Skip to main content
SpringerLink
Log in

Impact of Artificial Intelligence in Travel, Tourism, and Hospitality

  • Living reference work entry
  • First Online:
Book cover Handbook of e-Tourism

Abstract

Artificial intelligence (AI) is currently present in almost every area of travel and tourism, appearing in different types of applications such as personalization and recommender systems, robots, conversational systems, smart travel agents, prediction and forecasting systems, language translation applications, and voice recognition and natural language processing systems. Recent improvements in big data, algorithms, and computing power have enabled significant enhancements in AI. In this chapter, we review how AI has changed and is changing the main processes in the tourism industry. We start with the IT foundations of AI that are relevant for travel and tourism and then address the AI systems and applications available in the sector. We then examine hospitality in detail, as a sector in which most of these systems are being implemented. We conclude with the challenges that AI faces in the tourism sector, a research agenda, and draw a scenario of the future of AI in tourism.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

References

  • Aarts E, Wichert R (2009) Ambient intelligence. In: Bullinger HJ (ed) Technology guide. Springer, Berlin/Heidelberg, pp 244–249

    Google Scholar 

  • Basiri A, Amirian P, Winstanley A, Moore T (2018) Making tourist guidance systems more intelligent, adaptive and personalised using crowd sourced movement data. J Ambient Intell Humaniz Comput 9(2):413–427

    Google Scholar 

  • Benckendorff PJ, Xiang Z, Sheldon PJ (2019) Tourism information technology. CABI, Boston

    Google Scholar 

  • Bostrom N (2016) Superintelligence: paths, dangers, strategies. Oxford University Press, Oxford

    Google Scholar 

  • Bostrom N, Yudkowsky E (2014) The ethics of artificial intelligence. In: The Cambridge handbook of artificial intelligence. Cambridge University Press, Cambridge, pp 316–334

    Google Scholar 

  • Bowen J, Morosan C (2018) Beware hospitality industry: the robots are coming. Worldwide Hosp Tour Themes 10(6):726–733

    Google Scholar 

  • Brynjolfsson E, McAfee A (2011) Race against the machine: how the digital revolution is accelerating innovation, driving productivity, and irreversibly transforming employment and the economy. Digital Frontier Press, Lexington

    Google Scholar 

  • Buhalis D, Leung R (2018) Smart hospitality—interconnectivity and interoperability towards an ecosystem. Int J Hosp Manag 71:41–50

    Google Scholar 

  • Buhalis D, Harwood T, Bogicevic V, Viglia G, Beldona S, Hofacker C (2019) Technological disruptions in services: lessons from tourism and hospitality. J Serv Manag 30:484–506

    Google Scholar 

  • Bulchand-Gidumal J (2016) Aprendizaje profundo y su impacto en turismo. In La actividad turística española en 2015:(edición 2016): 419–422. Síntesis

    Google Scholar 

  • Claveria O, Torra S (2014) Forecasting tourism demand to Catalonia: neural networks vs. time series models. Econ Model 36:220–228

    Google Scholar 

  • Claveria O, Monte E, Torra S (2015) A new forecasting approach for the hospitality industry. Int J Contemp Hosp Manag 27(7):1520–1538. https://doi.org/10.1108/IJCHM-06-2014-0286

    Google Scholar 

  • Cohen E (2004) Contemporary tourism: diversity and change. Elsevier, Boston

    Google Scholar 

  • De Mauro A, Greco M, Grimaldi M (2016) A formal definition of big data based on its essential features. Libr Rev 65(3):122–135

    Google Scholar 

  • Gao M, Liu K, Wu Z (2010) Personalisation in web computing and informatics: theories, techniques, applications, and future research. Inf Syst Front 12(5):607–629

    Google Scholar 

  • Gavalas D, Konstantopoulos C, Mastakas K, Pantziou G (2014) Mobile recommender systems in tourism. J Netw Comput Appl 39:319–333

