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Artificial Intelligence and Robotics Driving Tourism 4.0: An Exploration

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Handbook of Technology Application in Tourism in Asia

Abstract

With massive technological revolutions in place, Tourism 4.0 is gearing up to a new world of possibilities driven by the colossal amount of data generated from the tourist mobility across the world. In this context, AI and robotics are emerging out to be the game changers in the era of Tourism 4.0. This chapter aims to bring forth the various facets of AI and robotics driving the tourism industry towards a sustainable future. Some of the challenges that lie ahead have also been discussed in this work. It is concluded that with many new integrations to the field of AI and robotics, the tourism industry is expected to reach new heights of customization, service delivery, and experience management amplified by the accurate forecasting techniques in the days to come.

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References

  • Akehurst, G. (2009). User generated content: The use of blogs for tourism organisations and tourism consumers. Service Business, 3(1), 51–61.

    Article  Google Scholar 

  • Baggio, R. (2018). Measuring tourism: Methods, indicators, and needs. In E. Fayos-Solà & C. Cooper (Eds.), The future of tourism: Innovation and sustainability (pp. 255–269). Springer International Publishing.

    Google Scholar 

  • Biron, B. (2020). Take look inside Alibaba’s high-tech ‘hotel of the future,’ which is run almost entirely by robots. Retrieved from: https://www.businessinsider.in/tech/news/take-look-inside-alibabas-high-tech-hotel-of-the-future-which-is-run-almost-entirely-by-robots/articleshow/71445793.cms (Accessed: the 21st August, 2020).

  • Bowen, J., & Morosan, C. (2018). Beware hospitality industry: The robots are coming. Worldwide Hospitality and Tourism Themes, 10(6), 726–733.

    Article  Google Scholar 

  • Buhalis, D., & Sinarta, Y. (2019). Real-time co-creation and nowness service: Lessons from tourism and hospitality. Journal of Travel and Tourism Marketing, 36(5), 563–582.

    Article  Google Scholar 

  • Chawaratthanarungsri, P., & Tongngam, S. (2020). Solving the travelling problem of Thai tourism, by improved ant colony optimization. International Journal of Innovation, Creativity and Change, 11(10), 575–589.

    Google Scholar 

  • Chen, P. (2020). Strategy of artificial intelligence assisted health tourism in the perspective of global region based on Markov chain model. In 2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC). Erode: IEEE: The 11th-13th march.

    Google Scholar 

  • Cho, V. (2003). A comparison of three different approaches to tourist arrival forecasting. Tourism Management, 24(3), 323–330.

    Article  Google Scholar 

  • Choi, Y., Oh, M., Choi, M., & Kim, S. (2020). Exploring the influence of culture on tourist experiences with robots in service delivery environment. Current Issues in Tourism. https://doi.org/10.1080/13683500.2020.1735318

  • de Kervenoael, R., Hasan, R., Schwob, A., & Goh, E. (2020). Leveraging human-robot interaction in hospitality services: Incorporating the role of perceived value, empathy, and information sharing into visitors’ intentions to use social robots. Tourism Management. https://doi.org/10.1016/j.tourman.2019.104042

  • Delisle, M. A. (2019). Montréal: A creative tourism destination? In N. Duxbury & G. Richards (Eds.), A research agenda for creative tourism (pp. 97–108). Edward Elgar Publishing Ltd..

    Chapter  Google Scholar 

  • Deloitte. (2019). Converge: Shaping AI for Southeast Asia. Retrieved from: https://www2.deloitte.com/content/dam/Deloitte/sg/Documents/innovation/sea-inno-converge-shaping-ai-for-sea.pdf (Accessed: the 21st August, 2020).

  • Dexeus, C. R. (2018). The deepening effects of the digital revolution. In E. Fayos-Solà & C. Cooper (Eds.), The future of tourism: Innovation and sustainability (pp. 43–69). Springer International Publishing.

    Google Scholar 

  • Feierherd, G., González, F., Viera, L., Soler, R., Romano, L., Delía, L., & Depetris, B. (2019). Combining Artificial Intelligence Services for the Recognition of Flora Photographs: Uses in Augmented Reality and Tourism. In P. Pesado & C. Aciti (Eds.), Computer Science – CACIC 2018. CACIC 2018. Communications in Computer and Information Science (pp. 367–375). Springer Verlag.

