Research Article
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Year 2024, , 46 - 56, 30.09.2024
https://doi.org/10.24288/jttr.1523976

Abstract

References

  • Aarts E. & Wichert, R. (2009). Ambient intelligence. In: Bullinger HJ (ed) Technology guide. Springer, Berlin/Heidelberg, pp 244–249.
  • Abadicio, M. (2019). AI in the travel and tourism industry–Current applications. Emerj, the AI Research and Advisory Company.
  • Basiri, A., Amirian, P., Winstanley, A., & Moore, T. (2018). Making tourist guidance systems more intelligent, adaptive, and personalized using crowd-sourced movement data. Journal of Ambient Intelligence and Humanized Computing, 9, 413-427.
  • Benckendorff, P.J., Xiang Z. & Sheldon P.J. (2019). Tourism information technology. CABI, Boston Bostrom N (2016) Superintelligence: paths, dangers, strategies. Oxford University Press, Oxford Bostrom N.
  • Bowen, J., & Morosan, C. (2018). Beware hospitality industry: the robots are coming. Worldwide Hospitality and Tourism Themes, 10(6), 726-733.
  • 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.
  • Buchanan, B., Sutherland, G., & Feigenbaum, E. A. (1969). Heuristic DENDRAL – A program for generating explanatory hypotheses in organic chemistry. In B. Meltzer & D. Michie (Eds.), Machine Intelligence 4 (pp. 209–254). Edinburgh University Press.
  • Buhalis, D., & Leung, R. (2018). Smart hospitality—Interconnectivity and interoperability towards an ecosystem. International Journal of Hospitality Management, 71, 41-50.
  • Buhalis, D., & Moldavska, I. (2022). Voice assistants in hospitality: Using artificial intelligence for customer service. Journal of Hospitality and Tourism Technology, 13(3), 386–403. https://doi.org/10.1108/JHTT-03-2021-0104.
  • Buhalis, D., Harwood, T., Bogicevic, V., Viglia, G., Beldona, S., & Hofacker, C. (2019). Technological disruptions in services: lessons from tourism and hospitality. Journal Of Service Management, 30(4), 484-506.
  • Bulchand-Gidumal, J. (2016) Aprendizaje profundo y su impacto en turismo. In La actividad turística española en 2015: (edición 2016): 419–422. Síntesis. Bulchand-Gidumal, J. (2022). Impact of artificial intelligence in travel, tourism, and hospitality. In Handbook of e-Tourism (pp. 1943-1962). Cham: Springer International Publishing.
  • CAICT. (2018). 2018 world artificial intelligence industry development blue book. http://www.caict.ac.cn/kxyj/qwfb/bps/201809/P020180918696200669434.pdf.
  • Chen, H. (2019). Success factors impacting artificial intelligence adoption --- Perspective from the telecom industry in China. [Unpublished Doctoral Dissertation]. Department of Business Administration- Information Technology, Old Dominion University.
  • Chen, K.Y. & Wang, C.H. (2007). Support vector regression with genetic algorithms in forecasting tourism demand. Tourism Management, 8(1), 215–226. https://doi.org/10.1016/j.tourman.2005.12.018
  • Chen, R., Liang, C.Y., Hong, W.C., & Gu, D.X. (2015). Forecasting holiday daily tourist flow based on seasonal support vector regression with adaptive genetic algorithm. Applied Soft Computing Journal, 26, 435–443. https://doi.org/10.1016/j.asoc.2014.10.022
  • Cheong, A., Lau, M. W. S., Foo, E., Hedley, J., & Bo, J. W. (2016). Development of a robotic waitersystem.IFAC-PapersOnLine,49 (21),681–686. https://doi.org/10.1016/j.ifacol.2016.10.679
  • Chow, W.S., Shyu, J.C., & Wang, K.C. (1998). Developing a forecast system for hotel occupancy rate using integrated ARIMA models. Journal of International Hospitality, Leisure & Tourism Management, 1(3), 55-80.
  • Claveria, O., & Torra, S. (2014). Forecasting tourism demand to Catalonia: Neural networks vs. time series models. Economic Modelling, 36, 220-228.
  • Claveria, O., Monte, E., & Torra, S. (2015). A new forecasting approach for the hospitality industry. International Journal of Contemporary Hospitality Management, 27(7), 1520-1538.
  • Dalgıç, A., Yaşar, E., & Demir, M. (2024). ChatGPT and learning outcomes in tourism education: The role of digital literacy and individualized learning. Journal of Hospitality, Leisure, Sport & Tourism Education, 34, https://doi.org/10.1016/j.jhlste.2024.100481
  • Demir, M., & Demir, Ş. Ş. (2023). Is ChatGPT the right technology for service individualization and value co-creation? Evidence from the travel industry. Journal of Travel & Tourism Marketing, 40(5), 383-398.
