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

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Journal of Revenue and Pricing Management Aims and scope

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.

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References

  • Asvanunt, A. and Kachani, S. (2006) Optimal Purchasing Policy for Strategic Customers under Different Dynamic Pricing Models. Proceedings of the Presentation at the 6th Annual INFORMS Revenue Management and Pricing Section Conference; New York. Columbia University.

  • Bangwayo-Skeete, P.F. and Skeete, R.W. (2015) Can Google data improve the forecasting performance of tourist arrivals? Mixed-data sampling approach. Tourism Management 46 (2015): 454–464.

    Article  Google Scholar 

  • Barthel, J. and Perret, S. (2015) OTAs – A Hotel’s Friend or Foe? How Reliant are Hotels on OTAs?, hospitality net, http://www.hospitalitynet.org/news/4070994.html, accessed 1 October 2015.

  • Carroll, B. and Siguaw, J. (2003) The evolution of electronic distribution: Effects on hotels and intermediaries. The Cornell Hotel and Restaurant Administration Quarterly 44 (4): 38–50.

    Article  Google Scholar 

  • Chen, C. and Schwartz, Z. (2008) Room rate patterns and customers’ propensity to book a hotel room. Journal of Hospitality & Tourism Research 32 (3): 287–306.

    Article  Google Scholar 

  • Chen, C.-C. and Schwartz, Z. (2013) On revenue management and last minute booking dynamics. International Journal of Contemporary Hospitality Management 25 (1): 7–22.

    Article  Google Scholar 

  • Chen, C.-C., Schwartz, Z. and Vargas, P. (2011) The search for the best deal: How hotel cancellation policies affect the search and booking decisions of deal-seeking customers. International Journal of Hospitality Management 30 (1): 129–135.

    Article  Google Scholar 

  • Choi, S. and Kimes, S.E. (2002) Electronic distribution channels’ effect on hotel revenue management. Cornell Hotel & Restaurant Administration Quarterly 43(3): 23.9.

  • Claveria, O., Monte, E. and Torra, S. (2015) A new forecasting approach for the hospitality industry. International Journal of Contemporary Hospitality Management 27 (7): 1520–1538.

    Article  Google Scholar 

  • Cross, R.G., Higbie, J.A. and Cross, D.Q.D. (2009) Revenue management’s renaissance a rebirth of the art and science of profitable revenue generation. Cornell Hospitality Quarterly 50 (1): 56–81.

    Article  Google Scholar 

  • Gasdia, M. (2015) Mobile travel shopping hits tipping point – But PC still dominates booking, http://www.phocuswright.com/Travel-Research/Research-Updates/2015/Mobile-Travel-Shopping-Hits-Tipping-Point-But-PC-Still-Dominates-Booking, accessed 1 October 2015.

  • Jerath, K., Netessine, S. and Veeraraghavan, S.K. (2010) Revenue management with strategic customers: Last-minute selling and opaque selling. Management Science 56 (3): 430–448.

    Article  Google Scholar 

  • Law, R. (1998) Room occupancy rate forecasting: A neural network approach. International Journal of Contemporary Hospitality Management 10 (6): 234–239.

    Article  Google Scholar 

  • Noone, B.M. and Mattila, A.S. (2009) Hotel revenue management and the Internet: The effect of price presentation strategies on customers’ willingness to book. International Journal of Hospitality Management 28 (2): 272–279.

    Article  Google Scholar 

  • O’Connor, P. and Frew, A.J. (2002) The future of hotel electronic distribution: Expert and industry perspectives. The Cornell Hotel and Restaurant Administration Quarterly 43 (3): 33–45.

    Article  Google Scholar 

  • Pan, B., Chenguang Wu, D. and Song, H. (2012) Forecasting hotel room demand using search engine data. Journal of Hospitality and Tourism Technology 3 (3): 196–210.

    Article  Google Scholar 

  • Schwartz, Z. (2006) Advanced booking and revenue management: Room rates and the consumers’ strategic zones. International Journal of Hospitality Management 25 (3): 447–462.

    Article  Google Scholar 

  • Schwartz, Z. (2008) Time, price, and advanced booking of hotel rooms. International Journal of Hospitality & Tourism Administration 9 (2): 128–146.

    Article  Google Scholar 

  • Schwartz, Z. and Chen, C.-C. (2012) Hedonic motivations and the effectiveness of risk perceptions – Oriented revenue management policies. Journal of Hospitality & Tourism Research 36 (2): 232–250.

    Article  Google Scholar 

  • Shen, Z.J.M. and Su, X. (2007) Customer behavior modeling in revenue management and auctions: A review and new research opportunities. Production and Operations Management 16 (6): 713–728.

    Article  Google Scholar 

  • Su, X. (2007) Intertemporal pricing with strategic customer behavior. Management Science 53 (5): 726–741.

    Article  Google Scholar 

  • TravelClick (2015) Hoteliers continue to see growth in bookings via online channels, http://www.travelclick.com/en/news-events/press-release/hoteliers-continue-see-growth-bookings-online-channels, accessed 1 October 2015.

  • Uysal, M. and El Roubi, M.S. (1999) Artificial neural networks versus multiple regression in tourism demand analysis. Journal of Travel Research 38 (2): 111–118.

    Article  Google Scholar 

  • Weatherford, L.R. and Kimes, S.E. (2003) A comparison of forecasting methods for hotel revenue management. International Journal of Forecasting 19 (3): 401–415.

    Article  Google Scholar 

  • Yang, X., Pan, B., Evans, J.A. and Lv, B. (2015) Forecasting Chinese tourist volume with search engine data. Tourism Management 46 (2015): 386–397.

    Article  Google Scholar 

  • Yang, Y., Pan, B. and Song, H. (2014) Predicting hotel demand using destination marketing organization’s web traffic data. Journal of Travel Research 53 (4): 433–447.

    Article  Google Scholar 

  • Zhou, Y.-P., Fan, M. and Cho, M. (2005) On the Threshold Purchasing Behavior of Customers Facing Dynamically Priced Perishable Products. Seattle, Washington DC: University of Washington.

    Google Scholar 

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Authors and Affiliations

  1. Virginia Polytechnic Institute, Blacksburg, USA

    Timothy Webb

  2. Delaware North Companies, Buffalo, USA

    Timothy Webb

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  1. Timothy Webb
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Correspondence to Timothy Webb.

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Webb, T. From travel agents to OTAs: How the evolution of consumer booking behavior has affected revenue management. J Revenue Pricing Manag 15, 276–282 (2016). https://doi.org/10.1057/rpm.2016.16

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  • DOI: https://doi.org/10.1057/rpm.2016.16

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