    Google Scholar 

  • Ferràs, X., Hitchen, E. L., Tarrats-Pons, E., & Arimany-Serrat, N. (2020). Smart tourism empowered by artificial intelligence: The case of Lanzarote. Journal of Cases on Information Technology, 22(1), 1–13.

    Article  Google Scholar 

  • Gajdošík, T., & Marciš, M. (2019). Artificial intelligence tools for smart tourism development. In R. Silhavy (Ed.), Artificial Intelligence Methods in Intelligent Algorithms. CSOC 2019. Advances in intelligent systems and computing (pp. 392–402). Springer Verlag.

    Google Scholar 

  • Go, H., Kang, M., & Suh, S. C. (2020). Machine learning of robots in tourism and hospitality: Interactive technology acceptance model (iTAM) – Cutting edge. Tourism Review. https://doi.org/10.1108/TR-02-2019-0062

  • Goh, C., & Law, R. (2003). Incorporating the rough sets theory into travel demand analysis. Tourism Management, 24(5), 511–517.

    Article  Google Scholar 

  • Goh, C., & Law, R. (2011). The methodological progress of tourism demand forecasting: A review of related literature. Journal of Travel and Tourism Marketing, 28(3), 296–317.

    Article  Google Scholar 

  • Guo, R. (2020). Exploration and analysis on the development path of smart tourism in Shandong Province in the era of artificial intelligence. Retrieved from: https://iopscience.iop.org/article/10.1088/1742-6596/1575/1/012022/pdf (Accessed: The 15th September, 2020).

  • Hadavandi, E., Ghanbari, A., Shahanaghi, K., & Abbasian-Naghneh, S. (2011). Tourist arrival forecasting by evolutionary fuzzy systems. Tourism Management, 32(5), 1196–1203.

    Article  Google Scholar 

  • Han, S., Guo, Y., Cao, H., Feng, Q., & Li, Y. (2017). A cross-view model for tourism demand forecasting with artificial intelligence method. In X. Song, W. Xie, Z. Lu, B. Zou, M. Li, & H. Wang (Eds.), Data Science. ICPCSEE 2017. Communications in Computer and Information Science (pp. 573–582). Springer Verlag.

    Google Scholar 

  • Helgemeir, T., & Cenzano, C. H. (2019). Artificial intelligence in tourism software solutions: Opportunities and challenges until 2024. Paper presented at the 28th international conference for the International Association of Management of technology: Managing Technology for Inclusive and Sustainable Growth, IAMOT 2019. Mumbai: The 07th -11th April.

    Google Scholar 

  • Huang, B., & Hao, H. (2020). A novel two-step procedure for tourism demand forecasting. Current Issues in Tourism. https://doi.org/10.1080/13683500.2020.1770705

  • International Institute of Communications. (2020). Artificial Intelligence in the Asia-Pacific Region. Retrieved from: https://www.iicom.org/wp-content/uploads/IIC-AI-Report-2020.pdf (Accessed: the 21st August, 2020).

  • Ivanov, S. H., Webster, C., Stoilova, E., & Slobodskoy, D. (2020). Biosecurity, crisis management, automation technologies and economic performance of travel, tourism and hospitality companies – A conceptual framework. Tourism Economics. https://doi.org/10.1177/1354816620946541

  • Ivanov, S., Webster, C., & Seyyedi, P. (2018). Consumers' attitudes towards the introduction of robots in accommodation establishments. Tourism, 66(3), 302–317.

    Google Scholar 

  • Jiao, E. X., & Chen, J. L. (2019). Tourism forecasting: A review of methodological developments over the last decade. Tourism Economics, 25(3), 469–492.

    Article  Google Scholar 

  • Jin, Y. (2020). Preliminary study of low-altitude tourism forecast under the condition of artificial intelligence. Retrieved from: https://iopscience.iop.org/article/10.1088/1742-6596/1533/3/032027/pdf (Accessed: The 15th September, 2020).

