Artificial Intelligence in the Tourism Industry: An Overview of Reviews
Abstract
:1. Introduction
- RQ1: What are the key themes, strengths, and limitations in the application of artificial intelligence (AI) within the tourism industry as identified in the existing literature?
- RQ2: What are the common applications of AI within the tourism industry and what potential implications do they hold?
- RQ3: What are the potential future lines of research within AI applications in the tourism industry, as suggested by the existing literature and current trends in the field?
2. Materials and Methods
3. Results
- Basic information about systematic reviews (e.g., title; first author; year of publication; journal). Full references are presented in the bibliography;
- Keywords;
- Outcomes.
4. Discussion
4.1. Forecasting
4.2. Improving Operational Efficiency
4.3. Enhancing Customer Experiences
4.4. Sustainability
5. Conclusions and Future Research
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A
Year | 1st Author | Title | Journal | Keywords |
---|---|---|---|---|
(2023) | Al-Nafjan, A | Systematic Review and Future Direction of Neuro-Tourism Research | Brain sciences | artificial intelligence; attention; brain; brain-computer interface; electroencephalography; eye-tracking; neuro-tourism; neuromarketing; neuroscience; tourist emotion |
(2019) | Cain, LN | From sci-fi to sci-fact: the state of robotics and AI in the hospitality industry | Journal of hospitality and tourism technology | animacy; artificial intelligence (ai); challenges; future; hospitality technology; intelligence; literature review; management; quality; robotics; service; special issue; technology; tourism |
(2022) | Chen, MY | Overviews of Internet of Things Applications in China’s Hospitality Industry | Processes | cost reduction; efficiency; Internet of Things (IoT); smart hotels |
(2022) | Chen, YL | Big Data and Predictive Analytics for Business Intelligence: A Bibliographic Study (2000–2021) | Forecasting | bibliographic study; big data; business intelligence; CiteSpace; data science; predictive analytics; strategic value; trends |
(2020) | Chi, OH | Artificially intelligent device use in service delivery: a systematic review, synthesis, and research agenda | Journal of Hospitality Marketing & Management | acceptance; adoption; artificial intelligence; attitudes; behavior; consumers; experiences; framework; healthcare; hospitality; hospitality; intentions; interaction; review; service; social robots |
(2021) | Das, G | Pandemics and marketing: insights, impacts, and research opportunities | Journal of the Academy of Marketing Science | 7p model; artificial intelligence; disease; frugal; industry sectors; less; macro-level forces; marketing implications; outcomes; pandemics; performance; price; research opportunities; resource scarcity; strategy; uncertainty |
(2022) | Doborjeh, Z | Artificial intelligence: a systematic review of methods and applications in hospitality and tourism | International Journal of Contemporary Hospitality Management | ai algorithms; ai applications; ai methods; algorithms; applications; artificial intelligence; augmented reality; demand; future studies; future studies-in-tourism; hospitality; machine; model; prediction; spiking neural-networks; tourism; tourism; travel |
(2022) | Elkhwesky, Z | Driving hospitality and tourism to foster sustainable innovation: A systematic review of COVID-19-related studies and practical implications in the digital era | Tourism and Hospitality Research | COVID-19 pandemic; digital technology; hospitality; hotels; performance; sustainability; sustainable development; sustainable innovation; tourism; trends |
(2022) | Essien, A | Deep learning in hospitality and tourism: a research framework agenda for future research | International Journal of Contemporary Hospitality Management | artificial intelligence; big data; deep learning; hospitality management; information; internet; machine learning; neural network; progress; representations; sentiment analysis; smart tourism; systematic literature review (slr); tourism management; tourism research; trends |
(2021) | Gaur, L | Role of artificial intelligence and robotics to foster the touchless travel during a pandemic: a review and research agenda | International Journal of Contemporary Hospitality Management | artificial intelligence and robotics; cleanliness and sanitation; COVID-19 pandemic; health care and wellness; hospitality; hotels; innovation; management; service; systems |
(2022) | Giotis, G | The Role of Managerial and Technological Innovations in the Tourism Industry: A Review of the Empirical Literature | Sustainability | adoption; business performance; destination; employee creativity; empowering leadership; firms; managerial innovation; mobile technology; organizational innovation; service innovation; social media; sustainable tourism development; technological innovation |
(2019) | Ivanov, S | Progress on robotics in hospitality and tourism: a review of the literature | Journal of Hospitality and Tourism Technology | artificial intelligence; education; research agenda; robonomics; robot adoption; robotics; rservice; service robot; servicescape |
