IDEAS home Printed from https://ideas.repec.org/a/pal/jorapm/v15y2016i3d10.1057_rpm.2016.16.html
   My bibliography  Save this article

From travel agents to OTAs: How the evolution of consumer booking behavior has affected revenue management

Author

Listed:
  • Timothy Webb

    (Virginia Polytechnic Institute
    Delaware North Companies)

Abstract

Travel booking behavior has changed substantially over the past two decades. The traditional approach of utilizing travel agents and booking ahead has evolved into a fast-paced, last-minute booking environment. This evolution has had substantial effects on revenue management (RM) in the areas of forecasting, pricing and online travel agency inventory allocations. These changes have made understanding the consumer booking process a necessary requirement for success. This article reviews the relevant literature on this historical shift and the effects it has had on RM.

Suggested Citation

  • Timothy Webb, 2016. "From travel agents to OTAs: How the evolution of consumer booking behavior has affected revenue management," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 15(3), pages 276-282, July.
    • Handle: RePEc:pal:jorapm:v:15:y:2016:i:3:d:10.1057_rpm.2016.16
    DOI: 10.1057/rpm.2016.16
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1057/rpm.2016.16
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1057/rpm.2016.16?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. Xuanming Su, 2007. "Intertemporal Pricing with Strategic Customer Behavior," Management Science, INFORMS, vol. 53(5), pages 726-741, May.
    2. Weatherford, Larry R. & Kimes, Sheryl E., 2003. "A comparison of forecasting methods for hotel revenue management," International Journal of Forecasting, Elsevier, vol. 19(3), pages 401-415.
    3. Kinshuk Jerath & Serguei Netessine & Senthil K. Veeraraghavan, 2010. "Revenue Management with Strategic Customers: Last-Minute Selling and Opaque Selling," Management Science, INFORMS, vol. 56(3), pages 430-448, March.
    4. Yang, Xin & Pan, Bing & Evans, James A. & Lv, Benfu, 2015. "Forecasting Chinese tourist volume with search engine data," Tourism Management, Elsevier, vol. 46(C), pages 386-397.
    5. Bangwayo-Skeete, Prosper F. & Skeete, Ryan W., 2015. "Can Google data improve the forecasting performance of tourist arrivals? Mixed-data sampling approach," Tourism Management, Elsevier, vol. 46(C), pages 454-464.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zvi Schwartz & Timothy Webb & Jean-Pierre I van der Rest & Larissa Koupriouchina, 2021. "Enhancing the accuracy of revenue management system forecasts: The impact of machine and human learning on the effectiveness of hotel occupancy forecast combinations across multiple forecasting horizo," Tourism Economics, , vol. 27(2), pages 273-291, March.
    2. Carmen Berne-Manero & Maria Gómez-Campillo & Mercedes Marzo-Navarro & Marta Pedraja-Iglesias, 2018. "Reviewing the Online Tourism Value Chain," Administrative Sciences, MDPI, vol. 8(3), pages 1-18, August.
    3. Slak Valek, Nataša, 2015. "Tourism expenditure according to mode of transportation: A comparative study between 2009 and 2012," MPRA Paper 77406, University Library of Munich, Germany, revised 07 Oct 2015.
    4. Alderighi, Marco & Nava, Consuelo R. & Calabrese, Matteo & Christille, Jean-Marc & Salvemini, Chiara B., 2022. "Consumer perception of price fairness and dynamic pricing: Evidence from Booking.com," Journal of Business Research, Elsevier, vol. 145(C), pages 769-783.
    5. Seung Hyun Lee & Cynthia S. Deale & Jaeyong Lee, 2022. "Does it pay to book direct?: Customers’ perceptions of online channel distributors, price, and loyalty membership on brand dimensions," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 21(6), pages 657-667, December.
    6. Guizzardi, Andrea & Ballestra, Luca Vincenzo & D'Innocenzo, Enzo, 2022. "Hotel dynamic pricing, stochastic demand and covid-19," Annals of Tourism Research, Elsevier, vol. 97(C).
    7. Miriam Scaglione & Coin Johnson & Pascale Favre, 2019. "As time goes by: last minute momentum booking and the planned vacation process," Information Technology & Tourism, Springer, vol. 21(1), pages 9-22, March.
    8. Kwanglim Seo, 2019. "Same-day discounting’s effect on consumers’ evaluations of a hotel," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 18(2), pages 107-111, April.
    9. Martin Falk & Markku Vieru, 2019. "Myth of early booking gains," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 18(1), pages 52-64, February.
    10. Andrea Guizzardi & Luca Vincenzo Ballestra & Enzo D’Innocenzo, 2024. "Reverse engineering the last-minute on-line pricing practices: an application to hotels," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 33(3), pages 943-971, July.