  • Johnson, R. D., Stone, D. L., & Lukaszewski, K. M. (2020). The benefits of eHRM and AI for talent acquisition. Journal of Tourism Futures. https://doi.org/10.1108/JTF-02-2020-0013

  • Kazak, A. N., Chetyrbok, P. V., & Oleinikov, N. N. (2020). Artificial intelligence in the tourism sphere. Retrieved from: https://iopscience.iop.org/article/10.1088/1755-1315/421/4/042020/pdf (Accessed: The 21st august, 2020).

  • Kim, K., Park, O. J., Yun, S., & Yun, H. (2017). What makes tourists feel negatively about tourism destinations? Application of hybrid text mining methodology to smart destination management. Technological Forecasting and Social Change, 123, 362–369.

    Article  Google Scholar 

  • Korstanje, M. E., & Seraphin, H. (2018). Awakening: A critical discussion of the role of robots in the rite of hospitality. In R. A. Krebs (Ed.), Tourism and hospitality: Perspectives, opportunities and challenges (pp. 59–77). Nova Science Publishers.

    Google Scholar 

  • Küçük, K., & Bakan, G. (2013). Water quality simulation of lakes Cernek and Balik at the Kizilirmak wetlands (Turkey). Paper presented at the 10th global congress on ICM: Lessons learned to address new challenges. Marmaris: The 30th October-03rd November.

    Google Scholar 

  • Lee, H. K., & Fernando, Y. (2015). The antecedents and outcomes of the medical tourism supply chain. Tourism Management, 46, 148–157.

    Article  Google Scholar 

  • Leung, X. Y. (2019). Technology-enabled service evolution in tourism: A perspective article. Tourism Review, 75(1), 279–282.

    Article  Google Scholar 

  • Li, J. J., Bonn, M. A., & Ye, B. H. (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. Tourism Management, 73, 172–181.

    Article  Google Scholar 

  • Liu, H., Liu, Y., Wang, Y., & Pan, C. (2019). Hot topics and emerging trends in tourism forecasting research: A scientometric review. Tourism Economics, 25(3), 448–468.

    Article  Google Scholar 

  • Loureiro, A. (2018). There is a fourth industrial revolution: The digital revolution. Worldwide Hospitality and Tourism Themes, 10(6), 740–744.

    Article  Google Scholar 

  • Lu, L., Cai, R., & Gursoy, D. (2019). Developing and validating a service robot integration willingness scale. International Journal of Hospitality Management, 80, 36–51.

    Article  Google Scholar 

  • Martin, B. A. S., Jin, H. S., Wang, D., Nguyen, H., Zhan, K., & Wang, Y. X. (2020). The influence of consumer anthropomorphism on attitudes towards artificial intelligence trip advisors. Journal of Hospitality and Tourism Management, 44, 108–111.

    Article  Google Scholar 

  • Mazanec, J. A. (2007). New frontiers in tourist behavior research: Steps toward causal inference from non-experimental data. Asia Pacific Journal of Tourism Research, 12(3), 223–235.

    Article  Google Scholar 

  • McKinsey & Company. (2017). Artificial Intelligence and Southeast Asia's Future. Retrieved from: https://www.mckinsey.com/~/media/McKinsey/Featured%20Insights/Artificial%20Intelligence/AI%20and%20SE%20ASIA%20future/Artificial-intelligence-and-Southeast-Asias-future.ashx (Accessed: the 21st August, 2020).

  • Menegaki, A. N., & Agiomirgianakis, G. M. (2019). Sustainable technologies in tourist accommodation: A qualitative review. Progress in Industrial Ecology, 13(4), 373–400.

    Article  Google Scholar 

  • Mofokeng, N. E. M., & Matima, T. K. (2018). Future tourism trends: Utilizing non-fungible tokens to aid wildlife conservation. African Journal of Hospitality, Tourism and Leisure, 7(4), 1–20.

    Google Scholar 

  • Mohanty, P. (2020). ICT and sustainable development: Implications for the tourism industry [in press]. In A. Hassan & A. Sharma (Eds.), The emerald handbook of ICT in tourism and hospitality. Emerald Publishing.