(2019) | Jiao, EX | Tourism forecasting: A review of methodological developments over the last decade | Tourism Economics | accuracy; algorithm; arrivals; combination; demand; forecasting; internet; new trends; prediction; regression-model; review; time series; tourism demand; volatility |
(2021) | Kirtil, IG | Artificial intelligence in tourism: a review and bibliometrics research | Advances in Hospitality and Tourism Research-ahtr | artificial intelligence; bibliometric; co-citation analysis; co-occurrence analysis; collaboration; hospitality; hospitality and tourism; lessons; management; network analysis; patterns; science; search; thematic analysis; travel; trends |
(2020) | Leung, XY | Technology-enabled service evolution in tourism: a perspective article | Tourism Review | a-service; e-service; m-service; service evolution; smart experience |
(2021) | Li, ML | A systematic review of AI technology-based service encounters: Implications for hospitality and tourism operations | International Journal of Hospitality Management | artificial intelligence (ai); artificial intelligence ai; customer satisfaction; experience; impact; industry; measurement scales; media; moderating role; public health emergency; responses; service encounter; service experience; systematic review; virtual reality |
(2021) | Li, X | Review of tourism forecasting research with internet data | Tourism Management | arrivals; big data analytics; demand; destinations; google trends; internet data; online reviews; search; search engine; sentiment classification; social media; social media; systematic review; tourism forecasting; volume |
(2019) | Liu, H | Hot topics and emerging trends in tourism forecasting research: A scientometric review | Tourism Economics | accuracy; bibliometric analysis; CiteSpace; cocitation; demand; flows; genetic algorithms; international tourism; knowledge mapping; model; regenerative medicine; research frontiers; scientometrics; time-series; tourism forecasting |
(2022) | Loureiro, SMC | Culture, heritage looting, and tourism: A text mining review approach | Frontiers in Psychology | context; cultural heritage looting; cultural heritage preservation; destruction; heritage destruction; human rights; property protection; protection of cultural property; public access |
(2022) | Lv, H | A look back and a leap forward: a review and synthesis of big data and artificial intelligence literature in hospitality and tourism | Journal of Hospitality Marketing & Management | artificial intelligence; bibliometric analysis; big data; customer satisfaction; data analytics; hospitality; hotel performance; information search; learning model; literature review; online reviews; smart tourism; social media; tourism; tracking data; word-of-mouth |
(2022) | Ndaguba, EA | A Systematic Review of a City in a City: An Aerotropolitan Perspective | Land | aerotropolis; built cities; CiteSpace; new urban extension; technology; transit-bound tourism; transitional cities; transportation; urbanisation; urbanization; VOSviewer |
(2020) | Osei, BA | Prospects of the fourth industrial revolution for the hospitality industry: a literature review | Journal of Hospitality and Tourism Technology | challenges; context; fourth industrial revolution; future; hospitality; hospitality revolution 4; information; prospects; service; smart tourism; technologies; tourism |
(2022) | Rahmadian, E | A systematic literature review on the use of big data for sustainable tourism | Current Issues in Tourism | analytics; artificial intelligence; behavior; big data; Chinese tourists; countries; demand; destination image; flow; Internet of Things; patterns; search; social media; sustainable tourism; systematic literature review |
(2020) | Samara, D | Artificial intelligence and big data in tourism: a systematic literature review | Journal of Hospitality and Tourism Technology | architecture; artificial intelligence; attraction recommendation; big data; business value; demand; future; fuzzy time-series; hospitality; information technology; integration; literature review; recent trends; sustainable tourism; tourism |
(2023) | Soliman, M | Exploring the Major Trends and Emerging Themes of Artificial Intelligence in the Scientific Leading Journals amidst the COVID-19 Era | Big Data and Cognitive Computing | ai leading journals; artificial intelligence (ai); bibliometric analysis; bibliometric analysis; COVID-19; impact; scopus; tourism; visualization |
(2019) | Song, HY | A review of research on tourism demand forecasting | Annals of Tourism Research | artificial intelligence model; cointegration analysis; econometric forecasts; econometric model; economic-crisis; forecast combination; inbound tourism; international tourism; judgment forecasts; neural-network model; support vector regression; time series; time-varying parameter; tourism demand; travel demand; united-states |
(2023) | Thayyib, PV | State-of-the-Art of Artificial Intelligence and Big Data Analytics Reviews in Five Different Domains: A Bibliometric Summary | Sustainability | ai; artificial intelligence; bibliometric analysis; big data; big data analysis; big data analytics; business models; deep learning; evolution; expert systems; future; fuzzy logic; healthcare; i4; industry 4.0; insights; internet; knowledge; management; neural networks; nlp; robotics |
