CABI TOURISM TEXTS
2nd Edition
Tourism Information
Technology
PIERRE J. BENCKENDORFF
PAULINE J. SHELDON
DANIEL R. FESENMAIER
COMPLIMENTARY TEACHING
MATERIALS
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Chapter 2
The Digital Tourism Landscape
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Chapter 2 Learning Objectives
After studying this chapter you should be able to:
1. analyze the drivers of innovation and technological change in the
digital landscape;
2. explain and evaluate the components of IT in tourism using a
digital tourism ecosystem perspective;
3. apply concepts of tourist behavior to explain how digital travelers
use and respond to information technologies in tourism settings;
4. evaluate the factors that determine whether travelers will use a
particular technology;
5. explain the role of IT in tourists’ decision-making processes; and
6. compare and contrast traditional and electronic tourism
distribution systems.
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Key Concepts
 Computer anxiety, computer phobia and technophobia
 Diffusion of Innovations Theory
 Digital tourism ecosystem
 Hype Cycle
 Multi-level perspective (MLP)
 Technological Innovation Theory
 Technology Acceptance Model (TAM)
 Unified Theory of Acceptance and Use of Technology
(UTAUT)
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Inspiration
Entities
Connections
Reflection
Broadband,
Mobile, WiFi,
NFC, BLE, GPS,
Broadcasting,
Protocols,
Standards
Content
Rich media,
Maps &
navigation,
Transactions,
Dynamic content,
User-generated
content
Suppliers,
Travelers,
Intermediaries,
Governments,
DMOs
Communities
Social networks,
Blogs, Reviews,
Forums, Wikis,
Local experts,
Media sharing
Devices
Desktops, Smart
devices, Mobile
devices, Digital
kiosks
Transaction
Touch Points
Websites,
Search engines,
Mobile apps,
Email, Telephone,
Face-to-face
Experience
FIGURE 2.1 The digital tourism ecosystem
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Digital Tourism Ecosystem
Ecosystem functions
 Inspiration
 Transaction
 Experience
 Reflection
Ecosystem health
 Productivity
 Resilience
 Diversity
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Digital Tourism Ecosystem
Roles of entities and communities
 Catalyzers
 Dictators
 Milkers
 Niche players
Digital technological environment
 Devices
 Connections
 Content
 Touch points
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Increasing structuration of activities in local practices
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Landscape
developments
Landscape developments put pressure on
existing regime, which opens up, creating
windows of opportunity for novelties
Market preferences
New regime
influences landscape
Policy
Culture
Socio-technical
regime
Industry
Technology
Science
Socio-technical regime
is “dynamically stable”
External
influences
on niches
New configuration breaks through, taking
advantage of “windows of opportunity”.
Adjustments occur in socio-technical regime.
Elements become aligned and stabilize in a
dominant design. Internal momentum
increases.
Small networks of actors support novelties on the basis of expectations and
visions. Learning processes take place on multiple dimensions (co-construction).
Efforts to link different elements in a seamless web.
Niche
innovations
Time
Source: Geels (2002)
MARKET SHARE (%)
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100
75
50
25
TIME
Innovators
2.5%
Early
adopters
13.5%
Early
majority
34%
Late
majority
34%
Laggards
16%
FIGURE 2.3 Diffusion of innovations
Source: Rogers (1962)
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PERFORMANCE
SIXTH
Ubiquitous computing
nanotechnology
FIFTH
Digital
networks
FOURTH
Electronics
SECOND
Steam power
THIRD
Electricity
FIRST
Mechanization
1780
INDUSTRIAL REVOLUTION
1840
1900
INFORMATION REVOLUTION
1950
1990
2020
TIME
FIGURE 2.4 Waves of IT innovation
Source: Schumpeter (1934), Perez (2002)
EXPECTATIONS/VISIBILITY
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“Early adopters”
adopt products
Supplier proliferation
Negative publicity
“Early majority” start to
adopt product triggering
high growth phase
Mass-media hype
Supplier
failure/consolidation
“Innovators” adopt products
1st generation
products (expensive)
New paradigms and
practices become accepted
3rd generation
products
New rounds of
venture capital
2nd generation
products
Startup firms
R&D
Technology
trigger
Peak of
inflated
expectations
Trough of
disillusionment
Slope of
enlightenment
Plateau of
productivity
TIME
FIGURE 2.5 Gartner Hype Cycle.
