Artificial Intelligence in Tourism: A Review and Bibliometrics Research

Year 2021, , 205 - 233, 01.06.2021

İsmail Gökay Kırtıl , Volkan Aşkun

https://doi.org/10.30519/ahtr.801690

Cited By: 18

Abstract

Artificial Intelligence (AI) came up as an ambiguous concept from computer sciences and now it is being used in many areas of our life. It has stimulated academia’s interest due to its alternative insights into complex problems. Therefore, a bibliometric method was applied in this study to observe the progress of AI in the tourism field. A total of 102 papers were collected from Scopus database. Key factors such as most productive authors, collaborations and institutions were identified, and research hotspots were determined using co-occurrence network and most common author keywords. Progress of AI was visualized with thematic evolution analysis. Findings indicate that there is a progressive interest in AI after 2017, and average citations signify that papers are highly cited. Since this is the first study conducting a bibliometric on AI in the tourism context, it could be considered useful for academics and tourism professionals as it provides general overview of AI, demonstrates research trends and popular papers.

Keywords

bibliometric, artificial intelligence, hospitality and tourism, co-citation analysis, co-occurrence analysis, thematic analysis

References

• Ajiferuke, I., Burell, Q., & Tague, J. (1988). Collaborative coefficient: A single measure of the degree of collaboration in research. Scientometrics, 14(5–6), 421–433.
• Akehurst, G. (2009). User generated content: The use of blogs for tourism organisations and tourism consumers. Service Business, 3(1), 51–61. https://doi.org/10.1007/s11628-008-0054-2
• Andreu, L., Bigne, E., Amaro, S., & Palomo, J. (2020). Airbnb research: an analysis in tourism and hospitality journals. International Journal of Culture, Tourism, and Hospitality Research, 14(1), 2–20. https://doi.org/10.1108/IJCTHR-06-2019-0113
• Aria, M., & Cuccurullo, C. (2017). Bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975. https://doi.org/10.1016/j.joi.2017.08.007
• Askun, V., & Cizel, R. (2019). Kompleks problem çözme üzerine R programı ile bir bibliyometrik analiz. Mediterranean Journal of Humanities, 9(1), 37–47. https://doi.org/10.13114/mjh.2019.445
• Askun, V., & Cizel, R. (2020). Twenty years of research on mixed methods. Journal of Mixed Methods Research, 1(1), 28–43. https://doi.org/10.14689/jomes.2020.1.2
• Barrios, M., Borrego, A., Vilaginés, A., Ollé, C., & Somoza, M. (2008). A bibliometric study of psychological research on tourism. Scientometrics, 77(3), 453–467. https://doi.org/10.1007/s11192-007-1952-0
• Benckendorff, P. (2009). Themes and trends in Australian and New Zealand tourism research: A social network analysis of citations in two leading journals (1994-2007). Journal of Hospitality and Tourism Management, 16(1), 1–15. https://doi.org/10.1375/jhtm.16.1.1
• Benckendorff, P., & Zehrer, A. (2013). A network analysis of tourism research. Annals of Tourism Research, 43, 121–149. https://doi.org/10.1016/j.annals.2013.04.005
• Börner, K., Chen, C., & Boyack, K. W. (2005). Visualizing knowledge domains. Annual Review of Information Science and Technology, 37, 179–255. https://doi.org/10.1002/aris.1440370106
• Borràs, J., Moreno, A., & Valls, A. (2014). Intelligent tourism recommender systems: A survey. Expert Systems with Applications, 41, 7370–7389. https://doi.org/10.1016/j.eswa.2014.06.007
• Bowen, J., & Whalen, E. (2017). Trends that are changing travel and tourism. Worldwide Hospitality and Tourism Themes, 9(6), 592–602. https://doi.org/10.1108/WHATT-09-2017-0045
• Buhalis, D. (2003). eTourism: Information technology for strategic tourism management. London: Pearson Education.