(2020) | Tussyadiah, I | A review of research into automation in tourism: Launching the Annals of Tourism Research Curated Collection on Artificial Intelligence and Robotics in Tourism | Annals of Tourism Research | artificial intelligence; automated tourism; foundations; future; information technology; innovation; intelligent automation; internet; negative attitudes; research agenda; service robot; service robots; sustainability transitions; things; user acceptance |
(2021) | Yang, JJ | A Systematic Review for Service Humanoid Robotics Model in Hospitality | International Journal of Social Robotics | artificial intelligence; capabilities; robot in hospitality; service humanoid robotics |
(2022) | Ye, HY | A Review of Robotic Applications in Hospitality and Tourism Research | Sustainability | experience; hotel; management; robot; robotic applications; service; state; technology; tourism |
(2020) | Yeh, CCR | Labor Displacement in Artificial Intelligence Era: A Systematic Literature Review | Taiwan Journal of East Asian studies | ai; artificial intelligence; automation; computers; employment; future; humans; impact; labor displacement; labor market; robots; technology; work |
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Inclusion Criteria | Title 2 |
---|---|
Reviews published in scientific journals. | Official literature |
Period 2019–2023 | Other secondary data analysis |
Written in English |
Categorization | Authors | Main Contribution |
---|---|---|
Forecasting | Song et al. (2019) | Discuss traditional methodologies in tourism forecasting and emphasize the emergence and success of AI models in this field. |
Liu et al. (2019) | Highlight the use of AI in tourism forecasting since 2009, despite concerns about the ‘black box’ nature of AI models. | |
Doborjeh et al. (2022) | Discuss how AI can aid in forecasting future business conditions, revenues, and trends in guest/tourist demand. | |
Essien and Chukwukelu (2022) | Focus on high-frequency forecasting techniques using big data from mobile devices for predicting and managing crowdedness in tourism destinations. | |
Lv et al. (2022) | Emphasize the importance of diverse data sources like professional databases, government databases, and operation data for effective forecasting in hospitality and tourism management. | |
Chen et al. (2022b) | Identify BI and IT-driven solutions as the most effective methods for leveraging growth trend in the hospitality industry. | |
Liu et al. (2019) | Discuss recent aggressive application of big data analyses, machine learning, and use of search engine data and web search intensity in tourism forecasting research. | |
Li et al. (2021b) | Highlight some limitations and challenges related to the use of internet data in tourism forecasting, such as quality of search engine data and presence of noise or irrelevant information in social media data. | |
Improving operational efficiency | Song et al. (2019) | Outlined the roles of traditional methods in tourism forecasting and the increasing use of AI-based methods. |
Liu et al. (2019) | Highlighted the growing use of AI methods in tourism forecasting but noted their limitations including the need for large amounts of data and their complexity. | |
Jiao and Chen (2019) | Discussed the challenges of using AI methods in tourism forecasting, including the difficulty of interpretation and the need for significant computational resources. | |
Li et al. (2021b) | Highlighted some limitations and challenges related to the use of internet data in tourism forecasting. | |
Yang and Chew (2021) | Discussed the potential of intelligent robotics to address challenges in the hospitality industry. | |
Ivanov et al. (2019) | Outlined the application of AI in the tourism industry with specific use-cases like the deployment of robots as bartenders, waiters, and receptionists. | |
Cain et al. (2019) | Highlighted the prominence of AI applications in the tourism industry, including the use of robots in various roles. | |
Enhancing customer experiences | Elkhwesky et al. (2022) | AI demonstrates considerable promise in augmenting customer experiences. |
Cain et al. (2019) | Implementation of customized recommendations and suggestions encompasses the integration of cloud robotics and robotic navigation. | |
Li et al. (2021a) | Service engagements enhanced by AI-integrated media contribute to customer satisfaction, loyalty, and positive recommendations. | |
Chen et al. (2022a) | Use of IoT platforms can remember guests’ specific comfort preferences, leading to improved customer experiences. | |
Lv et al. (2022) | Big data from user-generated content is used to investigate visitor attitudes, satisfaction, and preferences in the travel industry. | |
Samara et al. (2020) | AI facilitates trust-based recommendations in the tourism industry. | |
Cain et al. (2019) | AI-powered chatbots and virtual assistants like small, multi-lingual robots are used to answer customer inquiries. | |
Chi et al. (2020) | Virtual multi-lingual personal cruise assistants improve customer experiences. | |
Doborjeh et al. (2022) | CRM built on big data and AI empowers chatbots to offer personalized travel services. | |
