Presentation done by Miriam Scaglione and Roland Schegg, during "Distribution systems" workshop, of the ENTER2015 eTourism conference.

1. ENTER 2015 Research Track Slide Number 1
The impact of attribute preferences on
adoption timing of hotel distribution
channels: Are OTAs winning the customer
race?
Miriam Scaglione & Roland Schegg
HES-SO Valais, Sierre, Switzerland
miriam.scaglione@hevs.ch / roland.schegg@hevs.ch
http://www.hevs.ch

2. ENTER 2015 Research Track Slide Number 2
Agenda
• Introduction
– Hotel distribution landscape
– Goal of Study
• Past evolution of distribution channels (2002-2014)
• Simulation of future evolution
– Generations of distribution channel
– Multi-generation diffusion models
– Simulation results
• Discussion and conclusions

3. ENTER 2015 Research Track Slide Number 3
Hotels in a complex landscape of
distribution
• Online intermediaries such as OTAs play a major role in the
distribution of hotel rooms all over the world. The online
travel agencies (OTAs) had gross bookings exceeding $150
billion in 2013, representing 38% of the global online market
and 13% of the global travel market. (Trefis team, 2015)
• Hotels have underestimated in the past the importance of an
effective online marketing strategy whereas OTAs have
invested with success in online marketing and aggressive
conversion techniques (Egger & Buhalis, 2008).

4. ENTER 2015 Research Track Slide Number 4
Overall goal of study
• As there is still little research, we want to look at the
future evolution of distribution channels.
• The aim of this research is thus to gain an
understanding of the dynamics of competing sales
funnels and the adoption process in the hospitality
industry.

5. ENTER 2015 Research Track Slide Number 5
Data on evolution of distribution
channels in Swiss hotels
• Since 2003, regular surveys have been carried out
among the over 2000 members of hotelleriesuisse.
– There are «snapshots» for the reference years: 2002,
2005, 2006, 2008, 2009, 2010, 2011, 2012, 2013 and 2014
• The online questionnaire monitored how bookings
are distributed among available direct (telephone,
fax, walk-in, etc.) and indirect (tour operator,
tourism office, GDS, OTA etc.) distribution channels.

6. ENTER 2015 Research Track Slide Number 6
OTA market shares in Europe
2013
Less than 30 observations -> weak validity of mean value, less than 10 values -> no validity. For
Germany the weighted mean between individual and branded hotels is used.
Source: Schegg 2014

7. ENTER 2015 Research Track Slide Number 7
Booking channel evolution in
Swiss hotels 2002-2014
Source: Schegg 2015

8. ENTER 2015 Research Track Slide Number 8
Relative OTA market shares in
Europe 2013
Austria
Belgium
Czech
Republic
Denmark
Finland
Germany
Greece
Hungary
Italy
Norway
Spain
Switzerland
overall
sample
Weighted
European
average
observations (n) 49 56 55 50 42 1067 163 68 88 41 127 247 1736 1736
Agoda 0.2 0.0 0.3 0.0 0.0 0.2 0.5 2.1 1.2 0.2 1.4 0.4 0.5 0.9
Booking.com 66.8 70.2 65.0 58.1 64.6 41.6 73.3 55.7 63.0 72.7 66.2 69.7 59.5 60.9
Priceline 67.0 70.2 65.2 58.2 64.6 41.8 73.8 57.8 64.2 72.9 67.6 70.1 60.0 61.8
HRS 7.9 3.6 8.6 4.7 1.9 31.1 0.5 6.1 3.7 2.3 2.0 6.7 11.9 8.9
Hotel.de 4.0 1.1 5.0 1.7 0.6 10.4 0.7 2.5 1.8 1.4 1.5 3.0 4.6 3.8
Tiscover 3.4 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.1 0.2 0.2
HRS (total) 15.3 4.7 13.5 6.4 2.5 41.6 1.2 8.6 5.5 3.7 3.5 9.8 16.6 12.9
Expedia 5.5 6.6 8.3 8.9 2.5 5.8 8.8 8.3 15.0 8.5 10.9 6.3 7.5 9.2
Venere 2.1 1.6 3.5 1.6 0.5 1.1 3.7 1.6 6.3 0.4 3.9 2.6 2.4 3.3
Hotels.com 0.3 0.6 1.5 9.1 2.2 1.0 2.8 1.1 1.8 5.7 1.1 1.0 1.8 1.6
Expedia (total) 7.9 8.9 13.2 19.7 5.2 7.9 15.2 11.0 23.2 14.5 16.0 9.9 11.8 14.0
Total of top 3 90.2 83.7 91.9 84.2 72.3 91.4 90.2 77.3 92.8 91.1 87.1 89.8 88.3 88.8
Source: Schegg 2014