  • Donaire, J. A., Galí, N., & Gulisova, B. (2020). Tracking visitors in crowded spaces using zenith images: Drones and time -lapse. Tourism Management Perspectives, 35,100680. https://doi.org/10.1016/j.tmp.2020.100680.
  • Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., Baabdullah, A. M., Koohang, A., Raghavan, V., Ahuja, M., & Albanna, H. (2023). Opinion Paper: “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. International Journal of Information Management, 71, 1-22. https://doi.org/10.1016/j.ijinfomgt.2023.102642.
  • Fesenmaier, D. R., Xiang, Z., Pan, B., & Law, R. (2011). A framework of search engine use for travel planning. Journal of Travel Research, 50(6), 587–601. https://doi.org/10.1177/0047287510385466.
  • Filloon, W. (2016). Bratwurst-cooking robot is a feat of German engineering. Retrieved on December, 30, 2016.
  • Gao, M., Liu, K., & Wu, Z. (2010). Personalisation in web computing and informatics: Theories, techniques, applications, and future research. Information Systems Frontiers, 12(5), 607-629.
  • García-Madurga, M. Á., & Grilló-Méndez, A. J. (2023). Artificial Intelligence in the tourism industry: An overview of reviews. Administrative Sciences, 13(8), 172.
  • Gavalas, D., Konstantopoulos, C., Mastakas, K., & Pantziou, G. (2014). Mobile recommender systems in tourism. Journal of Network and Computer Applications, 39, 319-333.
  • Gil, D., Hobson, S., Mojsilović, A., Puri, R., & Smith, J. R. (2020). AI for management: An overview. In J. Canals & F. Heukamp (Eds.). The Future of Management in an AI World (pp. 03–19). IESE Business Collection.
  • Giuliani, M., Petrick, R. P. A., Foster, M. E., Gaschler, A., Isard, A., Pateraki, M., & Sigalas, M. (2013, December). Comparing task-based and socially intelligent behaviour in a robot bartender. Paper presented at the ICMI 2013 – 2013 ACM International Conference on Multimodal Interaction (pp. 263–270). http://dx.doi.org/10.1145/2522848.2522869.
  • Goel, P., Kaushik, N., Sivathanu, B., Pillai, R., & Vikas, J. (2022). Consumers’ adoption of artificial intelligence and robotics in hospitality and tourism sector: Literature review and future research agenda. Tourism Review, 77(4), 1081–1096. https://doi.org/10.1108/TR-03-2021-0138.
  • Gretzel, U. (2011). Intelligent systems in tourism: A social science perspective. Annals of tourism research, 38(3), 757-779.
  • Gretzel, U., Sigala, M., Xiang, Z., & Koo, C. (2015). Smart tourism: foundations and developments. Electronic Markets, 25, 179-188.
  • Gursoy, D. (2018). Future of hospitality marketing and management research. Tourism Management Perspectives, 25, 185-188.
  • Hintze, A. (2016). From Reactive Robots to Sentient Machines: The 4 Types of AI. Recuperado de https://www. livescience.com/56858-4-types-artificial-intelligence.html.
  • Hong, W. C., Dong, Y., Chen, L. Y., & Wei, S. Y. (2011). SVR with hybrid chaotic genetic algorithms for tourism demand forecasting. Applied Soft Computing Journal, 11(2), 1881–1890. https://doi.org/10.1016/j.asoc.2010.06.003. Hristova, Y. (2019). Face recognition for the hospitality industry. https://roombre.com/en/blog/hoteltechnology/face-recognition-for-the-hospitality-industry.html. Hu, W., Singh, R. R., & Scalettar, R. T. (2017). Discovering phases, phase transitions, and crossovers through unsupervised machine learning: A critical examination. Physical Review E, 95(6), 062122.
  • Hu, Y., & Min, H. (2023). The dark side of artificial intelligence in service: The “watching-eye” effect and privacy concerns. International Journal of Hospitality Management, 110, https://doi.org/10.1016/j.ijhm.2023.103437.
  • Huang, H. C. (2014). A Study on Artificial Intelligence Forecasting of Resort Demand. Journal of Theoretical & Applied Information Technology, 70(2), 1-11.
  • Ivanov, S. H., & Webster, C. (2017). Adoption of robots, artificial intelligence and service automation by travel, tourism and hospitality companies–a cost-benefit analysis. Artificial Intelligence and Service Automation by Travel, Tourism and Hospitality Companies–A Cost-Benefit Analysis.
  • Ivanov, S. H., Webster, C., & Berezina, K. (2017). Adoption of robots and service automation by tourism and hospitality companies. Revista Turismo & Desenvolvimento, 27(28), 1501-1517.
