Amazon

, 35

Applications of AI in tourism and hospitality industry

, 36

Artificial Intelligence (AI)

, 32–33, 35, 47–48, 82–83, 120, 122–123, 135

AI-based chatbots

, 38

AI-based forecasting system

, 38

AI-driven prediction tools

, 36

AI-enhanced hotel

, 36

applications in sectors

, 35

applications of AI and robotics in global tourism industry

, 38–39

applications of AI in tourism and hospitality industry

, 36

assistants

, 36

challenges to adopt AI in Indian tourism industry

, 40

challenges to adopt AI-based applications in tourism industry

, 36–37

components of

, 34–35

facial recognition

, 39

findings of study

, 38–40

future possibilities of AI applications in tourism industry

, 39–40

literature review

, 33–37

real-time feedback

, 39

research methodology

, 37–38

smart forecasting

, 38

Artificial Intelligence for Drug Discovery (AIDD)

, 35

Asian Development Bank (ADB)

, 143

Augmented reality (AR). (see also Virtual reality (VR))

, 51, 87, 96, 99, 124–125, 127–128, 135

adoptability of AR in hospitality industry

, 101

in hotel industry

, 97–98

Authenticity, re-creation of

, 126

Average Variance Extracted (AVE)

, 8

B-Chabot marketing

, 128

Bangladesh

, 2–3

residents of

, 5

Banking and Financial Services sector

, 35

Big Data (BD)

, 45–46, 51–52, 121, 134–136, 141–142

analytics

, 46, 139–140

big data in sustainable tourism

, 50–52

data collection and scope of research

, 53

findings

, 56

industry 4.0 and

, 47–48

institutions promoting big data for sustainable development

, 142–144

limitations and future research

, 74

location-aware technologies

, 51

method

, 52–55

practical implications

, 73

resources

, 50

rise of big data in tourism sector

, 138

selection of articles and analysis process

, 53–55

sustainable management with big data

, 48–50

technology

, 45–46

theoretical background

, 47–52

transactions

, 50–51

Block chain

, 123

Brundtland Report

, 110

Business

, 50

process

, 40

Business and leisure guests in hotel industry

, 100

Business intelligence (BI)

, 50

Central Government’s Destination Management Organisation

, 111

Chat robot (Chatbot)

, 36, 38, 122

China’s economy

, 21

Cloud computing

, 51, 123–124

Cloud-based technologies

, 124

Cobb-Douglas function

, 21

Collaboration platform

, 114

Common method bias (CMB)

, 6–7

Composite Reliability (CR)

, 8

Computers

, 87

Conceptual research

, 136

Construct reliability and validity

, 8

Correlation analysis

, 24–25

COVID-19 pandemic

, 84–85, 87, 108–109

virus

, 40

Cronbach’s Alpha (Cα)

, 8

Cutting-edge technologies

, 107–108

Cyber-physical systems (CPS)

, 135

Data

, 50–51

exhaust

, 135

mining

, 35

Data science

, 134–136, 141–142

institutions promoting data science for sustainable development

, 142–144

Decision-making process

, 48

Destination management organisations (DMOs)

, 108–109, 140–141

Destination management systems (DMS)

, 82–83

Destinations marketing

, 84

Digital learning system

, 35–36

Digital technologies

, 107, 133–134, 142–143

Digitalisation

, 138–139

Discriminant validity

, 8–9

E-commerce

applications

, 82–83

giants

, 35

e-Marketing

, 96

East Asia

, 19–20

Economic growth, effect of tourism and technological contribution on

correlation analysis

, 24–25

data collection and methodology

, 23–24

descriptive statistics

, 24

literature review

, 21–23

regression equation

, 24

regression results

, 24–25, 28

technological contribution and economic growth

, 21–22

tourism and economic growth

, 22–23

Economic interdependence

, 20

Eco–tourist destination

, 5

assessment of structural model

, 9–11

construct reliability and validity

, 8

data analysis and results

, 8–11

discriminant validity

, 8–9

implications, limitations

, 11–12

literature review and hypotheses development

, 3–5

methodology

, 5–7

tourists visit intention

, 3

Vlogger attractiveness

, 4–5

Vlogger credibility

, 3

Vlogger expertise

, 4

Vlogger trustworthiness

, 3–4

Education

, 35–36

Electronic word-of-mouth (e-WOM)

, 127

European Tourism Indicators System (ETIS)

, 143–144

Expertise

, 4

Extended reality (XR)

, 87

Facial recognition technologies

, 39

Feasible least squares (FGLS)

, 20

Financial development

, 23–24

Flipkart

, 35

Forecasting

, 46–47

software

, 82–83

Fornell and Larcker Criterion (FL criterion)