9. ENTER 2015 Research Track Slide Number 9
Kracht & Wang (2010):
Model for Evolution of
Distribution Channels (I)
• The first generation channels emerged in the pre-World-Wide-
Web era, before 1993 and are composed of traditional retail
and traditional TA/TO, GDS, incoming travel agents, switches,
destination marketing and DMOs and suppliers

10. ENTER 2015 Research Track Slide Number 10
Kracht & Wang Model (II)
• The 2. generation channels developed after WWW had been
made freely available in 1993. Suppliers began to connect
directly with customers through web-mediated channels and
thus began the disintermediation of traditional intermediaries.
-> the importance of new direct channels such as e-mail etc.

11. ENTER 2015 Research Track Slide Number 11
Kracht & Wang Model (III)
• The third generation channels: slightly after the time that
suppliers started disintermediating traditional intermediaries,
another layer of intermediation began to develop based on the
growing importance of internet search engines such as Google ->
Online Travel Agencies (OTAs)

12. ENTER 2015 Research Track Slide Number 12
Methodological approach
Based on the distribution channel typology of Kracht & Wang
(2010), we have aggregated the individual channels in the following
way in order to analyse the evolution of market shares of
successive distribution channel generations:
•G1 - Generation 1 (traditional channels): Telephone, fax, letter,
travel agency, tour operator, DMO (local, regional or Swiss Tourism),
conference organizers, CRS of hotel chain or franchisee, GDS, others.
•G2 - Generation 2 (online direct channels) : E-mail, reservation
form on website, real-time booking on the property website.
•G3 - Generation 3 (new online intermediaries) : OTA, social media
channel.

13. ENTER 2015 Research Track Slide Number 13
Multi-generation diffusion models
• The pioneer work by Rogers (2003) yields an
appropriate frame for the analysis of the perceived
attributes of innovations on the one hand (relative
advantage, compatibility, complexity, trialability and
observability), and on the other hand the influence
that these latter attributes have on the rate of
adoption (Islam, 2014).
• Diffusion models based on multi-generations models
give an estimation of the rate of adoption and timing
and allow to characterize the dynamics of the
adoption process.

14. ENTER 2015 Research Track Slide Number 14
Norton & Bass (1987,1992)
( ) ( )
1 exp( ( ) )
( )
1 ( / )exp(-(p+q)t)
X t mF t
p q t
F t
q p
=
− − +
=
+
Single generation diffusion model Bass(1966)
( ) is number of adopters at time
M is potential number of adopters
( ) is cumulative proportion of adopter time
X t t
F t t
Norton & Bass model of successive generations
( ) ( )i i iX t m F t=
( ) is number of adopters of the generation at time
M is potential number of adopters for the generation
( ) is cumulative proportion of adopters of the generation time ;generaration int
i
i
i
X t i t
i
F t i t i
( ) =0 for t<
roduced at time i
i i iF t τ τ
τ
−
{ }
1 1 1 2 2
2 2 2 2 1 1 3 3
3 3 3 3 2 2 2 1 1
( ) ( ) [1 ( )]
( ) ( )[ ( ) ][1 ( )]
( ) ( ) ( )[ ( ) ]
X t F t M F t
X t F t M F t M F t
X t F t M F t M F t M
τ
τ τ
τ τ
= − −
= − + − −
= − + − +

15. ENTER 2015 Research Track Slide Number 15
Norton & Bass (cont’)
1 exp( )
( ) , where and
1 ( )exp( )
i i
i i i i i
i i i
a t q
F t a p q b
b a t p
− −
= = + =
+ −
Norton & Bass model of successive generations
Restricted Norton & Bass
Strong assumption
Unrestricted Norton & Bass
Assumption: adopters’
behavior does not change
across generation.
Total number of param. to be
estimated = 3(#gen.)+2 (p&q)=5
Assumption: adopters’
behavior change across
generation.
Total number of param. to be
estimated = 3(#gen.)*2(p&q)
+3(#gen)=9
,i ip p q q i= = ∀ : ori j i ji j p p q q∃ ∃ ≠ ≠

16. ENTER 2015 Research Track Slide Number 16
NB parameters for restricted and
unrestricted models
(***p-value<0.01, **<0.05, *<0.1)
Mi is the incremental potential number of
adopters served by generation i.

