Contributions of Web Science to eTourism Research and Development

Dr. Ulrike Gretzel

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)

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

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.

Data silos

Tourism Consumer Behaviour

P R E – T R A V E L T R A V E L P O S T – T R A V E L

Physical Movement through Space & Time

S O C I A L

H E D O N I C

Preparation Prolonging the Experience

Travel as Social Activity Travel as Social Identity

Pleasure Entertainment

Dreaming – Planning – Booking - Anticipating Documenting

Debriefing – Sharing – Reconstructing Experience

Impact of Technology

P R E – T R A V E L T R A V E L P O S T – T R A V E L

Physical Movement through Space & Time

S O C I A L

H E D O N I C

Preparation Prolonging the Experience

Travel as Social Activity Travel as Social Identity

Pleasure Entertainment

Dreaming – Planning – Booking - Anticipating

Documenting

Debriefing – Sharing – Reconstructing Experience

The Geospatial Tourism Web

The Social Tourism Web

Social Media Developments

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

Predicting Tourist Behaviour

Influencing Tourist BehaviourADVERTISING

DMO EFFECTINPUT

　

EXPOSURE EFFECT PROCESSING EFFECT

FollowersVerified

followersAverage

commentAverage forward

Active follower

rate

ACTIVITY

posts

Pearson Correlation .705** .789** .730** .631** .160

Sig. .000 .000 .000 .001 .444

average posts

Pearson Correlation .773** .800** .759** .704** .082

Sig. .000 .000 .000 .000 .697

Original post rate

Pearson Correlation .046 -.118 .080 .034 .037

Sig. .826 .573 .702 .873 .860

interactive rate

Pearson Correlation .814** .765** .870** .794** -.028

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).

Describing Tourists’ Online Behaviour

Profile of Destination Experts – Emerging Social Structures

Profile Characteristic DEs General Reviewers

GenderMale 49.2 53.9Female 50.8 46.1

Age18-24 1.0 3.125-34 14.9 26.335-49 42.8 42.550-64 35.3 25.565+ 6.0 2.6

LocationEurope 28.0 36.0Asia 11.1 14.8Africa 2.2 1.7Oceania 8.6 9.9North America 41.6 34.5Central & South America 8.5 3.1

Average length of membership 5.8 2.6Profile picture 97.8 99.2Age indicated 70.5 44.6Gender indicated 86.3 49.2Badges

No badge 20.0 22.4Reviewer 10.3 19.1Senior Reviewer 12.5 18.4Contributor 19.3 16.8Senior Contributor 22.5 16.1Top Contributor 15.5 7.3

Compliments received 1.3 0.1

A Relational Perspective• Semantic relationships among

documents/comments/concepts• Interactions/social relationships among sources

of documents• Influence

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.

Type of Travel Company Befriended% of Respondents who have befriended a

travel company on FacebookHotel 58.3Restaurant 49.9Airline/rental car 47.9Attraction/theme park 37.9Travel Agency 26.9Museum 26.9Travel community (e.g. Tripadvisor) 21.2Destination marketing organization 18.7Other 6.4

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.

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.

What Motivates Online Behaviour?

Motivation% of Fans

DestinationExclusive deal or offer 47.8Keep informed through news for events, etc. 63.8I am a current customer/plan to travel to the destination 71.0Interesting or entertaining content 70.8Customer service and support -I would like to help promote the company/destination 53.5Other people I know are fans of the company/destination 49.9I feel emotionally attached 66.7I want to show others that I am a customer/associate with the destination.

52.3

I (or people I know) am/are employee(s) of the company/current or former residents of the destination

60.4

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.

Opion leadership Opinion seeking2

3

4

3.1

3.4

2.5

3.0FanNon-Fan

Influence of Online on Offline

Travel Decisions% of Online American Travelers

Decreased Same IncreasedNumber of places/dest. considered Destination Fans 7.0 54.1 38.9 Others 5.6 73.7 20.7Number of places/dest. visited Destination Fans 6.8 58.2 35.1 Others 6.1 75.3 18.6Amount of money spent on travel Destination Fans 12.6 56.4 31.1 Others 12.4 72.4 15.2

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

Methodological Implications• Anti-disciplinary• Mixed methods• Need for new approaches to dealing with big

data, including extraction and storage• Natural language processing• Visualization

Travel Personalities

Questions?ugretzel@uow.edu.au