• 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. https://doi.org/10.1108/JOSM-12-2018-0398
• Buhalis, D., & Sinarta, Y. (2019). Real-time co-creation and nowness service: Lessons from tourism and hospitality. Journal of Travel & Tourism Marketing, 36(5), 563–582. https://doi.org/10.1080/10548408.2019.1592059
• Cahlik, T. (2000). Search for fundamental articles in economics. Scientometrics, 49(3), 389–402.
• Cain, L. N., Thomas, J. H., & Alonso, M. (2019). From sci-fi to sci-fact: The state of robotics and AI in the hospitality industry. Journal of Hospitality and Tourism Technology, 10(4), 624–650. https://doi.org/10.1108/JHTT-07-2018-0066
• Casteleiro-Roca, J.-L., Gomez-Gonzalez, J. F., Calvo-Rolle, J. L., Jove, E., Quintian, H., Acosta Martin, J. F., … Mendez-Perez, J. A. (2018). Prediction of the energy demand of a hotel using an artificial intelligence-based model. In J. F. de Cos Juez, J. R. Villar, E. A. de la Cal & A. Herrero (Eds.), Hybrid Artificial Intelligent Systems (Vol. 1, pp. 586–596). Cham: Springer. https://doi.org/10.1007/978-3-319-92639-1
• Chiu, W. T., & Ho, Y. S. (2007). Bibliometric analysis of tsunami research. Scientometrics, 73(1), 3–17. https://doi.org/10.1007/s11192-005-1523-1
• Cho, V. (2003). A comparison of three different approaches to tourist arrival forecasting. Tourism Management, 24(3), 323–330. https://doi.org/10.1016/S0261-5177(02)00068-7
• Chui, M., Manyika, J., Miremadi, M., Henke, N., Chung, R., Nel, P., & Malhotra, S. (2018). Notes from the AI frontier: Insights from hundreds of use cases. In McKinsey& Company. Retrieved September 10, 2020, from https://www.mckinsey.com/~/media/mckinsey/featured%20insights/artificial%20intelligence/notes%20from%20the%20ai%20frontier%20applications%20and%20value%20of%20deep%20learning/notes-from-the-ai-frontier-insights-from-hundreds-of-use-cases-discussion-paper.ashx.
• Cobo, M. J., López-Herrera, A. G., Herrera-Viedma, E., & Herrera, F. (2011). Science mapping software tools: Review, analysis, and cooperative study among tools. Journal of the American Society for Information Science and Technology, 62(7), 1382–1402. https://doi.org/10.1002/asi.21525
• Comerio, N., & Strozzi, F. (2019). Tourism and its economic impact: A literature review using bibliometric tools. Tourism Economics, 25(1), 109–131. https://doi.org/10.1177/1354816618793762
• Corchado, J. M., & Lees, B. (1998). Cognitive models for integrating artificial intelligence approaches. AII Workshop on Knowledge Discovery. Glasgow, UK.
• Cunill, O. M., Salvá, A. S., Gonzalez, L. O., & Mulet-Forteza, C. (2019). Thirty-fifth anniversary of the International Journal of Hospitality Management: A bibliometric overview. International Journal of Hospitality Management, 78, 89–101. https://doi.org/10.1016/j.ijhm.2018.10.013
• Dhamija, P., & Bag, S. (2020). Role of artificial intelligence in operations environment: A review and bibliometric analysis. TQM Journal, 32(4), 869–896. https://doi.org/10.1108/TQM-10-2019-0243
• 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. https://doi.org/10.4018/JCIT.2020010101
• Freeman, L. C. (1977). A set of measures of centrality based on betweenness. Sociometry, 40(1), 35- 41. https://doi.org/10.2307/3033543
• Gajdošík, T., & Marciš, M. (2019). Artificial intelligence tools for smart tourism development. In R. Silhavy (Ed.), Artificial Intelligence Methods in Intelligent Algorithms (Vol. 985, pp. 392–402). Cham: Springer. https://doi.org/10.1007/978-3-030-19810-7_39
• García-Lillo, F., Úbeda-García, M., & Marco-Lajara, B. (2016). The intellectual structure of research in hospitality management: A literature review using bibliometric methods of the journal International Journal of Hospitality Management. International Journal of Hospitality Management, 52, 121–130. https://doi.org/10.1016/j.ijhm.2015.10.007
• Glänzel, W., & Schubert, A. (2005). Analysing scientifc networks through co-authorship. In H. F. Moed, W. Glänzel, & U. Schmoch (Eds.), Handbook of quantitative science and technology research (pp. 257–276). Berlin: Springer.