Adapted from Tarkovskiy (2013)
Value Realization
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High
Assessing
External market
external customer
& internal client
value (CV)
Internal organization
Organizational learning
Low
High
Taking value
propositions to
market
Value Potential
Executing
business innovation for
growth (BI)
Matching
with economic
opportunities (EO)
Choosing
Low
enabling/emerging
technologies (ET)
Conveying new IT
insights
Communicating
e-business initiatives
COMMUNICATING
ET
ET
ET
Time
FIGURE 2.6 Net-based Business Innovation Cycle.
Adapted from Wheeler (2002)
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Understanding the Digital Tourist
Aspects of IT and behavior:
 Technology use and acceptance
 demographics
 trip characteristics
 psychographics
 UTAUT
 Decision-making
 information search
 trip planning
 purchase
 Information sharing
 Co-creation of experiences
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High tech and high touch travelers
Travel
Everyday life
High tech
High touch
High tech
High touch
Spillovers
Opportunity seekers
Compensators
Luddites
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Performance expectancy
Effort
expectancy
Social
influence
Hedonic
motivation
Price
value
Habit
Behavioral intention
Use
behavior
Mediating factors
• Gender
• Age
• Experience
Facilitating factors
• Resources
• Knowledge
• Compatibility
• Support
FIGURE 2.7 Unified Theory of Acceptance and Use of Technology II (UTAUT II).
Adapted from Venkatesh, et al. (2012)
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Understanding the Digital Tourist
Quantitative
Qualitative














Traditional surveys
Online surveys
Polls
Web analytics
Big data
Visitor tracking
Experiments
Interviews
Focus groups
Content analysis
Sentiment analysis
Netnography
Observation
Prototyping
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SUPPLIERS
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Airlines
Rail
Cruise
Car
rental
Activities
and events
Hotels
BOOKINGS
CONNECTIONS
Supplier reservation systems (CRS, ARS, PMS)
Global distribution system
(GDS)
Switch
Tour operator / wholesaler
Point of sale (POS)
Call center
Traditional travel agent
FIGURE 2.8 The traditional travel distribution system.
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SUPPLIERS
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Airlines
Rail
Car
rental
Cruise
Activities
and events
Hotels
BOOKINGS
CONNECTIONS
Supplier reservation systems (CRS, ARS, PMS)
Channel
manager
Internet
booking
engine (IBE)
GDS new
entrants
(GNE)
Switch
Travel
management
company (TMC)
Tour
operator
wholesaler
Call
center
Point of sale
(POS)
Online travel agent
(OTA)
Supplier
website
Social
media
Destination
management system
(DMS)
Global
distribution
system (GDS)
Mobile
app
Metasearch
Affiliate
DMO
website
Traditional travel
agent
FIGURE 2.9 The digital travel distribution system.
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Types of Information in Travel Distribution
 Descriptive information
 User information
 Analytical information
 Transactional information
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Discussion Questions
1.
2.
3.
In 2007 the World Economic Forum released three scenarios of
digital ecosystems, which are summarized in the following
YouTube video: http://youtu.be/jnrAtXt3uu4. Considering IT
developments since 2007, which one has been the most
accurate? Justify your answer and discuss the implications for IT
and tourism.
Which of the innovation models presented in this chapter are
most relevant to the tourism industry? Explain why.
Conduct your own research about the major generational cohorts
alive today (Baby Boomers, Gen X, Gen Y and Gen Z). Do they
differ in how they use technologies? Are older consumers as
likely to use IT for travel purposes as younger consumers?
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Discussion Questions
4.
5.
6.
Provide examples of how the use of technology can deliver both
high-tech and high-touch outcomes.
What are the key elements of the Unified Theory of Acceptance
and Use of Technology II (UTAUT II). Provide your own tourism
and technology example to illustrate the various components of
this model.
What challenges do small and medium tourism enterprises
(SMTEs) face in travel distribution? How might SMTEs respond
to the increasingly complex structure of the digital tourism
distribution system?
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Useful Websites
American Society of Travel Agents (ASTA)
http://www.asta.org/
Mygola
http://www.mygola.com/
World Economic Forum Digital Ecosystems
http://www.weforum.org/reports/digital-ecosystem-convergencebetween-it-telecoms-media-and-entertainment-scenarios-2015
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Case Study Mygola
 Online trip planning tool that aims to answer the following questions:
 What should I see?
 How do I get there?
 What should I look out for?
 Over 5000 curated itineraries.
 Use of text-mining software to parse travel articles and extract the
structure of a trip.
 Algorithms also mine other information such as opening hours and
travel distances between sites.
 Visually stunning images and videos are sourced to match itinerary.
 Users can customize itineraries by selecting interests, which are fed
back to create a rich ‘big data’ source for refining the predictive
power of the platform.