, 8–9

Fourth Industrial Revolution, The

, 47

Futuristic perspective of AI

AI-based chatbots

, 38

applications in sectors

, 35

applications of AI and robotics in global tourism industry

, 38–39

applications of AI in tourism and hospitality industry

, 36

challenges to adopt AI in Indian tourism industry

, 40

challenges to adopt AI-based applications in tourism industry

, 36–37

components of

, 34–35

facial recognition

, 39

findings of study

, 38–40

future possibilities of AI applications in tourism industry

, 39–40

literature review

, 33–37

overview of

, 33–35

real-time feedback

, 39

research methodology

, 37–38

smart forecasting

, 38

Geographic information systems (GIS)

, 82–83

Global tourism industry, current applications of AI and robotics in

, 38–39

GMM method

, 22–23

Government organisations

, 140–141

Green Economy Report, The

, 81–82

Grounded theory approach

, 137–138

Growth, positive effects of tourism on

, 20

Growth decomposition method (GDM)

, 23

Healthcare industry

, 35

Heterotrait-Monotrait Ratio of Correlations (HTMT)

, 8–9

High-performance computing (HPC)

, 123–124

Hilton Hotel Group

, 109–110

Hopper (AI-driven prediction tools)

, 36

Hospitality

, 2

adoptability of AR and VR in hospitality industry

, 101

applications of AI in hospitality industry

, 36

sectors

, 101

Hotel industry

, 96–97

AR and VR in

, 97–98

mobile technology trends in

, 99

Hotel sales, influence of technology on

, 99–100

business and leisure guests in hotel industry

, 100

Hotels.com

, 139–140

I4.0. see Industry 4.0

Indian tourism industry, challenges to adopt AI in

, 40

Industrial Revolution 4.0 (IR 4.0)

, 127–128, 133–134, 136

B-Chabot marketing

, 128

future directions for research

, 128–129

influencer marketing

, 128

intervention of IR 4.0 in service points

, 125–126

marketing automation

, 128

social selling

, 128

Industry 4.0 (I4.0)

, 45–48, 107–108, 120, 135–136

immersion in tourism services

, 125–126

technologies

, 46, 49–50

Industry stakeholders

, 120

Influencer marketing

, 128

Information and communication technologies (ICT)

, 106, 135

Institutions promoting big data and data science for sustainable development

, 142–144

Intelligence centre

, 112–113

International Network of Sustainable Tourism Observatories (INSTO)

, 143–144

International tourism

, 22

Internet

, 133–134

systems

, 135

Internet of thing (IoT)

, 51, 82–83, 99, 120–121, 135

LAB turisme Barcelona

, 143–144

Language

, 34–35

Learning

, 34–36

process

, 20, 26

Local tourism hub

, 113

Marketing automation

, 128

Marriott Hotel Group, The

, 109–110

Mental process

, 3

Metaverse

, 108–110

exploring metaverse by destinations towards sustainability

, 110

exploring metaverse towards tourism sustainability

, 110–114

ongoing metaverse experiments in tourism destinations

, 109–110

Mixed virtual reality (MR)

, 87

Mobile phones

, 87

Mobile technology

trends in hotel industry

, 99

use of

, 98–99

National statistical systems (NSSs)

, 143

National tourism hub

, 113

Natural resources

, 46

Net Zero Emissions

, 142–143

Network mapping

, 53–55

Non-probability sampling techniques

, 5–6

NTO

, 140–141

NuMedii

, 35

Online databases

, 37–38

Online food communities

, 72

Online reservation data

, 50–51

Pandemic

, 120

Panel-corrected standard errors (PCSE)

, 26

Perception

, 34–35

Platform-as-a-service (PaaS)

, 123–124

Post-pandemic tourism

, 128–129

Preliminary analysis

, 55

Problem-solving

, 34

Real-time data tracking

, 45–46

Real-time feedback

, 39

Reasoning concept

, 34

Regression equation

, 24

Regression results

, 24–25, 28

‘Remote tourism campaign’

, 109–110

Research methodology

, 37

Revenue management

, 121

Robotics

, 122

current applications of AI and robotics in global tourism industry

, 38–39

Room sales, promoting guest

, 100–101

adoptability of AR and VR in hospitality industry

, 101

technology-based hospitality marketing

, 101

Security office

, 111–112

Sensors

, 135

Service points, intervention of IR 4.0 in

, 125–126

Sistema Inteligencia Turistica (SIT)

, 143–144

Small and medium-sized businesses (SMEs)

, 123–124

Smart cities

, 50

Smart devices

, 87

Smart forecasting

, 38

Smart systems

, 135, 140

Smart tourism development

, 88

SmartPLS software

, 6–7

Social media data

, 142

Software-as-a-Service (SaaS)

