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Chapter 2

Chapter 2. The Digital Tourism Landscape. Chapter 2 Learning Objectives. After studying this chapter you should be able to: a nalyze the drivers of innovation and technological change in the digital landscape;

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Chapter 2

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  1. Chapter 2 • The Digital Tourism Landscape

  2. Chapter 2 Learning Objectives • After studying this chapter you should be able to: • analyzethe drivers of innovation and technological change in the digital landscape; • explain and evaluate the components of IT in tourism using a digital tourism ecosystem perspective; • apply concepts of tourist behaviorto explain how digital travelers use and respond to information technologies in tourism settings; • evaluate the factors that determine whether travelers will use a particular technology; • explain the role of IT in tourists’ decision-making processes; and • compare and contrast traditional and electronic tourism distribution systems.

  3. 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)

  4. FIGURE 2.1 The digital tourism ecosystem Entities Suppliers, Travelers, Intermediaries, Governments, DMOs Connections Broadband, Mobile, WiFi, NFC, BLE, GPS, Broadcasting, Protocols, Standards Devices Desktops, Smart devices, Mobile devices, Digital kiosks Communities Social networks, Blogs, Reviews, Forums, Wikis, Local experts, Media sharing Touch Points Websites, Search engines, Mobile apps, Email, Telephone, Face-to-face Content Rich media, Maps & navigation, Transactions, Dynamic content, User-generated content

  5. Digital Tourism Ecosystem • Ecosystem functions • Inspiration • Transaction • Experience • Reflection • Ecosystem health • Productivity • Resilience • Diversity

  6. Digital Tourism Ecosystem • Roles of entities and communities • Catalyzers • Dictators • Milkers • Niche players Digital technological environment • Devices • Connections • Content • Touch points

  7. Landscapedevelopments Landscape developments put pressure on existing regime, which opens up, creating windows of opportunity for novelties New regime influences landscape Market preferences Policy Culture Socio-technicalregime Industry Increasing structuration of activities in local practices Science Technology New configuration breaks through, taking advantage of “windows of opportunity”. Adjustments occur in socio-technical regime. Socio-technical regime is “dynamically stable” External influences on niches Elements become aligned and stabilize in a dominant design. Internal momentum increases. Nicheinnovations 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. Time Source: Geels(2002)

  8. FIGURE 2.3 Diffusion of innovations 100 MARKET SHARE (%) 75 50 25 TIME Early adopters 13.5% Early majority34% Latemajority34% Innovators 2.5% Laggards16% Source: Rogers (1962)

  9. SIXTHUbiquitous computingnanotechnology FIGURE 2.4 Waves of IT innovation PERFORMANCE FIFTHDigital networks FOURTH Electronics THIRDElectricity SECONDSteam power FIRSTMechanization INFORMATION REVOLUTION INDUSTRIAL REVOLUTION TIME 1780 1840 1900 1950 1990 2020 Source: Schumpeter(1934), Perez(2002)

  10. FIGURE 2.5 Gartner Hype Cycle. “Early adopters” adopt products Negative publicity Supplier proliferation EXPECTATIONS/VISIBILITY “Early majority” start to adopt product triggering high growth phase Mass-media hype New paradigms and practices become accepted Supplier failure/consolidation “Innovators” adopt products 3rd generation products 1st generation products (expensive) New rounds of venture capital 2nd generation products Startup firms R&D Peak of inflated expectations Trough of disillusionment Slope of enlightenment Plateau of productivity Technology trigger TIME Adapted from Tarkovskiy(2013)

  11. High FIGURE 2.6 Net-based Business Innovation Cycle. Assessingexternal customer & internal client value (CV) External market Value Realization Internal organization Organizational learning Low High Taking value propositions to market Executingbusiness innovation for growth (BI) Value Potential Matchingwith economic opportunities (EO) Communicating e-business initiatives COMMUNICATING Choosingenabling/emerging technologies (ET) Conveying new IT insights ET ET ET Low Time Adapted from Wheeler(2002)

  12. 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

  13. High tech and high touch travelers

  14. Performance expectancy FIGURE 2.7 Unified Theory of Acceptance and Use of Technology II (UTAUT II). Effort expectancy Socialinfluence Behavioral intention Use behavior Hedonic motivation Pricevalue • Mediating factors • Gender • Age • Experience Habit • Facilitating factors • Resources • Knowledge • Compatibility • Support Adapted from Venkatesh, et al.(2012)

  15. Understanding the Digital Tourist • Quantitative • Traditional surveys • Online surveys • Polls • Web analytics • Big data • Visitor tracking • Experiments • Qualitative • Interviews • Focus groups • Content analysis • Sentiment analysis • Netnography • Observation • Prototyping

  16. FIGURE 2.8 The traditional travel distribution system. Airlines Rail Cruise Carrental Activitiesand events Hotels SUPPLIERS Supplier reservation systems (CRS, ARS, PMS) CONNECTIONS Global distribution system (GDS) Switch Tour operator / wholesaler BOOKINGS Point of sale (POS) Call center Traditional travel agent

  17. FIGURE 2.9 The digital travel distribution system. Airlines Rail Cruise Carrental Activitiesand events Hotels SUPPLIERS Supplier reservation systems (CRS, ARS, PMS) Channel manager Switch CONNECTIONS Internet booking engine (IBE) GDS new entrants (GNE) Global distribution system (GDS) Destination management system (DMS) Travel management company (TMC) Tour operatorwholesaler BOOKINGS Call center DMO website Meta-search Online travel agent (OTA) Point of sale (POS) Supplier website Social media Mobile app Affiliate Traditional travel agent

  18. Types of Information in Travel Distribution • Descriptive information • User information • Analytical information • Transactional information

  19. Discussion Questions • 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?

  20. Discussion Questions • 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?

  21. Useful Websites

  22. 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.

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