    Google Scholar 

  • Moreno-Izquierdo, L., Egorova, G., Peretó-Rovira, A., & Más-Ferrando, A. (2019). Exploring the use of artificial intelligence in price maximisation in the tourism sector: Its application in the case of Airbnb in the Valencian community. Investigaciones Regionales, 2019(42), 113–128.

    Google Scholar 

  • Moreno-Izquierdo, L., Ramón-Rodríguez, A., & Such-Devesa, M. J. (2019). The challenge of long-term tourism competitiveness in the age of innovation: Spain as a case study. Investigaciones Regionales, 2019(42), 13–34.

    Google Scholar 

  • Moro, S., & Rita, P. (2016). Forecasting tomorrow’s tourist. Worldwide Hospitality and Tourism Themes, 8(6), 643–653.

    Article  Google Scholar 

  • Nikkei Asian review. (2020). Robots in, buffets out: Asia hotels try socially distant hospitality. Retrieved from: https://asia.nikkei.com/Spotlight/Coronavirus/Robots-in-buffets-out-Asia-hotels-try-socially-distant-hospitality (Accessed: the 21st August, 2020).

  • Park, S. (2020). Multifaceted trust in tourism service robots. Annals of Tourism Research. https://doi.org/10.1016/j.annals.2020.102888

  • Parvez, M. O. (2020). Use of machine learning technology for tourist and organizational services: High-tech innovation in the hospitality industry. Journal of Tourism Futures. https://doi.org/10.1108/JTF-09-2019-0083

  • Qiu, R., Xu, W., & Li, S. (2016). Agent-based modeling of the spatial diffusion of tourist flow—A case study of Sichuan, China. Journal of China Tourism Research, 12(1), 85–107.

    Article  Google Scholar 

  • Rui, Z., & Wukui, W. (2020). Tourism Design of Forest Park Based on artificial intelligence and GIS system. In J. H. Abawajy, K. K. R. Choo, R. Islam, Z. Xu, & M. Atiquzzaman (Eds.), International conference on applications and techniques in cyber intelligence ATCI 2019 (pp. 1937–1948). Springer.

    Chapter  Google Scholar 

  • Samala, N., Katkam, B. S., Bellamkonda, R. S., & Rodriguez, R. V. (2020). Impact of AI and robotics in the tourism sector: A critical insight. Journal of Tourism Futures. https://doi.org/10.1108/JTF-07-2019-0065

  • Samara, D., Magnisalis, I., & Peristeras, V. (2020). Artificial intelligence and big data in tourism: A systematic literature review. Journal of Hospitality and Tourism Technology. https://doi.org/10.1108/JHTT-12-2018-0118

  • Song, H., Qiu, R. T. R., & Park, J. (2019). A review of research on tourism demand forecasting. Annals of Tourism Research, 75, 338–362.

    Article  Google Scholar 

  • Stylos, N. (2019). Technological evolution and tourist decision-making: A perspective article. Tourism Review, 75(1), 273–278.

    Article  Google Scholar 

  • Sun, G. (2020). Symmetry analysis in analyzing cognitive and emotional attitudes for tourism consumers by applying artificial intelligence python technology. Symmetry. https://doi.org/10.3390/SYM12040606

  • Târnǎveanu, D. A. (2010). A model of decision support system in economy. WSEAS Transactions on Business and Economics, 7(4), 444–453.

    Google Scholar 

  • Tsaih, R.-H., & Hsu, C. C. J. A. I. (2018). Artificial intelligence in smart tourism: A conceptual framework. 2.

    Google Scholar 

  • TTG Asia. (2019). Expedia, AI Singapore join forces on AI to improve online searches for Asian travelers. Retrieved from: https://www.ttgasia.com/2019/09/09/expedia-ai-singapore-join-forces-on-ai-to-improve-online-searches-for-asian-travellers/ (Accessed: the 21st August, 2020).

  • TTG mice. (2019). How AI is impacting Asia’s multibillion dollar corporate travel sector. Retrieved from: https://www.ttgmice.com/2019/03/07/how-ai-is-impacting-asias-multibillion-dollar-corporate-travel-sector/ (Accessed: the 21st August, 2020).