  • Kim, M. J., Hall, C. M., Kwon, O., Hwang, K., & Kim, J. S. (2023). Orbital and sub-orbital space tourism: Motivation, constraint and artificial intelligence. Tourism Review, 78(3), https://doi.org/10.1108/TR-01-2023-0017.
  • Kılıçhan, R., & Yılmaz, M. (2020). Artificial intelligence and robotic technologies in tourism and hospitality industry. Erciyes Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 50, 353-380.
  • Kurzweil, R. (2005). The singularity is near. In Ethics and emerging technologies (pp. 393-406). London: Palgrave Macmillan UK.
  • LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444..
  • Lee, H., Kim, Y., & Park, J. (2024). Cultural Variability in AI Adoption: A Comparative Study across Markets. International Journal of Technology and Culture, 29(2), 112-130.
  • Li, J., Xu, L., Tang, L., Wang, S., & Li, L. (2018). Big data in tourism research: A literature review. Tourism Management, 68, 301-323.
  • Loureiro, S. M. C., Guerreiro, J., & Ali, F. (2020). 20 years of research on virtual reality and augmented reality in tourism context: A text-mining approach. Tourism Management, 77, 104028. https://doi.org/10.1016/j.tourman.2019.104028.
  • Lv, H., Shi, S., & Gursoy, D. (2022). 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, 31(2), 145–175. https://doi.org/10.1080/19368623.2021.1937434
  • Ma, Y., Xiang, Z., Du, Q., & Fan, W. (2018). Effects of user-provided photos on hotel review helpfulness: An analytical approach with deep leaning. International Journal of Hospitality Management, 71, 120-131.
  • McCorduck, P., & Cfe, C. (2004). Machines who think: A personal inquiry into the history and prospects of artificial intelligence. AK Peters/CRC Press.
  • Melián-González, S., Gutiérrez-Taño, D., & Bulchand-Gidumal, J. (2021). Predicting the intentions to use chatbots for travel and tourism. Current Issues in Tourism, 24(2), 192-210.
  • Murphy, J., Hofacker, C., & Gretzel, U. (2017). Dawning of the age of robots in hospitality and tourism: Challenges for teaching and research. European Journal of Tourism Research, 15(2017), 104-111.
  • Paschen, U., Pitt, C., & Kietzmann, J. (2020). Artificial intelligence: Building blocks and an innovationtypology. BusinessHorizons, 63(2),147–155. https://doi.org/10.1016/j.bushor.2019.10.004.
  • Pereira, T., Limberger, P. F., Minasi, S. M., & Buhalis, D. (2022). New insights into consumers’ intention to continue using chatbots in the tourism context. Journal of Quality Assurance in Hospitality & Tourism, 1–27. https://doi.org/10.1080/1528008X.2022.2136817.
  • Ricci F, Rokach L, Shapira B (2015) Recommender systems: introduction and challenges. In: Recommender systems handbook. Springer, Boston, pp 1–34
  • Rogers, E. M., Singhal, A., & Quinlan, M. M. (2014). Diffusion of innovations. In An integrated approach to communication theory and research (pp. 432-448). Routledge.
  • Russell, S. J., & Norvig, P. (2016). Artificial intelligence: a modern approach. Pearson.
  • Sigala, M. (2018). New technologies in tourism: From multi-disciplinary to anti-disciplinary advances and trajectories. Tourism Management Perspectives, 25, 151-155.
  • Smith, C. S. (2019). Dealing with bias in artificial intelligence. The New York Times, 19.
  • Smith, J., & Jones, R. (2022). The Role of Technical Academics in Applied Research: Opportunities and Limitations. Journal of Applied Research Methods, 10(4), 223-238.
  • Stalidis, G., Karapistolis, D., & Vafeiadis, A. (2015). Marketing decision support using Artificial Intelligence and Knowledge Modelling: application to tourist destination management. Procedia-Social and Behavioural Sciences, 175, 106-113.
  • Sterne, J. (2017). Artificial intelligence for marketing: practical applications. John Wiley & Sons.
  • Tung, V. W. S., & Law, R. (2017). The potential for tourism and hospitality experience research in human-robot interactions. International Journal of Contemporary Hospitality Management, 29(10), 2498-2513.
  • 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: Proceedings of the International Conference in Nicosia, Cyprus, January 30–February 1, 2019 (pp. 359-370). Springer International Publishing.
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Sustainability in tourism and hospitality: Artificial intelligence role in eco-friendly practices in Indian hotels

