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Contributions of Web Science to Tourism Research and DevelopmentPresentation Transcript
Contributions of Web Science to eTourismResearch 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.
Tourism Consumer Behaviour Pleasure HEDONIC EntertainmentTravel as Social Activity SOCIAL Travel as Social IdentityPRE–TRAVEL TRAVEL POST–TRAVEL Preparation Physical Movement through Prolonging the Experience Space & TimeDreaming – Planning – Debriefing – Sharing – DocumentingBooking - Anticipating Reconstructing Experience
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
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 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 .894Table 3 Correlations between the metrics of DMO activity and advertising effects**. Correlation is significant at the 0.01 level (2-tailed).
General Profile Characteristic DEs Reviewers GenderProfile of Male Female 49.2 50.8 53.9 46.1 AgeDestination 18-24 25-34 1.0 14.9 3.1 26.3Experts – 35-49 50-64 65+ 42.8 35.3 6.0 42.5 25.5 2.6Emerging Location Europe 28.0 36.0 Asia 11.1 14.8Social Africa Oceania 2.2 8.6 1.7 9.9Structures 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
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. % 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
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 ofDestination 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 OnlineBehaviour? % of Fans Motivation 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 52.3the destination.I (or people I know) am/are employee(s) of the 60.4company/current or former residents of the destination
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
Influence of Online on Offline % of Online American Travelers Travel Decisions 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