17. ENTER 2015 Research Track Slide Number 17
Simulation Results
R=restricted
UR=unrestricted

18. ENTER 2015 Research Track Slide Number 18
Remodelling with G1+G2 as first generation and
new data point for year 2014

19. ENTER 2015 Research Track Slide Number 19
Remodelling with G1+G2 as first generation and
new data point for year 2014
Model with new
data for 2014
(Schegg, 2015)

20. ENTER 2015 Research Track Slide Number 20
However (limitations!)
• Ceteris paribus, the third generation of channels will reach
half of all bookings between 2019 and 2021 and they will in
the long run dominate the distribution landscape.
• Yet, the long-term forecast has to be taken with caution, as
this is just a theoretical trend, which does not take into
account the rise of possible forthcoming generations of
distribution channels (i.e. Alibaba, Amazon).
• It does, however, give some evidence of the growing
domination of the last generation over the two previous
ones.

21. ENTER 2015 Research Track Slide Number 21
• The present research shows that the adoption model for G1
(trad. channels) and G2 (online direct) seems to be driven
only / mostly by innovation (or external influence).
• The results suggest that traditional and web-based direct
channels, which are both decaying, have low or inexistent
imitation effect.
• The launch of G3 by the OTAs has probably affected the
diffusion dynamics of G2 by reducing the driving effect of
imitation.
Discussion and Conclusions

22. ENTER 2015 Research Track Slide Number 22
• This research also shows that the decline of the share is
greater, in absolute values, for G1 (p1=1) than for G2
(p2=0.049) and that the second generation’s growth rate is
less than 6 times smaller than the third generation.
• This is suggesting the presence of a leapfrogger effect
(Scaglione, et al., 2010): customer which used traditional
channels switched to the OTA channel without having used
the direct online channels.
• The perceived attributes of trialability and observability
claimed by Rogers seem to ground this result.
Discussion and Conclusions

23. ENTER 2015 Research Track Slide Number 23
• The foreseen very high market share of OTAs is therefore a
serious threat for the Swiss lodging sector.
– Loose of control on distribution, customer data (CRM),
costs (!)
• Other challengers to come…
– New intermediaries (Amazon, Alibaba, …)
– New kids on the block (AirBnB)
Implications/Challenges

24. ENTER 2015 Research Track Slide Number 24
• “The mobile channel has successfully overcome all doubts
about its ability to become significant for the travel industry,
reaching an estimated 15% of global online travel sales in
2014.
• The rise of personalization: All big players in travel are
currently working on it in order to increase conversions and
offer a better consumer experience, which will become a key
competitive factor in the next few years.”
Driving forces of travel
distribution in the next few years
Source: Angelo Rossini 2015

25. ENTER 2015 Research Track Slide Number 25
• “An innovation with disruptive potential in the travel industry
is the one introduced by TripAdvisor through its Instant
Booking tool. This new model will expand TripAdvisor’s
functionalities beyond a travel review website, as TripAdvisor
starts offering services like other established OTAs.
• The rise of new players: Travel is the largest category for
ecommerce globally, and it is not surprising that the two
largest global online retailers, Amazon and Alibaba, have
decided to enter it.”
Blurring competitive landscape
Source: Angelo Rossini
2015

26. ENTER 2015 Research Track Slide Number 26
Challenges to come
Accor 2014: “Leading
Digital Hospitality”

27. ENTER 2015 Research Track Slide Number 27
Importance of AirBnB in Switzerland
Over 6’000 objects with over 20’000 beds
-> equivalent to 8% of the hotel bed offer in Switzerland!
http://www.tourobs.ch/media/303683/Airbnb_DE.pdf

28. ENTER 2015 Research Track Slide Number 28
Contact
Miriam Scaglione & Roland Schegg
University of Applied Sciences of Western Switzerland Valais (HES-SO Valais)
School of Management & Tourism
Institute of Tourism (ITO)
TechnoPôle 3
CH-3960 Sierre/Siders, Switzerland
Mail: miriam.scaglione@hevs.ch / roland.schegg@hevs.ch
Web: www.hevs.ch / www.etourism-monitor.ch
Bachelor of Science HES-SO in Tourism in German, French and English
http://tourism.hevs.ch