    Google Scholar 

  • Gretzel U (2011) Intelligent systems in tourism: a social science perspective. Ann Tour Res 38(3):757–779

    Google Scholar 

  • Gretzel U, Sigala M, Xiang Z, Koo C (2015) Smart tourism: foundations and developments. Electron Mark 25(3):179–188

    Google Scholar 

  • Gunter U, Önder I (2016) Forecasting city arrivals with Google analytics. Ann Tour Res 61: 199–212

    Google Scholar 

  • Gursoy D (2018) Future of hospitality marketing and management research. Tour Manag Perspect 25:185–188

    Google Scholar 

  • Hintze A (2016) Understanding the four types of AI, from reactive robots to self-aware beings. The conversation, Available at: http://theconversation.com/understanding-the-four-types-of-ai-fromreactive-robots-to-self-aware-beings-67616. Last accessed 17 July 2019

    Google Scholar 

  • Huang HC (2014) A study on artificial intelligence forecasting of resort demand. J Theor Appl Inf Technol 70(2):265–272

    Google Scholar 

  • Ivanov SH, Webster C (2017) Adoption of robots, artificial intelligence and service automation by travel, tourism and hospitality companies–a cost-benefit analysis. In: Artificial intelligence and service automation by travel, tourism and hospitality companies–a cost-benefit analysis. International scientific conference “contemporary tourism – traditions and innovations”, Oct 2017, Sofia University, pp 19–21

    Google Scholar 

  • Ivanov SH, Webster C, Berezina K (2017) Adoption of robots and service automation by tourism and hospitality companies. Revista Turismo Desenvolvimento 27(28):1501–1517

    Google Scholar 

  • Kurzweil R (2005) The singularity is near: when humans transcend biology. Penguin, New York

    Google Scholar 

  • Lai WC, Hung WH (2018) A framework of cloud and AI based intelligent hotel. In: Proceedings of the 18th international conference on electronic business, ICEB, Guilin, 2–6 Dec, pp 36–43

    Google Scholar 

  • Law R, Au N (1999) A neural network model to forecast Japanese demand for travel to Hong Kong. Tour Manag 20(1):89–97

    Google Scholar 

  • LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436

    Google Scholar 

  • Li J, Xu L, Tang L, Wang S, Li L (2018) Big data in tourism research: a literature review. Tour Manag 68:301–323

    Google Scholar 

  • Li JJ, Bonn MA, Ye BH (2019) Hotel employee’s artificial intelligence and robotics awareness and its impact on turnover intention: the moderating roles of perceived organizational support and competitive psychological climate. Tour Manag 73:172–181

    Google Scholar 

  • Ma Y, Xiang Z, Du Q, Fan W (2018) Effects of user-provided photos on hotel review helpfulness: an analytical approach with deep leaning. Int J Hosp Manag 71:120–131

    Google Scholar 

  • McCorduck P (2004) Machines who think. A personal inquiry into the history and prospects of artificial intelligence, 2nd edn. A K Peters/CRC Press, Boca Raton

    Google Scholar 

  • Melián-González S (2019) The impact of digital technology on work. Available at SSRN: https://ssrn.com/abstract=3353258

    Google Scholar 

  • Melián-Gonzalez S, Gutiérrez-Taño D, Bulchand-Gidumal J (2019) Predicting the intentions to use chatbots for travel and tourism. Curr Issues Tour. https://doi.org/10.1080/13683500.2019.1706457

    Google Scholar 

  • Murphy J, Hofacker C, Gretzel U (2017) Dawning of the age of robots in hospitality and tourism: challenges for teaching and research. Eur J Tour Res 15:104–111

    Google Scholar 

  • Musk E (2014) Available at https://twitter.com/elonmusk/status/495759307346952192. Last accessed 27 July 2019

  • Ricci F, Rokach L, Shapira B (2015) Recommender systems: introduction and challenges. In: Recommender systems handbook. Springer, Boston, pp 1–34

