2021. "How Service Quality Variability Hurts Revenue When Customers Learn: Implications for Dynamic Personalized Pricing," Operations Research, INFORMS, vol. 69(3), pages 683-708, May.
    17. Li Li & Li Jiang, 2022. "How should firms adapt pricing strategies when consumers are time‐inconsistent?," Production and Operations Management, Production and Operations Management Society, vol. 31(9), pages 3457-3473, September.

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