Year 2024, , 46 - 56, 30.09.2024
https://doi.org/10.24288/jttr.1523976

Abstract

This study explores the impact of artificial intelligence (AI) on the growth and competitiveness of regional tourism and hospitality destinations. AI's application has both positive and negative effects on sustainable tourism. On the positive side, AI enhances accessibility to tourist sites, provides detailed information about attractions, simplifies understanding of costs and amenities, boosts local economies, and increases federal income. A notable downside is the replacement of human workers by machine learning technologies, which may necessitate remedial measures such as training. This review examines AI's foundational IT principles, current applications, and systems in the industry, focusing on the hotel sector. It concludes with an overview of AI’s challenges in this field, proposes a research agenda, and suggests future directions for AI development in tourism and hospitality.

References

  • Aarts E. & Wichert, R. (2009). Ambient intelligence. In: Bullinger HJ (ed) Technology guide. Springer, Berlin/Heidelberg, pp 244–249.
  • Abadicio, M. (2019). AI in the travel and tourism industry–Current applications. Emerj, the AI Research and Advisory Company.
  • Basiri, A., Amirian, P., Winstanley, A., & Moore, T. (2018). Making tourist guidance systems more intelligent, adaptive, and personalized using crowd-sourced movement data. Journal of Ambient Intelligence and Humanized Computing, 9, 413-427.
  • Benckendorff, P.J., Xiang Z. & Sheldon P.J. (2019). Tourism information technology. CABI, Boston Bostrom N (2016) Superintelligence: paths, dangers, strategies. Oxford University Press, Oxford Bostrom N.
  • Bowen, J., & Morosan, C. (2018). Beware hospitality industry: the robots are coming. Worldwide Hospitality and Tourism Themes, 10(6), 726-733.
  • 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.
  • Buchanan, B., Sutherland, G., & Feigenbaum, E. A. (1969). Heuristic DENDRAL – A program for generating explanatory hypotheses in organic chemistry. In B. Meltzer & D. Michie (Eds.), Machine Intelligence 4 (pp. 209–254). Edinburgh University Press.
  • Buhalis, D., & Leung, R. (2018). Smart hospitality—Interconnectivity and interoperability towards an ecosystem. International Journal of Hospitality Management, 71, 41-50.
  • Buhalis, D., & Moldavska, I. (2022). Voice assistants in hospitality: Using artificial intelligence for customer service. Journal of Hospitality and Tourism Technology, 13(3), 386–403. https://doi.org/10.1108/JHTT-03-2021-0104.
  • Buhalis, D., Harwood, T., Bogicevic, V., Viglia, G., Beldona, S., & Hofacker, C. (2019). Technological disruptions in services: lessons from tourism and hospitality. Journal Of Service Management, 30(4), 484-506.
  • Bulchand-Gidumal, J. (2016) Aprendizaje profundo y su impacto en turismo. In La actividad turística española en 2015: (edición 2016): 419–422. Síntesis. Bulchand-Gidumal, J. (2022). Impact of artificial intelligence in travel, tourism, and hospitality. In Handbook of e-Tourism (pp. 1943-1962). Cham: Springer International Publishing.
  • CAICT. (2018). 2018 world artificial intelligence industry development blue book. http://www.caict.ac.cn/kxyj/qwfb/bps/201809/P020180918696200669434.pdf.
  • Chen, H. (2019). Success factors impacting artificial intelligence adoption --- Perspective from the telecom industry in China. [Unpublished Doctoral Dissertation]. Department of Business Administration- Information Technology, Old Dominion University.
  • Chen, K.Y. & Wang, C.H. (2007). Support vector regression with genetic algorithms in forecasting tourism demand. Tourism Management, 8(1), 215–226. https://doi.org/10.1016/j.tourman.2005.12.018
  • Chen, R., Liang, C.Y., Hong, W.C., & Gu, D.X. (2015). Forecasting holiday daily tourist flow based on seasonal support vector regression with adaptive genetic algorithm. Applied Soft Computing Journal, 26, 435–443. https://doi.org/10.1016/j.asoc.2014.10.022
  • Cheong, A., Lau, M. W. S., Foo, E., Hedley, J., & Bo, J. W. (2016). Development of a robotic waitersystem.IFAC-PapersOnLine,49 (21),681–686. https://doi.org/10.1016/j.ifacol.2016.10.679