    Google Scholar 

  • Rogers EM (2010) Diffusion of innovations. Simon and Schuster. New York

    Google Scholar 

  • Rudas IJ, Fodor J (2008) Intelligent systems. Int J Comput Commun Control III(Suppl.):132–138

    Google Scholar 

  • Russell SJ, Norvig P (2016) Artificial intelligence: a modern approach. Pearson Education Limited, Harlow

    Google Scholar 

  • Schuckert M, Liu X, Law R (2015) Hospitality and tourism online reviews: recent trends and future directions. J Travel Tour Market 32(5):608–621

    Google Scholar 

  • Sigala M (2018) New technologies in tourism: from multi-disciplinary to anti-disciplinary advances and trajectories. Tour Manag Perspect 25:151–155

    Google Scholar 

  • Smith CS (2019) Dealing with bias in artificial intelligence. Available at https://www.nytimes.com/2019/11/19/technology/artificial-intelligence-bias.html. Last accessed 17 Dec 2019

  • Stalidis G, Karapistolis D, Vafeiadis A (2015) Marketing decision support using artificial intelligence and knowledge modeling: application to tourist destination management. In: Kavoura A, Sakas DP, Tomaras P (eds) Procedia—social and behavioral sciences 175:106–113. Elsevier, Madrid

    Google Scholar 

  • Sterne J (2017) Artificial intelligence for marketing: practical applications. Wiley, Hoboken

    Google Scholar 

  • Sun S, Wei Y, Tsui KL, Wang S (2019) Forecasting tourist arrivals with machine learning and internet search index. Tour Manag 70:1–10

    Google Scholar 

  • Tung VWS, Law R (2017) The potential for tourism and hospitality experience research in human-robot interactions. Int J Contemp Hosp Manag 29(10):2498–2513

    Google Scholar 

  • Tussyadiah I, Miller G (2019) Perceived impacts of artificial intelligence and responses to positive behaviour change intervention. In: Information and communication technologies in tourism 2019. Springer, Cham, pp 359–370

    Google Scholar 

  • Wirth N (2018) Hello marketing, what can artificial intelligence help you with? Int J Market Res 60(5):435–438

    Google Scholar 

  • Yu G, Schwartz Z (2006) Forecasting short time-series tourism demand with artificial intelligence models. J Travel Res 45(2):194–203

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute for Sustainable Tourism and Economic Development (TIDES), University of Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain

    Jacques Bulchand-Gidumal

Authors
  1. Jacques Bulchand-Gidumal
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jacques Bulchand-Gidumal .

Editor information

Editors and Affiliations

  1. Department of Hospitality and Tourism Management, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA

    Zheng Xiang

  2. Department of Tourism Studies and Geography, Mid Sweden University, Östersund, Sweden

    Matthias Fuchs

  3. Annenberg School for Communication and Journalism, University of Southern California, Los Angeles, CA, USA

    Ulrike Gretzel

  4. Department of Business Informatics, University of Applied Sciences Ravensburg-Weingarten, Weingarten, Germany

    Wolfram Höpken

Section Editor information

  1. The Howard Feiertag Department of Hospitality and Tourism Management, Virginia Polytechnic Institute and State University; Pamplin College of Business, Blacksburg, VA, USA

    Zheng Xiang

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this entry

Check for updates. Verify currency and authenticity via CrossMark

Cite this entry

Bulchand-Gidumal, J. (2020). Impact of Artificial Intelligence in Travel, Tourism, and Hospitality. In: Xiang, Z., Fuchs, M., Gretzel, U., Höpken, W. (eds) Handbook of e-Tourism. Springer, Cham. https://doi.org/10.1007/978-3-030-05324-6_110-1

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-05324-6_110-1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-05324-6

  • Online ISBN: 978-3-030-05324-6

  • eBook Packages: Springer Reference Business and ManagementReference Module Humanities and Social SciencesReference Module Business, Economics and Social Sciences

Publish with us

Policies and ethics

Search

Navigation