  • Tuomi, A., Tussyadiah, I. P., & Stienmetz, J. (2020). Leveraging LEGO® serious play® to embrace AI and robots in tourism. Annals of Tourism Research, 81. https://doi.org/10.1016/j.annals.2019.06.003

  • Tuomi, A., Tussyadiah, I., Ling, E. C., Miller, G., & Lee, G. (2020). X=(tourism_work) y=(sdg8) while y=true: Automate(x). Annals of Tourism Research, 84, 1–13.

    Article  Google Scholar 

  • Tussyadiah, I. (2020). 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. https://doi.org/10.1016/j.annals.2020.102883

  • Volchek, K., Liu, A., Song, H., & Buhalis, D. (2019). Forecasting tourist arrivals at attractions: Search engine empowered methodologies. Tourism Economics, 25(3), 425–447.

    Article  Google Scholar 

  • Webster, C., & Ivanov, S. (2019). Future tourism in a robot-based economy: A perspective article. Tourism Review, 75(1), 329–332.

    Article  Google Scholar 

  • Webster, C., & Ivanov, S. (2020). Demographic change as a driver for tourism automation. Journal of Tourism Futures. https://doi.org/10.1108/JTF-10-2019-0109

  • Williams, N. L., Ferdinand, N., & Bustard, J. (2019). From WOM to aWOM – The evolution of unpaid influence: A perspective article. Tourism Review, 75(1), 314–318.

    Article  Google Scholar 

  • Wu, D. C. W., Ji, L., He, K., & Tso, K. F. G. (2020). Forecasting tourist daily arrivals with a hybrid SARIMA–LSTM approach. Journal of Hospitality and Tourism Research. https://doi.org/10.1177/1096348020934046

  • Xiang, Z., Shin, S., & Li, N. (2019). Online tourism-related text: A perspective article. Tourism Review, 75(1), 324–328.

    Article  Google Scholar 

  • Zeng, Z., Chen, P. J., & Lew, A. A. (2020). From high-touch to high-tech: COVID-19 drives robotics adoption. Tourism Geographies, 22(3), 724–734.

    Article  Google Scholar 

  • Zhang, B., Li, N., Shi, F., & Law, R. (2020). A deep learning approach for daily tourist flow forecasting with consumer search data. Asia Pacific Journal of Tourism Research, 25(3), 323–339.

    Article  Google Scholar 

  • Zhang, K., Chen, Y., & Li, C. (2019). Discovering the tourists' behaviors and perceptions in a tourism destination by analyzing photos' visual content with a computer deep learning model: The case of Beijing. Tourism Management, 75, 595–608.

    Article  Google Scholar 

  • Zhang, L., & Sun, Z. (2019). The application of artificial intelligence Technology in the Tourism Industry of Jinan. Retrieved from: https://iopscience.iop.org/article/10.1088/1742-6596/1302/3/032005/pdf (Accessed: The 15th September, 2020).

  • Zhang, Y., Li, G., Muskat, B., & Law, R. (2020). Tourism demand Forecasting: A Decomposed Deep Learning Approach. Journal of Travel Research. https://doi.org/10.1177/0047287520919522

  • Zheng, W., Liao, Z., & Lin, Z. (2020). Navigating through the complex transport system: A heuristic approach for city tourism recommendation. Tourism Management. https://doi.org/10.1016/j.tourman.2020.104162

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Authors and Affiliations

  1. Research Scholar, Institute of Hotel & Tourism Management, M.D. University, Rohtak, India

    Himanshi Dhoundiyal

  2. Department of Tourism Studies, Pondicherry University, Pondicherry, India

    Priyakrushna Mohanty

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  1. Himanshi Dhoundiyal
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  2. Priyakrushna Mohanty
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  1. Tourism Consultants Network, The Tourism Society, London, UK

    Azizul Hassan

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Dhoundiyal, H., Mohanty, P. (2022). Artificial Intelligence and Robotics Driving Tourism 4.0: An Exploration. In: Hassan, A. (eds) Handbook of Technology Application in Tourism in Asia. Springer, Singapore. https://doi.org/10.1007/978-981-16-2210-6_57

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