    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. Doris Chenguang Wu & Shiteng Zhong & Richard T R Qiu & Ji Wu, 2022. "Are customer reviews just reviews? Hotel forecasting using sentiment analysis," Tourism Economics, , vol. 28(3), pages 795-816, May.
    2. Guizzardi, Andrea & Pons, Flavio Maria Emanuele & Angelini, Giovanni & Ranieri, Ercolino, 2021. "Big data from dynamic pricing: A smart approach to tourism demand forecasting," International Journal of Forecasting, Elsevier, vol. 37(3), pages 1049-1060.
    3. Song, Haiyan & Qiu, Richard T.R. & Park, Jinah, 2019. "A review of research on tourism demand forecasting," Annals of Tourism Research, Elsevier, vol. 75(C), pages 338-362.
    4. Ling Tang & Chengyuan Zhang & Tingfei Li & Ling Li, 2021. "A novel BEMD-based method for forecasting tourist volume with search engine data," Tourism Economics, , vol. 27(5), pages 1015-1038, August.
    5. Eden Xiaoying Jiao & Jason Li Chen, 2019. "Tourism forecasting: A review of methodological developments over the last decade," Tourism Economics, , vol. 25(3), pages 469-492, May.
    6. Yingxiao Li & Jianheng Zhou, 2023. "Modeling the relationship between fairness concern and customer loyalty in dual distribution channel," Journal of Combinatorial Optimization, Springer, vol. 45(1), pages 1-25, January.
    7. Binru Zhang & Yulian Pu & Yuanyuan Wang & Jueyou Li, 2019. "Forecasting Hotel Accommodation Demand Based on LSTM Model Incorporating Internet Search Index," Sustainability, MDPI, vol. 11(17), pages 1-14, August.
    8. Fengzhi Sun & Zihan Li & Mingzhi Xu & Mingcan Han, 2024. "New Changes in Chinese Urban Tourism Pattern under the Impact of COVID-19 Pandemic: Based on Internet Attention," Sustainability, MDPI, vol. 16(14), pages 1-22, July.
    9. Long Wen & Chang Liu & Haiyan Song, 2019. "Forecasting tourism demand using search query data: A hybrid modelling approach," Tourism Economics, , vol. 25(3), pages 309-329, May.
    10. Yao Cui & Izak Duenyas & Ozge Sahin, 2018. "Pricing of Conditional Upgrades in the Presence of Strategic Consumers," Management Science, INFORMS, vol. 64(7), pages 3208-3226, July.
    11. Han Liu & Yongjing Wang & Haiyan Song & Ying Liu, 2023. "Measuring tourism demand nowcasting performance using a monotonicity test," Tourism Economics, , vol. 29(5), pages 1302-1327, August.
    12. Hulya Bakirtas & Vildan Gulpinar Demirci, 2022. "Can Google Trends data provide information on consumer’s perception regarding hotel brands?," Information Technology & Tourism, Springer, vol. 24(1), pages 57-83, March.
    13. Guizzardi, Andrea & Ballestra, Luca Vincenzo & D'Innocenzo, Enzo, 2022. "Hotel dynamic pricing, stochastic demand and covid-19," Annals of Tourism Research, Elsevier, vol. 97(C).
    14. Javier Sebastian, 2016. "Blockchain in financial services: Regulatory landscape and future challenges," Working Papers 16/21, BBVA Bank, Economic Research Department.
    15. A Fronzetti Colladon & B Guardabascio & R Innarella, 2021. "Using social network and semantic analysis to analyze online travel forums and forecast tourism demand," Papers 2105.07727, arXiv.org.
    16. Zhang, Chuan & Tian, Yu-Xin & Fan, Zhi-Ping, 2022. "Forecasting sales using online review and search engine data: A method based on PCA–DSFOA–BPNN," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1005-1024.
    17. Gang Xie & Xin Li & Yatong Qian & Shouyang Wang, 2021. "Forecasting tourism demand with KPCA-based web search indexes," Tourism Economics, , vol. 27(4), pages 721-743, June.
    18. Mantin, Benny & Gillen, David, 2011. "The hidden information content of price movements," European Journal of Operational Research, Elsevier, vol. 211(2), pages 385-393, June.
    19. Bi, Jian-Wu & Liu, Yang & Li, Hui, 2020. "Daily tourism volume forecasting for tourist attractions," Annals of Tourism Research, Elsevier, vol. 83(C).
    20. Rivera, Roberto, 2016. "A dynamic linear model to forecast hotel registrations in Puerto Rico using Google Trends data," Tourism Management, Elsevier, vol. 57(C), pages 12-20.

    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:pal:jorapm:v:15:y:2016:i:3:d:10.1057_rpm.2016.16. 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.palgrave.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.