Skip to main content

Forecasting the Final Penetration Rate of Online Travel Agencies in Different Hotel Segments

  • Conference paper
  • First Online:
Information and Communication Technologies in Tourism 2016
  • 5152 Accesses

Abstract

This research uses data on distribution channels of hotels gathered through a yearly survey addressed to Swiss hotels since 2006. The authors use the evolution of Online Travel Agencies (OTAs) market share as a time series which can be modelled using different growth curve methods. These various models cross-validate the forecasted final penetration rate. The study analyses the dynamics of the evolution of OTAs and determines their final penetration rate not only on an overall level, but also segmented by hotel category, location and size. Overall, a final penetration of around 35 % is predicted by our models, but they show also that the level of final penetration of OTAs depends on the typology of the hotel. The paper sheds some light on the statistical difficulties in forecasting with a limited set of data and gives insights into the future evolution of the distribution mix which is essential for the marketing and pricing strategy of hotels.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Anderson, C. K. (2011). Search, OTAs, and online booking: An expanded analysis of the billboard effect. Cornell Hospitality Report, 11(8), 14.

    Google Scholar 

  • Bass, F. M. (1969). A new product growth model for consumer durables. Management Science, 15(5), 215–227.

    Article  Google Scholar 

  • Bass, F. M., Jain, D., & Khishnan, T. (2000). Modeling the marketing-mix influence in new-product diffusion. In V. Mahajan, E. Muller, & Y. Wind (Eds.), New-product diffusion models. Boston, MA: Kluwer Academic.

    Google Scholar 

  • Bemmaor, A. C., & Lee, J. (2002). The impact of heterogeneity and ill-conditioning on diffusion model parameter estimates. Marketing Science, 21(2), 209–220.

    Article  Google Scholar 

  • Buhalis, D. (1999). Information Technology for small and medium-sized tourism enterprises: Adaptation and benefits. Journal of Information Technology & Tourism, 2(2), 79–95. Retrieved from http://www.sciencedirect.com/science/article/pii/S0261517799000953.

    Google Scholar 

  • Buhalis, D. (2003). ETourism: Information technology for strategic tourism management. Harlow: Financial Times Prentice Hall.

    Google Scholar 

  • Caroll, B., & Siguaw, J. (2003). The evolution of electronic distribution. Cornell Hotel and Restaurant Administration Quarterly, 44(4), 38–50.

    Article  Google Scholar 

  • Egger, R., & Buhalis, D. (2008). ETourism case studies: Management and marketing issues. Oxford: Butterworth-Heinemann.

    Google Scholar 

  • Euromonitor. (2014). The new online travel consumer – Featuring euromonitor international and the ETOA. Retrieved from http://aggregator.us/THE-NEW-ONLINE-TRAVEL-CONSUMER-ETOA-European.html

  • Fisher, J. C., & Pry, R. H. (1971). A simple substitution model of technological change. Technological Forecasting and Social Change, 3, 75–88. doi:10.1016/S0040-1625(71)80005-7.

    Article  Google Scholar 

  • Ford, R. C., Wang, Y., & Vestal, A. (2012). Power asymmetries in tourism distribution networks. Annals of Tourism Research, 39(2), 755–779. doi:10.1016/j.annals.2011.10.001.

    Article  Google Scholar 

  • Gazzoli, G., Kim, W. G., & Palakurthi, R. (2008). Online distribution strategies and competition: Are the global hotel companies getting it right? International Journal of Contemporary Hospitality Management, 20(4), 375–387. doi:10.1108/09596110810873499.

    Article  Google Scholar 

  • Harvey, A. C. (1990). Forecasting, structural time series models and the Kalman filter. Cambridge: Cambridge university press.

    Book  Google Scholar 

  • Irvine, W., & Anderson, A. R. (2008). ICT (Information communication technology), peripherality and smaller hospitality business in Scotland. International Journal of Entrepreneurial Behaviour & Research, 14(4), 200–218.

    Article  Google Scholar 

  • Kang, B., Brewer, K. P., & Baloglu, S. (2007). Profitability and survivability of hotel distribution channels. Journal of Travel & Tourism Marketing, 22(1), 37–50. doi:10.1300/J073v22n01_03.