Essien and Chukwukelu (2022) | Transfer learning in deep learning improves accuracy and efficiency in providing personalized recommendations to tourists. | |
Giotis and Papadionysiou (2022) | AI technologies help tourism companies target customers with personalized marketing messages. | |
Al-Nafjan et al. (2023) | Neuro-tourism utilizes neuroscience to enhance the tourism industry’s marketing strategies. | |
Li et al. (2021a) | Use of Virtual Reality significantly amplifies tourists’ service experiences. | |
Das et al. (2021) | AI can offer substitutes for travel experiences through augmented reality. | |
Loureiro et al. (2022) | AI technologies enhance visitor experiences at cultural heritage sites. | |
Gaur et al. (2021) | Robots’ contactless services are particularly important after the COVID-19 pandemic. | |
Doborjeh et al. (2022) | There is a shift towards developing customer-centric chatbot platforms that can understand customer behavior, emotions, and intentions. | |
Chi et al. (2020) | AI-powered devices can enhance customer experiences through stress management, emotional intelligence, and virtual personal assistants. | |
Cain et al. (2019) | Understanding how customers will accept and engage with robots and AI is vital for their adoption and success. | |
Leung (2020) | Traveler co-creation will play a significant role in shaping the future of tourism service delivery. | |
Osei et al. (2020) | Customers’ reluctance to use new technologies can pose a challenge for tourism firms. | |
Elkhwesky et al. (2022) | AI demonstrates considerable promise in augmenting customer experiences. | |
Sustainability | Gaur et al. (2021) | AI-powered systems can optimize resource allocation, minimize energy consumption, enhance waste management, and improve health and safety measures, promoting sustainable tourism initiatives and reducing the industry’s ecological footprint. |
Lv et al. (2022) | The use of big data to examine environmental performance of hospitality operations shows that greater corporate social responsibility involvement can result in higher environmental performance. | |
Rahmadian et al. (2022) | Big data is utilized in ecotourism to understand visitor patterns, attractions, impacts on natural resources, identify conservation areas, and develop smart tourism strategies. | |
Giotis and Papadionysiou (2022) | Technology reduces paper consumption, minimizes waste generation, and contributes to overall sustainability efforts by replacing traditional printed materials like brochures and posters. | |
Loureiro et al. (2022) | The rise of AI, VR, and AR technologies can prevent damage to cultural heritage sites by providing virtual experiences and predicting tourist flow. | |
Elkhwesky et al. (2022) | Drones may be used to provide live virtual tours of open-space tourist locations, contributing to eco-friendly innovation. | |
Tussyadiah (2020); Rahmadian et al. (2022) | The integration of AI tools, IoT, and big data in the tourism value chain supports the concept of smart tourism ecosystems, improving visitor experiences and promoting sustainable practices. | |
Kirtil and Askun (2021) | AI-powered chatbots and virtual assistants can promote responsible tourism practices by encouraging tourists to make informed decisions about their travel and activities. |
Research Question | Key Articles |
---|---|
RQ1: What are the key themes, strengths, and limitations in the application of artificial intelligence (AI) within the tourism industry as identified in the existing literature? | Cain et al. (2019); Ivanov et al. (2019); Jiao and Chen (2019); Leung (2020); Li et al. (2021b); Loureiro et al. (2022); Osei et al. (2020); Samara et al. (2020); Tussyadiah (2020) |
RQ2: What are the common applications of AI within the tourism industry and what potential implications do they hold? | Cain et al. (2019); Chen et al. (2022b); Chi et al. (2020); Das et al. (2021); Doborjeh et al. (2022); Elkhwesky et al. (2022); Essien and Chukwukelu (2022); Gaur et al. (2021); Giotis and Papadionysiou (2022); Ivanov et al. (2019); Kirtil and Askun (2021); Leung (2020); Li et al. (2021b); Loureiro et al. (2022); Lv et al. (2022); Chen et al. (2022a); Ndaguba et al. (2022); Osei et al. (2020); Rahmadian et al. (2022); Samara et al. (2020); Soliman et al. (2023); Tussyadiah (2020); Yang and Chew (2021) |
RQ3: What are the potential future lines of research within AI applications in the tourism industry as suggested by the existing literature and current trends in the field? | Cain et al. (2019); Gaur et al. (2021); Ivanov et al. (2019); Jiao and Chen (2019); Leung (2020); Osei et al. (2020); Samara et al. (2020); Tussyadiah (2020) |
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García-Madurga, M.-Á.; Grilló-Méndez, A.-J. Artificial Intelligence in the Tourism Industry: An Overview of Reviews. Adm. Sci. 2023, 13, 172. https://doi.org/10.3390/admsci13080172
García-Madurga M-Á, Grilló-Méndez A-J. Artificial Intelligence in the Tourism Industry: An Overview of Reviews. Administrative Sciences. 2023; 13(8):172. https://doi.org/10.3390/admsci13080172
Chicago/Turabian StyleGarcía-Madurga, Miguel-Ángel, and Ana-Julia Grilló-Méndez. 2023. "Artificial Intelligence in the Tourism Industry: An Overview of Reviews" Administrative Sciences 13, no. 8: 172. https://doi.org/10.3390/admsci13080172