• González-Rodríguez, M. R., Díaz-Fernández, M. C., & Pacheco Gómez, C. (2020). Facial-expression recognition: An emergent approach to the measurement of tourist satisfaction through emotions. Telematics and Informatics, 51. https://doi.org/10.1016/j.tele.2020.101404
• Gretzel, U. (2011). Intelligent systems in tourism. A social science perspective. Annals of Tourism Research, 38(3), 757–779. https://doi.org/10.1016/j.annals.2011.04.014
• Gretzel, U., Fesenmaier, D. R., & O’Leary, J. T. (2006). The transformation of consumer behaviour. In D. Buhalis & C. Costa (Eds.), Tourism Business Frontiers: Consumers, Products and Industry (pp. 9–18). Oxford: Elsevier. https://doi.org/10.1016/b978-0-7506-6377-9.50009-2
• Gretzel, U., Fuchs, M., Baggio, R., Hoepken, W., Law, R., Neidhardt, J., … Xiang, Z. (2020). e-Tourism beyond COVID-19: a call for transformative research. Information Technology and Tourism, 22(2), 187–203. https://doi.org/10.1007/s40558-020-00181-3
• Gunbayi, I., & Sorm, S. (2018). Social paradigms in guiding social research design: The functional, interpretive, radical humanist and radical structural paradigms. International Journal on New Trends in Education and Their Implications, 9(2), 57-76.
• Guns, R., Liu, Y. X., & Mahbuba, D. (2011). Q-measures and betweenness centrality in a collaboration network: A case study of the field of informetrics. Scientometrics, 87(1), 133–147. https://doi.org/10.1007/s11192-010-0332-3
• Guzeller, C. O., & Celiker, N. (2019). Bibliometrical analysis of Asia Pacific Journal of Tourism Research. Asia Pacific Journal of Tourism Research, 24(1), 108–120. https://doi.org/10.1080/10941665.2018.1541182
• Güzeller, C. O., & Çeli̇ker, N. (2018). Bibliometric analysis of tourism research for the period 2007-2016. Advances in Hospitality and Tourism Research, 6(1), 1–22. https://doi.org/10.30519/ahtr.446248
• Hadavandi, E., Ghanbari, A., Shahanaghi, K., & Abbasian-Naghneh, S. (2011). Tourist arrival forecasting by evolutionary fuzzy systems. Tourism Management, 32(5), 1196–1203. https://doi.org/10.1016/j.tourman.2010.09.015
• Inanc-Demir, M., & Kozak, M. (2019). Big data and its supporting elements: implications for tourism and hospitality marketing. In M. Sigala, R. Rahimi, & M. Thelwall (Eds.), Big Data and Innovation in Tourism, Travel, and Hospitality: Managerial Approaches, Techniques, and Applications (pp. 213-223). Singapore: Springer. https://doi.org/10.1007/978-981-13-6339-9
• Ivanov, S., & Webster, C. (2017). Adoption of robots, artificial intelligence and service automation by travel, tourism and hospitality companies – a cost-benefit analysis. In V. Marinov, M. Vodenska, M. Assenova & E. Dogramadjieva (Eds.) Traditions and Innovations in Contemporary Tourism (pp. 190-203). UK: Cambridge Scholars Publishing.