    Article  Google Scholar 

  • Kracht, J., & Wang, Y. (2010). Examining the tourism distribution channel: Evolution and transformation. International Journal of Contemporary Hospitality Management, 22(4–5), 736–757.

    Article  Google Scholar 

  • Ku, E. C., & Fan, Y. W. (2009). The decision making in selecting online travel agencies: An application of analytic hierarchy process. Journal of Travel and Tourism Marketing, 26(5–6), 482–493.

    Article  Google Scholar 

  • Law, R., & Jogaratnam, G. (2005). A study of hotel information technology applications. International Journal of Contemporary Hospitality Management, 17(2), 170–180.

    Article  Google Scholar 

  • Mahajan, V., Muller, E., & Srivastava, R. (1990, February). Determination of adopter categories by using innovation diffusion models. Journal of Marketing Research, XXVII, 37–50.

    Google Scholar 

  • Mansfield, E. (1961). Technical change and the rate of imitation. Econometrica, 29(4), 741–766.

    Article  Google Scholar 

  • Meade, N., & Islam, T. (2006). Modelling and forecasting the diffusion of innovation-A 25-year review. International Journal of Forecasting, 22(3), 519–545.

    Article  Google Scholar 

  • Morosan, C., & Jeong, M. (2008). Users’ perceptions of two types of hotel reservation Web sites. International Journal of Hospitality Management, 27(2), 284–292.

    Article  Google Scholar 

  • O’Connor, P. (2001). Room rates on the internet – Is the web really cheaper? Journal of Services Research, 1(1), 57–72.

    Google Scholar 

  • O’Connor, P., & Frew, A. J. (2002). The future of hotel electronic distribution: Expert and industry perspectives. Cornell Hotel and Restaurant Administration Quarterly, 43(3), 33–45. http://doi.org/10.1016/S0010-8804(02)80016-7.

    Article  Google Scholar 

  • Perruchoud-Massy, M.-F., Scaglione, M., Schegg, R., & Murphy, J. (2005). Adoption of innovation by Swiss hotels: Exploring internet strategies and dynamics. In P. Keller & T. Bieger (Eds.), Innovation in tourism: Creating customer value/AIEST, 55th congress 2005, Brainerd, USA (Vol. 67, pp. 171–185). St. Gallen: International Association of Scientific Experts in Tourism.

    Google Scholar 

  • PhoCusWright. (2014). Europe’s meta game: Update on evolution of travel search. Retrieved from http://www.phocuswright.com/Travel-Research/Social-Search/Europe-s-Meta-Game-Update-on-Evolution-of-Travel-Search

  • Rogers, E. M. (1962). Diffusion of innovations (1st ed.). New York: The Free Press.

    Google Scholar 

  • Rossini, A. (2015). OTA sector between increasing consolidation and the possible rise of new key players. Retrieved from http://blog.euromonitor.com/2015/06/ota-sector-between-increasing-consolidation-and-the-possible-rise-of-new-key-players.html

  • Runfola, A., Rosati, M., & Guercini, S. (2013). New business models in online hotel distribution: Emerging private sales versus leading IDS. Service Business, 7(2), 183–205. doi:10.1007/s11628-012-0150-1.

    Article  Google Scholar 

  • SAS Institute Inc. (2011). SAS/STAT® 9.22 user’s guide. Cary: SAS Institute Inc.

    Google Scholar 

  • Scaglione, M., & Schegg, R. (2015). The impact of attribute preferences on adoption timing of hotel distribution channels: Are OTAs winning the customer race? In I. Tussyadiah & A. Inversini (Eds.), Information and communication technologies in tourism 2015 (pp. 681–693). Cham: Springer.

    Google Scholar 

  • Scaglione, M., Schegg, R., Steiner, T., & Murphy, J. (2004a). The diffusion of domain names by small and medium-sized Swiss hotels. In P. Keller & T. Bieger (Eds.), The future of small and medium sized enterprises in tourism 54th AIEST congress (Vol. 46, pp. 259–271). St. Gallen: International Association of Scientific Experts in Tourism.

