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A hazard model of US airline passengers' refund and exchange behavior

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  • Iliescu, Dan C.
  • Garrow, Laurie A.
  • Parker, Roger A.

Abstract

This study explores the use of discrete choice methods for airline passenger cancellation behavior. A discrete time proportional odds model with a prospective time scale is estimated based on the occurrence of cancellations (defined as refund and exchange events) in a sample of tickets provided by the Airline Reporting Corporation. Empirical results based on 2004 data from eight domestic US markets indicate that the intensity of the cancellation process is strongly influenced by both the time from ticket purchase and the time before flight departure. Higher cancellations rates are generally observed for recently purchased tickets, and for tickets whose associated flight departure dates are near. Cancellations rates are influenced by several other covariates, including departure day of week, market, and group size.

Suggested Citation

  • Iliescu, Dan C. & Garrow, Laurie A. & Parker, Roger A., 2008. "A hazard model of US airline passengers' refund and exchange behavior," Transportation Research Part B: Methodological, Elsevier, vol. 42(3), pages 229-242, March.
    • Handle: RePEc:eee:transb:v:42:y:2008:i:3:p:229-242
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    References listed on IDEAS

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    1. Dipak C. Jain & Naufel J. Vilcassim, 1991. "Investigating Household Purchase Timing Decisions: A Conditional Hazard Function Approach," Marketing Science, INFORMS, vol. 10(1), pages 1-23.
    2. Kalyan Talluri & Garrett van Ryzin, 2004. "Revenue Management Under a General Discrete Choice Model of Consumer Behavior," Management Science, INFORMS, vol. 50(1), pages 15-33, January.
    3. Kiefer, Nicholas M, 1988. "Economic Duration Data and Hazard Functions," Journal of Economic Literature, American Economic Association, vol. 26(2), pages 646-679, June.
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    Cited by:

    1. Yingjie Lan & Michael O. Ball & Itir Z. Karaesmen, 2011. "Regret in Overbooking and Fare-Class Allocation for Single Leg," Manufacturing & Service Operations Management, INFORMS, vol. 13(2), pages 194-208, December.
    2. Yan Liu & Dan Zhang, 2024. "Intraconsumer Price Discrimination with Credit Refund Policies," Management Science, INFORMS, vol. 70(10), pages 6835-6851, October.
    3. Chiew, Esther & Daziano, Ricardo A. & Garrow, Laurie A., 2017. "Bayesian estimation of hazard models of airline passengers’ cancellation behavior," Transportation Research Part A: Policy and Practice, Elsevier, vol. 96(C), pages 154-167.
    4. Syed A. M. Shihab & Peng Wei, 2022. "A deep reinforcement learning approach to seat inventory control for airline revenue management," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 21(2), pages 183-199, April.
    5. Xie, Jiemin & Zhan, Shuguang & Wong, S.C. & Wen, Keyu & Qiang, Lixia & Lo, S.M., 2022. "High-speed rail services for elderly passengers: Ticket-booking patterns and policy implications," Transport Policy, Elsevier, vol. 125(C), pages 96-106.
    6. Sierag, D.D. & Koole, G.M. & van der Mei, R.D. & van der Rest, J.I. & Zwart, B., 2015. "Revenue management under customer choice behaviour with cancellations and overbooking," European Journal of Operational Research, Elsevier, vol. 246(1), pages 170-185.
    7. Moongil Yoon & Habin Lee, 2021. "Seat assignment problem with the payable up-grade as an ancillary service of airlines," Annals of Operations Research, Springer, vol. 307(1), pages 483-497, December.
    8. Patrick Winter & Paul Alpar, 2018. "On the relationship between print and mobile channels for newspapers," Electronic Markets, Springer;IIM University of St. Gallen, vol. 28(1), pages 79-92, February.
    9. Piening, J. & Ehrmann, T. & Meiseberg, B., 2013. "Competing risks for train tickets – An empirical investigation of customer behavior and performance in the railway industry," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 51(C), pages 1-16.
    10. Romero Morales, Dolores & Wang, Jingbo, 2010. "Forecasting cancellation rates for services booking revenue management using data mining," European Journal of Operational Research, Elsevier, vol. 202(2), pages 554-562, April.
    11. Cho, WooKeol & Chung, Jin-Hyuk & Kim, Jinhee, 2023. "Need-based approach for modeling multiday activity participation patterns and identifying the impact of activity/travel conditions," Transportation Research Part A: Policy and Practice, Elsevier, vol. 172(C).
    12. Habib, Khandker M. Nurul & Morency, Catherine & Islam, Mohammed Tazul & Grasset, Vincent, 2012. "Modelling users’ behaviour of a carsharing program: Application of a joint hazard and zero inflated dynamic ordered probability model," Transportation Research Part A: Policy and Practice, Elsevier, vol. 46(2), pages 241-254.
    13. ChihChien Chen, 2016. "Cancellation policies in the hotel, airline and restaurant industries," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 15(3), pages 270-275, July.
    14. Lee, Hyunae & Yang, Sung-Byung & Chung, Namho, 2021. "Out of sight, out of cancellation: The impact of psychological distance on the cancellation behavior of tourists," Journal of Air Transport Management, Elsevier, vol. 90(C).

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