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Impact of Penalty Cost on Customers' Booking Decisions

Author

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  • Jianghua Zhang
  • Daniel Zhuoyu Long
  • Rowan Wang
  • Chi Xie

Abstract

We study a novel newsvendor‐type problem where the information on demand quantity is not exogenously given. The customer needs to make the booking decision based on her estimation on demand, which is affected by the value of shortage penalty cost. The problem is motivated by low‐cost airline service practices where passengers need to book baggage allowance for their travel. The baggage overweight price affects the accuracy of passengers' baggage weight estimation and thus their booking quantities. Through stochastic decision models, we analytically characterize the impact of shortage penalty cost on passengers' booking decisions as well as airline's profit. We consider various modeling settings, including a system with multiple passengers and a system where passengers have stochastic inconvenience costs on not‐carrying overweight baggage. The results and insights from our study provide guidelines for firms to set their optimal penalty prices.

Suggested Citation

  • Jianghua Zhang & Daniel Zhuoyu Long & Rowan Wang & Chi Xie, 2021. "Impact of Penalty Cost on Customers' Booking Decisions," Production and Operations Management, Production and Operations Management Society, vol. 30(6), pages 1603-1614, June.
    • Handle: RePEc:bla:popmgt:v:30:y:2021:i:6:p:1603-1614
    DOI: 10.1111/poms.13297
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    References listed on IDEAS

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    1. Mariana Nicolae & Mazhar Arıkan & Vinayak Deshpande & Mark Ferguson, 2017. "Do Bags Fly Free? An Empirical Analysis of the Operational Implications of Airline Baggage Fees," Management Science, INFORMS, vol. 63(10), pages 3187-3206, October.
    2. Wai Hung Wong & Anming Zhang & Yer Van Hui & Lawrence C. Leung, 2009. "Optimal Baggage-Limit Policy: Airline Passenger and Cargo Allocation," Transportation Science, INFORMS, vol. 43(3), pages 355-369, August.
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    6. T. M. Whitin, 1955. "Inventory Control and Price Theory," Management Science, INFORMS, vol. 2(1), pages 61-68, October.
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    Cited by:

    1. Ran Yan & Wen Yi & Shuaian Wang, 2022. "Predicting Maximum Work Duration for Construction Workers," Sustainability, MDPI, vol. 14(17), pages 1-12, September.

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