  • Chow, W.S., Shyu, J.C., & Wang, K.C. (1998). Developing a forecast system for hotel occupancy rate using integrated ARIMA models. Journal of International Hospitality, Leisure & Tourism Management, 1(3), 55-80.
  • Claveria, O., & Torra, S. (2014). Forecasting tourism demand to Catalonia: Neural networks vs. time series models. Economic Modelling, 36, 220-228.
  • Claveria, O., Monte, E., & Torra, S. (2015). A new forecasting approach for the hospitality industry. International Journal of Contemporary Hospitality Management, 27(7), 1520-1538.
  • Dalgıç, A., Yaşar, E., & Demir, M. (2024). ChatGPT and learning outcomes in tourism education: The role of digital literacy and individualized learning. Journal of Hospitality, Leisure, Sport & Tourism Education, 34, https://doi.org/10.1016/j.jhlste.2024.100481
  • Demir, M., & Demir, Ş. Ş. (2023). Is ChatGPT the right technology for service individualization and value co-creation? Evidence from the travel industry. Journal of Travel & Tourism Marketing, 40(5), 383-398.
  • Donaire, J. A., Galí, N., & Gulisova, B. (2020). Tracking visitors in crowded spaces using zenith images: Drones and time -lapse. Tourism Management Perspectives, 35,100680. https://doi.org/10.1016/j.tmp.2020.100680.
  • Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., Baabdullah, A. M., Koohang, A., Raghavan, V., Ahuja, M., & Albanna, H. (2023). Opinion Paper: “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. International Journal of Information Management, 71, 1-22. https://doi.org/10.1016/j.ijinfomgt.2023.102642.
  • Fesenmaier, D. R., Xiang, Z., Pan, B., & Law, R. (2011). A framework of search engine use for travel planning. Journal of Travel Research, 50(6), 587–601. https://doi.org/10.1177/0047287510385466.
  • Filloon, W. (2016). Bratwurst-cooking robot is a feat of German engineering. Retrieved on December, 30, 2016.
  • Gao, M., Liu, K., & Wu, Z. (2010). Personalisation in web computing and informatics: Theories, techniques, applications, and future research. Information Systems Frontiers, 12(5), 607-629.
  • García-Madurga, M. Á., & Grilló-Méndez, A. J. (2023). Artificial Intelligence in the tourism industry: An overview of reviews. Administrative Sciences, 13(8), 172.
  • Gavalas, D., Konstantopoulos, C., Mastakas, K., & Pantziou, G. (2014). Mobile recommender systems in tourism. Journal of Network and Computer Applications, 39, 319-333.
  • Gil, D., Hobson, S., Mojsilović, A., Puri, R., & Smith, J. R. (2020). AI for management: An overview. In J. Canals & F. Heukamp (Eds.). The Future of Management in an AI World (pp. 03–19). IESE Business Collection.
  • Giuliani, M., Petrick, R. P. A., Foster, M. E., Gaschler, A., Isard, A., Pateraki, M., & Sigalas, M. (2013, December). Comparing task-based and socially intelligent behaviour in a robot bartender. Paper presented at the ICMI 2013 – 2013 ACM International Conference on Multimodal Interaction (pp. 263–270). http://dx.doi.org/10.1145/2522848.2522869.
  • Goel, P., Kaushik, N., Sivathanu, B., Pillai, R., & Vikas, J. (2022). Consumers’ adoption of artificial intelligence and robotics in hospitality and tourism sector: Literature review and future research agenda. Tourism Review, 77(4), 1081–1096. https://doi.org/10.1108/TR-03-2021-0138.
  • Gretzel, U. (2011). Intelligent systems in tourism: A social science perspective. Annals of tourism research, 38(3), 757-779.
  • Gretzel, U., Sigala, M., Xiang, Z., & Koo, C. (2015). Smart tourism: foundations and developments. Electronic Markets, 25, 179-188.
  • Gursoy, D. (2018). Future of hospitality marketing and management research. Tourism Management Perspectives, 25, 185-188.
  • Hintze, A. (2016). From Reactive Robots to Sentient Machines: The 4 Types of AI. Recuperado de https://www. livescience.com/56858-4-types-artificial-intelligence.html.
  • Hong, W. C., Dong, Y., Chen, L. Y., & Wei, S. Y. (2011). SVR with hybrid chaotic genetic algorithms for tourism demand forecasting. Applied Soft Computing Journal, 11(2), 1881–1890. https://doi.org/10.1016/j.asoc.2010.06.003. Hristova, Y. (2019). Face recognition for the hospitality industry. https://roombre.com/en/blog/hoteltechnology/face-recognition-for-the-hospitality-industry.html. Hu, W., Singh, R. R., & Scalettar, R. T. (2017). Discovering phases, phase transitions, and crossovers through unsupervised machine learning: A critical examination. Physical Review E, 95(6), 062122.