• Johnson, A. G., & Samakovlis, I. (2019). A bibliometric analysis of knowledge development in smart tourism research. Journal of Hospitality and Tourism Technology, 10(4), 600–623. https://doi.org/10.1108/JHTT-07-2018-0065
• Kazak, A. N., Chetyrbok, P. V., & Oleinikov, N. N. (2020). Artificial intelligence in the tourism sphere. IOP Conference Series: Earth and Environmental Science, 421(4). https://doi.org/10.1088/1755-1315/421/4/042020
• Kirilenko, A. P., Stepchenkova, S. O., Kim, H., & Li, X. (Robert). (2018). Automated sentiment analysis in tourism: Comparison of approaches. Journal of Travel Research, 57(8), 1012–1025. https://doi.org/10.1177/0047287517729757
• Köseoglu, M. A., Okumus, F., Putra, E. D., Yildiz, M., & Dogan, I. C. (2018). Authorship trends, collaboration patterns, and co-authorship networks in lodging studies (1990–2016). Journal of Hospitality Marketing and Management, 27(5), 561–582. https://doi.org/10.1080/19368623.2018.1399192
• Koseoglu, M. A., Rahimi, R., Okumus, F., & Liu, J. (2016). Bibliometric studies in tourism. Annals of Tourism Research, 61, 180–198. https://doi.org/10.1016/j.annals.2016.10.006
• Kuhn, T. S. (1962). The structure of scientific revolutions. Chicago: University of Chicago press.
• Law, R. (1998). Room occupancy rate forecasting: A neural network approach. International Journal of Contemporary Hospitality Management, 10(6), 234–239. https://doi.org/10.1108/09596119810232301
• Law, R. (2000). Back-propagation learning in improving the accuracy of neural network-based tourism demand forecasting. Tourism Management, 21(4), 331–340. https://doi.org/10.1016/S0261-5177(99)00067-9
• Law, R., Leung, R., & Buhalis, D. (2010). An analysis of academic leadership in hospitality and tourism journals. Journal of Hospitality and Tourism Research, 34(4), 455–477. https://doi.org/10.1177/1096348010370866
• Lei, Y., & Liu, Z. (2019). The development of artificial intelligence: A bibliometric analysis, 2007-2016. Journal of Physics: Conference Series, 1168(2). https://doi.org/10.1088/1742-6596/1168/2/022027
• Leung, X. Y., Sun, J., & Bai, B. (2017). Bibliometrics of social media research: A co-citation and co-word analysis. International Journal of Hospitality Management, 66, 35–45. https://doi.org/10.1016/j.ijhm.2017.06.012
• Littell, J. H., Corcoran, J., & Pillai, V. (2008). Systematic reviews and meta-analysis. New York: Oxford University Press.
• McCarthy, J., Minksy, M., Rochester, L., & Shannon, C. E. (1955). A proposal for the Dartmouth summer research project on artificial intelligence. AI magazine, 27(4), 12-12. Retrieved December 5, 2020, from http://www-formal.stanford.edu/jmc/history/dartmouth.pdf
• Merigó, J. M., Mulet-Forteza, C., Valencia, C., & Lew, A. A. (2019). Twenty years of Tourism Geographies: A bibliometric overview. Tourism Geographies, 21(5), 881–910. https://doi.org/10.1080/14616688.2019.1666913
• Mich, L. (2020). Artificial intelligence and machine learning. In Z. Xiang, M. Fuchs, U. Gretzel, & W. Höpken (Eds.), Handbook of e-Tourism. Springer Nature Switzerland. https://doi.org/10.1007/978-3-030-05324-6_25-1
• Niu, J., Tang, W., Xu, F., Zhou, X., & Song, Y. (2016). Global research on artificial intelligence from 1990-2014: Spatially-explicit bibliometric analysis. ISPRS International Journal of Geo-Information, 5(5), 1–19. https://doi.org/10.3390/ijgi5050066
• Norris, M., & Oppenheim, C. (2007). Comparing alternatives to the Web of Science for coverage of the social sciences’ literature. Journal of Informetrics, 1(2), 161–169. https://doi.org/10.1016/j.joi.2006.12.001
• Núñez-Tabales, J. M., Solano-Sánchez, M. Á., & Caridad-y-López-del-Río, L. (2020). Ten years of Airbnb phenomenon research: A bibliometric approach (2010–2019). Sustainability, 12(15). https://doi.org/10.3390/su12156205
• Okumus, B., Koseoglu, M. A., & Ma, F. (2018). Food and gastronomy research in tourism and hospitality: A bibliometric analysis. International Journal of Hospitality Management, 73, 64–74. https://doi.org/10.1016/j.ijhm.2018.01.020
• Okumus, F., Köseoglu, M. A., Putra, E. D., Dogan, I. C., & Yildiz, M. (2019). A bibliometric analysis of lodging-context research from 1990 to 2016. Journal of Hospitality and Tourism Research, 43(2), 210–225. https://doi.org/10.1177/1096348018765321
• Palys, T. (2008). Purposive sampling. In L. M. Given (Ed.), The Sage encyclopedia of qualitative research methods (pp. 697–698). California: Sage.
