Keynote delivered at the SKIMA 2012 conference in Chengdu

1. Contributions of Web
Science to eTourism
Research and Development
Dr. Ulrike Gretzel

2. Web Science Explained
• Interdisciplinary approaches and methods to
understanding the Web as a large-scale and
complex socio-technical phenomenon driven
by technical architectures, government
policies, business economics and social
interactions of billions of people
(Tinati, Halford, Carr & Pope, 2012)

3. eTourism = Big Data
• Industry Data
– Complex product descriptions
– Multimedia
– Complex industry structure
• Government Data
– Tourism statistics
• Consumer Data
– Experience documentation
– Queries, Inquiries
– Feedback
– Geospatial data

4. Challenges & Opportunities
• Dispersed – not always obvious what is tourism
and what is not
• Highly localized/context-dependent – tourism
ontologies, international sentiment
• Not routine – tourism as liminal space means
behaviours can be irrational, out of
character, time-specific, meaning relationships
are fleeting.

5. Data silos

6. Tourism Consumer Behaviour
Pleasure HEDONIC Entertainment
Travel as Social Activity SOCIAL Travel as Social Identity
PRE–TRAVEL TRAVEL POST–TRAVEL
Preparation Physical Movement through Prolonging the Experience
Space & Time
Dreaming – Planning – Debriefing – Sharing –
Documenting
Booking - Anticipating Reconstructing Experience

7. Impact of Technology
Pleasure HEDONIC Entertainment
Travel as Social Activity SOCIAL Travel as Social Identity
PRE–TRAVEL TRAVEL POST–TRAVEL
Preparation Physical Movement through Prolonging the Experience
Space & Time
Dreaming – Planning – Booking - Anticipating
Debriefing – Sharing – Reconstructing Experience
Documenting

8. The Geospatial Tourism Web

9. The Social Tourism Web

10. Social Media Developments

11. Defining the Tourism Industry
• Baggio, Scott & Cooper, 2010
• Piazzi, Baggio, Neidhardt & Werthner, 2012

12. Predicting Tourist Behaviour

13. Influencing Tourist Behaviour
EXPOSURE EFFECT PROCESSING EFFECT
ADVERTISING
DMO EFFECT Active
Verified Average Average
INPUT Followers follower
followers comment forward
rate
Pearson
Correlation .705** .789** .730** .631** .160
posts
Sig. .000 .000 .000 .001 .444
Pearson
average Correlation .773** .800** .759** .704** .082
posts
Sig. .000 .000 .000 .000 .697
ACTIVITY Pearson
Original Correlation .046 -.118 .080 .034 .037
post rate
Sig. .826 .573 .702 .873 .860
Pearson
interactive Correlation .814** .765** .870** .794** -.028
rate
Sig. .000 .000 .000 .000 .894
Table 3 Correlations between the metrics of DMO activity and advertising effects
**. Correlation is significant at the 0.01 level (2-tailed).

14. Describing Tourists’ Online Behaviour
Thumbnail
Full profile
profile
Contributions
overview

15. General
Profile Characteristic DEs
Reviewers
Gender
Profile of Male
Female
49.2
50.8
53.9
46.1
Age
Destination 18-24
25-34
1.0
14.9
3.1
26.3
Experts – 35-49
50-64
65+
42.8
35.3
6.0
42.5
25.5
2.6
Emerging Location
Europe 28.0 36.0
Asia 11.1 14.8
Social Africa
Oceania
2.2
8.6
1.7
9.9
Structures North America
Central & South America
41.6
8.5
34.5
3.1
Average length of membership 5.8 2.6
Profile picture 97.8 99.2
Age indicated 70.5 44.6
Gender indicated 86.3 49.2
Badges
No badge 20.0 22.4
Reviewer 10.3 19.1
Senior Reviewer 12.5 18.4
Contributor 19.3 16.8
Senior Contributor 22.5 16.1
Top Contributor 15.5 7.3
Compliments received 1.3 0.1

16. A Relational Perspective
• Semantic relationships among
documents/comments/concepts
• Interactions/social relationships among sources
of documents
• Influence

17. Engagement with Travel Content
• Groups: Of those respondents who have a
personal Facebook profile, 12.2% have joined a
Facebook group related to travel.
• Pages: 36.6% are fans of destinations while
21.6% have “liked” a travel-related company.
% of Respondents who have befriended a
Type of Travel Company Befriended
travel company on Facebook
Hotel 58.3
Restaurant 49.9
Airline/rental car 47.9
Attraction/theme park 37.9
Travel Agency 26.9
Museum 26.9
Travel community (e.g. Tripadvisor) 21.2
Destination marketing organization 18.7
Other 6.4

18. Relationship Status
• Rather passive:
– 71.5% have liked a post, but only 24.9% of the fans have
actually commented on a company post,
– 20.1% have actively posted something on the company
wall,
– 18.1% have downloaded an application from the company
page, and
– 15.0% have participated in a discussion.
• Active word-of-mouth is limited: while friends of the
fans will automatically see activities such as liking, only
27.4% of the fans actively shared a company post with
others and 20.1% invited others to become fans.

19. Demographic Profile of
Destination Fans
• More likely to be younger, African American and
Asian, single, and more educated than non-fans.
• More experienced Internet users.
• More active social media users and
content creators.
• Travel more frequently than non-fans.

20. What Motivates Online
Behaviour?
% of Fans
Motivation
Destination
Exclusive deal or offer 47.8
Keep informed through news for events, etc. 63.8
I am a current customer/plan to travel to the destination 71.0
Interesting or entertaining content 70.8
Customer service and support -
I would like to help promote the company/destination 53.5
Other people I know are fans of the company/destination 49.9
I feel emotionally attached 66.7
I want to show others that I am a customer/associate with 52.3
the destination.
I (or people I know) am/are employee(s) of the 60.4
company/current or former residents of the destination

21. Self-perceptions vs. Behaviour
• Destination fans are both more likely to influence
other travellers and be influenced by opinions of
others regarding travel than non-fans.
4
3.4
3 3.1 Fan
3.0
Non-Fan
2.5
2
Opion leadership Opinion seeking

22. Influence of Online on Offline
% of Online American Travelers
Travel Decisions
Decreased Same Increased
Number of places/dest. considered
Destination Fans 7.0 54.1 38.9
Others 5.6 73.7 20.7
Number of places/dest. visited
Destination Fans 6.8 58.2 35.1
Others 6.1 75.3 18.6
Amount of money spent on travel
Destination Fans 12.6 56.4 31.1
Others 12.4 72.4 15.2

23. Theoretical Implications
• A Marxist view of the Web: techno-economic base
structures cultural outcomes; hence an understanding of
the structure of the Web and its evolution is critical to
understanding eTourism.
• eTourism as a collective phenomenon: Electronic
traces of individual micro-behaviours, if aggregated on a
grand scale, can provide important insights into
behaviour and can be used to predict it.
• Social science theories important for making sense of
electronic traces

24. Methodological Implications
• Anti-disciplinary
• Mixed methods
• Need for new approaches to dealing with big
data, including extraction and storage
• Natural language processing
• Visualization

25. Travel Personalities

26. Questions?
ugretzel@uow.edu.au