  • Hu, Y., & Min, H. (2023). The dark side of artificial intelligence in service: The “watching-eye” effect and privacy concerns. International Journal of Hospitality Management, 110, https://doi.org/10.1016/j.ijhm.2023.103437.
  • Huang, H. C. (2014). A Study on Artificial Intelligence Forecasting of Resort Demand. Journal of Theoretical & Applied Information Technology, 70(2), 1-11.
  • Ivanov, S. H., & Webster, C. (2017). Adoption of robots, artificial intelligence and service automation by travel, tourism and hospitality companies–a cost-benefit analysis. Artificial Intelligence and Service Automation by Travel, Tourism and Hospitality Companies–A Cost-Benefit Analysis.
  • Ivanov, S. H., Webster, C., & Berezina, K. (2017). Adoption of robots and service automation by tourism and hospitality companies. Revista Turismo & Desenvolvimento, 27(28), 1501-1517.
  • Kim, M. J., Hall, C. M., Kwon, O., Hwang, K., & Kim, J. S. (2023). Orbital and sub-orbital space tourism: Motivation, constraint and artificial intelligence. Tourism Review, 78(3), https://doi.org/10.1108/TR-01-2023-0017.
  • Kılıçhan, R., & Yılmaz, M. (2020). Artificial intelligence and robotic technologies in tourism and hospitality industry. Erciyes Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 50, 353-380.
  • Kurzweil, R. (2005). The singularity is near. In Ethics and emerging technologies (pp. 393-406). London: Palgrave Macmillan UK.
  • LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444..
  • Lee, H., Kim, Y., & Park, J. (2024). Cultural Variability in AI Adoption: A Comparative Study across Markets. International Journal of Technology and Culture, 29(2), 112-130.
  • Li, J., Xu, L., Tang, L., Wang, S., & Li, L. (2018). Big data in tourism research: A literature review. Tourism Management, 68, 301-323.
  • Loureiro, S. M. C., Guerreiro, J., & Ali, F. (2020). 20 years of research on virtual reality and augmented reality in tourism context: A text-mining approach. Tourism Management, 77, 104028. https://doi.org/10.1016/j.tourman.2019.104028.
  • Lv, H., Shi, S., & Gursoy, D. (2022). 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, 31(2), 145–175. https://doi.org/10.1080/19368623.2021.1937434
  • Ma, Y., Xiang, Z., Du, Q., & Fan, W. (2018). Effects of user-provided photos on hotel review helpfulness: An analytical approach with deep leaning. International Journal of Hospitality Management, 71, 120-131.
  • McCorduck, P., & Cfe, C. (2004). Machines who think: A personal inquiry into the history and prospects of artificial intelligence. AK Peters/CRC Press.
  • Melián-González, S., Gutiérrez-Taño, D., & Bulchand-Gidumal, J. (2021). Predicting the intentions to use chatbots for travel and tourism. Current Issues in Tourism, 24(2), 192-210.
  • Murphy, J., Hofacker, C., & Gretzel, U. (2017). Dawning of the age of robots in hospitality and tourism: Challenges for teaching and research. European Journal of Tourism Research, 15(2017), 104-111.
  • Paschen, U., Pitt, C., & Kietzmann, J. (2020). Artificial intelligence: Building blocks and an innovationtypology. BusinessHorizons, 63(2),147–155. https://doi.org/10.1016/j.bushor.2019.10.004.
  • Pereira, T., Limberger, P. F., Minasi, S. M., & Buhalis, D. (2022). New insights into consumers’ intention to continue using chatbots in the tourism context. Journal of Quality Assurance in Hospitality & Tourism, 1–27. https://doi.org/10.1080/1528008X.2022.2136817.
  • Ricci F, Rokach L, Shapira B (2015) Recommender systems: introduction and challenges. In: Recommender systems handbook. Springer, Boston, pp 1–34
  • Rogers, E. M., Singhal, A., & Quinlan, M. M. (2014). Diffusion of innovations. In An integrated approach to communication theory and research (pp. 432-448). Routledge.
  • Russell, S. J., & Norvig, P. (2016). Artificial intelligence: a modern approach. Pearson.
  • Sigala, M. (2018). New technologies in tourism: From multi-disciplinary to anti-disciplinary advances and trajectories. Tourism Management Perspectives, 25, 151-155.
  • Smith, C. S. (2019). Dealing with bias in artificial intelligence. The New York Times, 19.
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There are 66 citations in total.