• Pannu, A. (2015). Artificial intelligence and its application in different areas. International Journal of Engineering and Innovative Technology, 4(10), 79–84.
• Pappas, N. (2019). UK outbound travel and Brexit complexity. Tourism Management, 72, 12–22. https://doi.org/10.1016/j.tourman.2018.11.004
• Pavaloiu, A., Köse, U., & Boz, H. (2017). How to apply artificial intelligence in social sciences. IASOS - Congress of International Applied Social Sciences, (September). Uşak, Turkey. Retrieved September 10, 2020, from https://www.researchgate.net/publication/325398286_How_to_Apply_Artificial_Intelligence_in_Social_Sciences
• Perrault, R., Shoham, Y., Brynjolfsson, E., Clark, J., Etchemendy, J., Grosz Harvard, B., … Mishra, S. (2019). The AI Index 2019 Annual Report. In AI Index Steering Committee, Human-Centered AI Institute. Stanford. Retrieved September 10, 2020, from https://hai.stanford.edu/sites/g/files/sbiybj10986/f/ai_index_2019_report.pdf
• Pritchard, A. (1969). Statistical bibliography or bibliometrics. Journal of Documentation, 25(4), 348–349.
• Qian, J., Law, R., & Wei, J. (2019). Knowledge mapping in travel website studies: A scientometric review. Scandinavian Journal of Hospitality and Tourism, 19(2), 192–209. https://doi.org/10.1080/15022250.2018.1526113
• Ritchie, B. W. (2004). Chaos, crises and disasters: A strategic approach to crisis management in the tourism industry. Tourism Management, 25(6), 669–683. https://doi.org/10.1016/j.tourman.2003.09.004
• Ruhanen, L., Weiler, B., Moyle, B. D., & McLennan, C. J. (2015). Trends and patterns in sustainable tourism research: A 25-year bibliometric analysis. Journal of Sustainable Tourism, 23(4), 517–535. https://doi.org/10.1080/09669582.2014.978790
• Ruiz-Real, J. L., Uribe-Toril, J., Valenciano, J. de P., & Gázquez-Abad, J. C. (2020). Rural tourism and development: Evolution in scientific literature and trends. Journal of Hospitality and Tourism Research, 1–25. https://doi.org/10.1177/1096348020926538
• Russell, S. J., & Norvig, P. (2016). Artificial intelligence: A modern approach (3rd ed.). Harlow: Pearson Education.