    Google Scholar 

  • Scaglione, M., Schegg, R., Steiner, T., & Murphy, J. (2004b). Internet adoption by Swiss hotels: The dynamics of domain name registration. In A. J. Frew (Ed.), Proceedings of the 11th international conference on information technologies in tourism, Cairo, Egypt (pp. 479–488). New York: Springer.

    Google Scholar 

  • Schaal, D. (2015). And then the earth shook: Google enters travel booking. Retrieved from http://skift.com/2015/07/13/and-then-the-earth-shook-google-enters-travel-booking/

  • Schegg, R. (2014). European hotel distribution study: The rise of online intermediaries. Special Focus Switzerland. Retrieved from http://etourism-monitor.ch/node/129

  • Schegg, R. (2015). Swiss hotel distribution study: Are OTAs winning the customer race? Retrieved from http://etourism-monitor.ch/node/135

  • Schegg, R. & Fux, M. (2010). Die Bedeutung des Online-Vertriebs in der Schweizer Hotellerie. Jahrbuch der Schweizer Hotellerie, 101–104.

    Google Scholar 

  • Schegg, R., & Scaglione, M. (2013). Substitution effects across hotel distribution channels. In Z. Xiang & I. Tussyadiah (Eds.), Information and communication technologies in tourism 2014 (pp. 801–812). Cham: Springer.

    Chapter  Google Scholar 

  • Schegg, R., Stangl, B., Fux, M., & Inversini, A. (2013). Distribution channels and management in the Swiss hotel sector. In L. Cantoni & Z. Xiang (Eds.), Information and communication technologies in tourism 2013 (pp. 554–565). Berlin: Springer.

    Chapter  Google Scholar 

  • Schegg, R., Steiner, T., Frey, S., & Murphy, J. (2002). Benchmarks of Web site design and marketing by Swiss hotels. Information Technology & Tourism, 5(2), 73–89.

    Article  Google Scholar 

  • Srinivasan, V., & Mason, C. H. (1986). Technical note – Nonlinear least squares estimation of new product diffusion models. Marketing Science, 5(2), 169–178. doi:10.1287/mksc.5.2.169.

    Article  Google Scholar 

  • Thompson, G. (2005). Hotel room rates across booking channels. Cornell Hotel and Restaurent Administration Quarterly, 46(2), 106–107.

    Article  Google Scholar 

  • Toh, R. S., Raven, P., & DeKay, F. (2011). Selling rooms: Hotels vs. third-party websites. Cornell Hospitality Quarterly, 52(2), 181–189.

    Article  Google Scholar 

  • Van den Bulte, C., & Lilien, G. L. (1997). Bias and systematic change in the parameter estimates of macro-level diffusion models. Marketing Science, 16(4), 338–353. doi:10.1287/mksc.16.4.338.

    Article  Google Scholar 

  • Varini, K., Scaglione, M., & Schegg, R. (2011). Distribution channel and efficiency: An analytic hierarchy process approach. In R. Law, M. Fuchs, & F. Ricci (Eds.), Information and communication technologies in tourism 2011: Proceedings of the international conference in Innsbruck, Austria, January 26–28, 2011. Wien/New York: Springer.

    Google Scholar 

  • Werthner, H., & Klein, S. (1999). Information technology and tourism – A challenging relationship (1st ed.). Vienna: Springer.

    Book  Google Scholar 

  • Young, P., & Ord, K. (1985). The use of discounted least squares in technological forecasting. Technological Forecasting and Social Change, 28(3), 263–274. doi:10.1016/0040-1625(85)90048-4.

    Article  Google Scholar 

Download references

Acknowledgements

A first draft of this paper was presented at the 2013 conference of the International Association of Scientific Experts in Tourism (AIEST) (Izmir-Turkey, 25–29.8.2013). The authors thank the participants for the comments and suggestions.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Miriam Scaglione .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Scaglione, M., Schegg, R. (2016). Forecasting the Final Penetration Rate of Online Travel Agencies in Different Hotel Segments. In: Inversini, A., Schegg, R. (eds) Information and Communication Technologies in Tourism 2016. Springer, Cham. https://doi.org/10.1007/978-3-319-28231-2_51

Download citation

Publish with us

Policies and ethics