Details

Primary Language English
Subjects Recreation Management, Sustainable Tourism
Journal Section Research Articles
Authors

Madhu Kumari 0009-0006-7300-3597

Sandeep Guleria This is me 0000-0001-5836-1691

Suneel Kumar This is me 0000-0002-8412-3993

Early Pub Date August 25, 2024
Publication Date September 30, 2024
Submission Date July 31, 2024
Acceptance Date August 25, 2024
Published in Issue Year 2024

Cite

APA Kumari, M., Guleria, S., & Kumar, S. (2024). Sustainability in tourism and hospitality: Artificial intelligence role in eco-friendly practices in Indian hotels. Journal of Tourism Theory and Research, 10(2), 46-56. https://doi.org/10.24288/jttr.1523976
AMA Kumari M, Guleria S, Kumar S. Sustainability in tourism and hospitality: Artificial intelligence role in eco-friendly practices in Indian hotels. Journal of Tourism Theory and Research. September 2024;10(2):46-56. doi:10.24288/jttr.1523976
Chicago Kumari, Madhu, Sandeep Guleria, and Suneel Kumar. “Sustainability in Tourism and Hospitality: Artificial Intelligence Role in Eco-Friendly Practices in Indian Hotels”. Journal of Tourism Theory and Research 10, no. 2 (September 2024): 46-56. https://doi.org/10.24288/jttr.1523976.
EndNote Kumari M, Guleria S, Kumar S (September 1, 2024) Sustainability in tourism and hospitality: Artificial intelligence role in eco-friendly practices in Indian hotels. Journal of Tourism Theory and Research 10 2 46–56.
IEEE M. Kumari, S. Guleria, and S. Kumar, “Sustainability in tourism and hospitality: Artificial intelligence role in eco-friendly practices in Indian hotels”, Journal of Tourism Theory and Research, vol. 10, no. 2, pp. 46–56, 2024, doi: 10.24288/jttr.1523976.
ISNAD Kumari, Madhu et al. “Sustainability in Tourism and Hospitality: Artificial Intelligence Role in Eco-Friendly Practices in Indian Hotels”. Journal of Tourism Theory and Research 10/2 (September 2024), 46-56. https://doi.org/10.24288/jttr.1523976.
JAMA Kumari M, Guleria S, Kumar S. Sustainability in tourism and hospitality: Artificial intelligence role in eco-friendly practices in Indian hotels. Journal of Tourism Theory and Research. 2024;10:46–56.
MLA Kumari, Madhu et al. “Sustainability in Tourism and Hospitality: Artificial Intelligence Role in Eco-Friendly Practices in Indian Hotels”. Journal of Tourism Theory and Research, vol. 10, no. 2, 2024, pp. 46-56, doi:10.24288/jttr.1523976.
Vancouver Kumari M, Guleria S, Kumar S. Sustainability in tourism and hospitality: Artificial intelligence role in eco-friendly practices in Indian hotels. Journal of Tourism Theory and Research. 2024;10(2):46-5.