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

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.

[1]  R. Law,et al.  A study of hotel information technology applications , 2005 .

[2]  Yi-Wen Fan,et al.  THE DECISION MAKING IN SELECTING ONLINE TRAVEL AGENCIES: AN APPLICATION OF ANALYTIC HIERARCHY PROCESS , 2009 .

[3]  Judy A. Siguaw,et al.  The Evolution of Electronic DistributionEffects on Hotels and Intermediaries , 2003 .

[4]  Bill Carroll,et al.  The Evolution of Electronic Distribution , 2003 .

[5]  John Kracht,et al.  Examining the tourism distribution channel: evolution and transformation , 2010 .

[6]  Jamie Murphy,et al.  Internet Adoption by Swiss Hotels: The Dynamics of Domain Name Registration , 2004, ENTER.

[7]  Dimitrios Buhalis,et al.  eTourism case studies : management and marketing issues , 2008 .

[8]  Peter O'Connor.  Room rates on the internet - is the web really cheaper? , 2001 .

[9]  Hannes Werthner,et al.  Information Technology and Tourism — A Challenging Relationship , 1999 .

[10]  Miriam Scaglione,et al.  Substitution Effects Across Hotel Distribution Channels , 2014, ENTER.

[11]  J. C. Fisher,et al.  A simple substitution model of technological change , 1971 .

[12]  Miriam Scaglione,et al.  Distribution channel and efficiency: An Analytic Hierarchy Process approach , 2011, ENTER.

[13]  Dimitrios Buhalis.  Information Technology for Small and Medium-Sized Tourism Enterprises: Adaptation and Benefits , 1999, J. Inf. Technol. Tour..

[14]  Gary M. Thompson.  Hotel Room Rates across Booking Channels , 2005 .

[15]  Bomi Kang,et al.  Profitability and Survivability of Hotel Distribution Channels , 2007 .

[16]  E. Rogers,et al.  Diffusion of Innovations , 1964 .

[17]  N. Meade,et al.  Modelling and forecasting the diffusion of innovation – A 25-year review , 2006 .

[18]  Charlotte H. Mason,et al.  Technical Note---Nonlinear Least Squares Estimation of New Product Diffusion Models , 1986 .

[19]  Andrew J. Frew,et al.  The future of hotel electronic distribution: expert and industry perspectives , 2002 .

[20]  Susanne Frey,et al.  Benchmarks of Web Site Design and Marketing by Swiss Hotels , 2002, J. Inf. Technol. Tour..

[21]  Miriam Scaglione,et al.  The Impact of Attribute Preferences on Adoption Timing of Hotel Distribution Channels: Are OTAs Winning the Customer Race? , 2015, ENTER.

[22]  E. Mansfield.  TECHNICAL CHANGE AND THE RATE OF IMITATION , 1961 .

[23]  R. Ford,et al.  Power asymmetries in tourism distribution networks , 2012 .

[24]  J. Keith Ord,et al.  The use of discounted least squares in technological forecasting , 1985 .

[25]  Michael Fux,et al.  Distribution Channels and Management in the Swiss Hotel Sector , 2013, ENTER.

[26]  Vijay Mahajan,et al.  Determination of Adopter Categories by Using Innovation Diffusion Models , 1990 .

[27]  Albert C. BemmaorJanghyuk Lee,et al.  Research Note The Impact of Heterogeneity and Ill-Conditioning on Diffusion Model Parameter Estimates , 2002 .

[28]  Alistair R. Anderson,et al.  ICT (information communication technology), peripherality and smaller hospitality businesses in Scotland , 2008 .

[29]  F. Bass.  A new product growth model for consumer durables , 1976 .

[30]  Rex S. Toh,et al.  Selling Rooms: Hotels vs. Third-Party Websites , 2011 .

[31]  Chris K. Anderson,et al.  Search, OTAs, and Online Booking: An Expanded Analysis of the Billboard Effect , 2011 .

[32]  S. Guercini,et al.  New business models in online hotel distribution: emerging private sales versus leading IDS , 2013 .

[33]  W. Kim,et al.  Online distribution strategies and competition: are the global hotel companies getting it right? , 2008 .

[34]  Cristian Morosan,et al.  Users’ perceptions of two types of hotel reservation Web sites , 2008 .

[35]  Andrew Harvey,et al.  Forecasting, Structural Time Series Models and the Kalman Filter , 1990 .

[36]  G. Lilien,et al.  Bias and Systematic Change in the Parameter Estimates of Macro-Level Diffusion Models , 1997 .