, 123–124

Solow production function

, 21

Source expertise

, 4

Speech recognition technology

, 39–40

SPSS software

, 6–7

Structural equation modelling

, 6–7

Structural model, assessment of

, 9–11

Structural vector autoregressive model

, 21

Sustainability

, 48–49

central government’s destination management organisation

, 111

exploring metaverse by destinations towards

, 110

exploring metaverse towards tourism sustainability

, 110–114

hosts

, 114

implementation, control, monitoring and evaluation centre

, 113

intelligence centre

, 112–113

local tourism hub

, 113

metaverse

, 108–110

methodology

, 106–107

national tourism hub

, 113

results and final considerations

, 114–115

security office

, 111–112

tourism trends

, 72–73

tourists

, 113–114

Sustainable development

, 83

concept

, 110

Sustainable Development Goals (SDGs)

, 110, 142–143

Sustainable management with big data

, 48–50

Sustainable tourism. (see also Virtual tourism)

, 52

background

, 135–136

big data in

, 50–52

destinations

, 51–52

exploratory research, conceptual research, frameworks and grounded theory

, 136–138

findings

, 138–144

industry 4.0, big data and data science

, 135–136

institutions promoting big data and data science for sustainable development

, 142–144

management

, 72

methodology

, 136–138

rise of big data in tourism sector

, 138

tourism business dimension

, 138–140

tourism governance dimension

, 140–141

tourism research dimension

, 141–142

virtual tourism and

, 88–91

Systematic review method

, 52–53

Technological advancement

, 120, 122–123

Technological complexity

, 21–22

Technological innovations

, 106

Technological revolution

, 26–28

Technologies 4.0

, 107–108, 114

Technology

, 20, 82, 120, 125–126

on economic growth

, 20

innovations in tourism industry

, 120–125

technology-based hospitality marketing

, 101

Theorisation

, 137–138

3D technology

, 82–83

Tourism. (see also Virtual tourism; Sustainable tourism)

, 23, 32, 107–108

alternative form of Tourism in crisis period

, 84–85

applications of AI in

, 36

business dimension

, 138–140

challenges to adopt AI-based applications in

, 36–37

curse of

, 136

dimension

, 141–142

and economic growth

, 22–23

ecosystem

, 51

education

, 85

experiences

, 108–109

exploring metaverse towards tourism sustainability

, 110–114

future possibilities of AI applications in

, 39–40

governance dimension

, 140–141

immersion of Industry 4.0 in tourism services

, 125–126

industry

, 2, 23

industry practitioners

, 11–12

management

, 85

metaverse

, 115

ongoing metaverse experiments in tourism destinations

, 109–110

research

, 134, 142

rise of big data in tourism sector

, 138

society in Cardiff

, 32

technology innovations in

, 120–125

Tourism 4.0

, 114, 120

virtual reality and application in field of

, 83–85

Tourists

, 51, 120

sector

, 127

tracking technologies

, 142

visit intention

, 3

Travel industry

, 96–97

Travel intention

, 3

Travel vlogs

, 2

UNESCO World Heritage Sites

, 89

United Nation World Tourism Organisation (UNWTO)

, 36–37, 88, 143–144

United Nations (UN)

, 48–49, 110, 143

UN Global Pulse

, 143

UN Statistical Division

, 110

United Nations’ Sustainable Development Goals (SDGs)

, 48–49

User-generated content (UGC)

, 135, 142

Virtual destination marketing initiatives

, 127

Virtual reality (VR). (see also Augmented reality (AR))

, 82–85, 96, 99, 124, 135

alternative form of tourism in crisis period

, 84–85

and application in field of tourism

, 83–85

destinations marketing

, 84

in hospitality industry

, 101

in hotel industry

, 97–98

technology

, 85

tourism education and tourism management

, 85

travelling possibilities expanding

, 84

trip planning and experience enhancing

, 83–84

Virtual technologies

, 87

Virtual tourism. (see also Sustainable tourism)

, 84–85, 91–92, 126

advantages and disadvantages of

, 85–87

future of

, 87–88

and sustainable tourism

, 88–91

Visit intention

, 4

Vlogger attractiveness

, 4–5

Vlogger credibility

, 3

Vlogger expertise

, 4

Vlogger trustworthiness

, 3–4

Volume, variety and velocity (3V)

, 48

‘VRoom Service’

, 109–110

Web of science core collections

, 53

Web page visit data

, 50–51

Web search data

, 50–51

World Travel and Tourism Council

, 90

Worldwide Network of Tourism Experts

, 32

Yellowstone National Park

, 109–110

YouTube

, 2–3, 133–134