• Samara, D., Magnisalis, I., & Peristeras, V. (2020). Artificial intelligence and big data in tourism: A systematic literature review. Journal of Hospitality and Tourism Technology, 11(2), 343-367. https://doi.org/10.1108/JHTT-12-2018-0118
• Sánchez, A. D., de la Cruz Del Río Rama, M., & García, J. Á. (2017). Bibliometric analysis of publications on wine tourism in the databases Scopus and WoS. European Research on Management and Business Economics, 23(1), 8–15. https://doi.org/10.1016/j.iedeen.2016.02.001
• Shukla, A. K., Janmaijaya, M., Abraham, A., & Muhuri, P. K. (2019). Engineering applications of artificial intelligence: A bibliometric analysis of 30 years (1988–2018). Engineering Applications of Artificial Intelligence, 85, 517–532. https://doi.org/10.1016/j.engappai.2019.06.010
• Singh, S. K., Rathore, S., & Park, J. H. (2020). BlockIoTIntelligence: A blockchain-enabled intelligent IoT architecture with artificial intelligence. Future Generation Computer Systems, 110, 721–743. https://doi.org/10.1016/j.future.2019.09.002
• Song, H., Qiu, R. T. R., & Park, J. (2019). A review of research on tourism demand forecasting. Annals of Tourism Research, 75, 338–362. https://doi.org/10.1016/j.annals.2018.12.001
• Teixeira, S. J., & Ferreira, J. J. D. M. (2018). A bibliometric study of regional competitiveness and tourism innovation. International Journal of Tourism Policy, 8(3), 214–243. https://doi.org/10.1504/IJTP.2018.094483
• Thomas, R. (2019). The AI ladder: Demystifying AI challenges. In: IBM and O’Reilly. Retrieved September 10, 2020, from https://www.oreilly.com/online-learning/report/The-AI-Ladder.pdf.
• Todeschini, R., & Baccini, A. (2016). Handbook of bibliometric indicators : Quantitative tools for studying and evaluating research. Weinheim, Germany: Wiley-VCH.
• Topal, I., & Uçar, M. K. (2018). In tourism, using artificial intelligence forecasting with Tripadvisor data : Year of Turkey in China. International Conference on Artificial Intelligence and Data Processing (IDAP), 1–5. Retrieved February 15, 2020, from https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8620874
• Tran, B., Vu, G., Ha, G., Vuong, Q.-H., Ho, M.-T., Vuong, T.-T., … Ho, R. (2019). Global evolution of research in artificial intelligence in health and medicine: A bibliometric study. Journal of Clinical Medicine, 8(360). https://doi.org/10.3390/jcm8030360
• 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, 81, 102883. https://doi.org/10.1016/j.annals.2020.102883
• 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 (pp. 359–370). Springer International Publishing. https://doi.org/10.1007/978-3-030-05940-8
• van Nunen, K., Li, J., Reniers, G., & Ponnet, K. (2018). Bibliometric analysis of safety culture research. Safety Science, 108, 248–258. https://doi.org/10.1016/j.ssci.2017.08.011
• van Raan, A. F. J. (2003). The use of bibliometric analysis in research performance assessment and monitoring of interdisciplinary scientific developments. TATuP - Zeitschrift Für Technikfolgenabschätzung in Theorie Und Praxis, 12(1), 20–29. https://doi.org/10.14512/tatup.12.1.20
• Virani, A., Wellstead, A., & Howlett, M. P. (2019). Where is the policy? A bibliometric review of the state of policy research on medical tourism. Global Health Research and Policy, 5, 1–16. https://doi.org/10.2139/ssrn.3445235
• Volchek, K., Liu, A., Song, H., & Buhalis, D. (2019). Forecasting tourist arrivals at attractions: Search engine empowered methodologies. Tourism Economics, 25(3), 425-447. https://doi.org/10.1177%2F1354816618811558
• Zenker, S., & Kock, F. (2020). The coronavirus pandemic-A critical discussion of a tourism research agenda. Tourism Management, 81, 104164. https://doi.org/10.1016/j.tourman.2020.104164
• 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. https://doi.org/10.1080/10941665.2019.1709876
• Zheng, W., Liao, Z., & Lin, Z. (2020). Navigating through the complex transport system: A heuristic approach for city tourism recommendation. Tourism Management, 81, 104162. https://doi.org/10.1016/j.tourman.2020.104162
• Zlatanov, S., & Popesku, J. (2019). Current applications of artificial intelligence in tourism and hospitality. In International Scientific Conference on Information Technology and Data Related Research (pp. 84-90), Sinteza, Romania.
• Zupic, I., & Čater, T. (2015). Bibliometric methods in management and organization. Organizational Research Methods, 18(3), 429–472. https://doi.org/10.1177/1094428114562629