AI-powered live chatbots and smart tour guide apps in tourism: A literature review and future research directions
DOI:
https://doi.org/10.18778/0867-5856.2025.06Keywords:
AI technology, smart tour guide apps, AI-powered live chatbots, ChatGPT, inteligent featuresAbstract
This study explores the critical intersection in the tourism sector combining artificial intelligence (AI) technologies with conventional methods. This research outlines three main goals: assessing the use of AI chatbots in the tourism industry, reviewing existing literature on intelligent tour guide apps, and pinpointing areas for further research. It focuses on incorporating AI into the tourism industry, highlighting the effectiveness of tools such as ChatGPT. The systematic literature review examines the use of ChatGPT in pre-trip, en route, and post-trip scenarios, analyzing its effects on customer engagement. Using technology acceptance model (TAM) and unified theory of acceptance and use of technology (UTAUT) frameworks, the adoption of automated intelligent tour guides is explored. The research follows a systematic review methodology, adhering to PRISMA guidelines for methodological rigor and has uncovered several factors that impact the adoption of AI-based intelligent tour guides, offering valuable insights for academic scholars and industry experts.
Downloads
References
Altinay, L., & Kozak, M. (2021). Revisiting destination competitiveness through chaos theory: The butterfly competitiveness model. Journal of Hospitality and Tourism Management, 49, 331–340. https://doi.org/10.1016/j.jhtm.2021.10.004
Google Scholar
DOI: https://doi.org/10.1016/j.jhtm.2021.10.004
Aluri, A. (2017). Mobile augmented reality (MAR) game as a travel guide: Insights from Pokémon GO. Journal of Hospitality and Tourism Technology, 8(1), 55–72. https://doi.org/10.1108/JHTT-12-2016-0087
Google Scholar
DOI: https://doi.org/10.1108/JHTT-12-2016-0087
Booth, P., Chaperon, S.A., Kennell, J.S., & Morrison, A.M. (2020). Entrepreneurship in island contexts: A systematic review of the tourism and hospitality literature. International Journal of Hospitality Management, 85, Article 102438. https://doi.org/10.1016/j.ijhm.2019.102438
Google Scholar
DOI: https://doi.org/10.1016/j.ijhm.2019.102438
Buberwa, R.F., & Msusa, A. (2019). Developing a mobile GIS tour guide app for Dar-es-salaam City, Tanzania. International Journal of Scientific & Technology Research, 8(4), 237–242. https://www.ijstr.org/final-print/apr2019/Developing-A-Mobile-Gis-Tour-Guide-App-For-Dar-es-salaam-City-Tanzania.pdf
Google Scholar
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
Google Scholar
DOI: https://doi.org/10.1108/JOSM-12-2018-0398
Çalişkan, G., & Sevim, B. (2023). Use of service robots in hospitality: An observational study in terms of technology acceptance model. Tourism and Hospitality Research, 0(0). https://doi.org/10.1177/14673584231198438
Google Scholar
DOI: https://doi.org/10.1177/14673584231198438
Calisto, M. de L., & Sarkar, S. (2024). A systematic review of virtual reality in tourism and hospitality: The known and the paths to follow. International Journal of Hospitality Management, 116, Article 103623. https://doi.org/10.1016/j.ijhm.2023.103623
Google Scholar
DOI: https://doi.org/10.1016/j.ijhm.2023.103623
Cao, Y., Li, S., Liu, Y., Yan, Z., Dai, Y., Yu, P.S., & Sun, L. (2023). A comprehensive survey of AI-generated content (AIGC): A history of generative AI from GAN to ChatGPT. arXiv – Cornell University. https://doi.org/10.48550/arXiv.2303.04226
Google Scholar
Carvalho, I., & Ivanov, S. (2024). ChatGPT for tourism: Applications, benefits and risks. Tourism Review, 79(2), 290–303. https://doi.org/10.1108/TR-02-2023-0088
Google Scholar
DOI: https://doi.org/10.1108/TR-02-2023-0088
Chang, C.-W., Zhang, J.Z., & Neslin, S.A. (2018). The role of the physical store: Developing customer value through ‘fit product’ purchase. Cheung Kong Graduate School of Business. https://english.ckgsb.edu.cn/the-role-of-the-physical-store-developing-customer-value-through-fit-product-purchases/
Google Scholar
Chatterjee, J., & Dethlefs, N. (2023). This new conversational AI model can be your friend, philosopher, and guide … and even your worst enemy. Patterns, 4(1), Article 100676. https://doi.org/10.1016/j.patter.2022.100676
Google Scholar
DOI: https://doi.org/10.1016/j.patter.2022.100676
Chen, H., & Rahman, I. (2018). Cultural tourism: An analysis of engagement, cultural contact, memorable tourism experience and destination loyalty. Tourism Management Perspectives, 26, 153–163. https://doi.org/10.1016/j.tmp.2017.10.006
Google Scholar
DOI: https://doi.org/10.1016/j.tmp.2017.10.006
Chi, O.H., Denton, G., & Gursoy, D. (2020). Artificially intelligent device use in service delivery: A systematic review, synthesis, and research agenda. Journal of Hospitality Marketing & Management, 29(7), 757–786. https://doi.org/10.1080/19368623.2020.1721394
Google Scholar
DOI: https://doi.org/10.1080/19368623.2020.1721394
Chrastina, J. (2020). Title analysis of (systematic) scoping review studies: Chaos or consistency? Nursing & Health Sciences, 22(3), 557–562. https://doi.org/10.1111/nhs.12694
Google Scholar
DOI: https://doi.org/10.1111/nhs.12694
Chui, M., Roberts, R., & Yee, L. (2022, December 20). Generative AI is here: How tools like ChatGPT could change your business. QuantumBlack, AI by McKinsey. https://www.mckinsey.com/capabilities/quantumblack/our-insights/generative-ai-is-here-how-tools-like-chatgpt-could-change-your-business
Google Scholar
Ciechanowski, L., Przegalinska, A., Magnuski, M., & Gloor, P. (2019). In the shades of the uncanny valley: An experimental study of human–chatbot interaction. Future Generation Computer Systems, 92, 539–548. https://doi.org/10.1016/j.future.2018.01.055
Google Scholar
DOI: https://doi.org/10.1016/j.future.2018.01.055
Corne, A., Massot, V., & Merasli, S. (2023). The determinants of the adoption of blockchain technology in the tourism sector and metaverse perspectives. Information Technology & Tourism, 25(4), 605–633. https://doi.org/10.1007/s40558-023-00263-y
Google Scholar
DOI: https://doi.org/10.1007/s40558-023-00263-y
Curran, K., & Smith, K. (2006). A location-based mobile tourist guide. Tourism and Hospitality Research, 6(2), 180–187. https://doi.org/10.1057/palgrave.thr.6040055
Google Scholar
DOI: https://doi.org/10.1057/palgrave.thr.6040055
Dale, R. (2016). The return of the chatbots. Natural Language Engineering, 22(5), 811–817. https://doi.org/10.1017/S1351324916000243
Google Scholar
DOI: https://doi.org/10.1017/S1351324916000243
Du, H., Li, Z., Niyato, D., Kang, J., Xiong, Z., Shen, X.(S.)., & Kim, D.I. (2023). Enabling AI-generated content (AIGC) services in wireless edge networks. arXiv – Cornell University. https://doi.org/10.48550/arXiv.2301.03220
Google Scholar
DOI: https://doi.org/10.1109/MWC.004.2300015
Duan, J., Xie, C., & Morrison, A.M. (2022). Tourism crises and impacts on destinations: A systematic review of the tourism and hospitality literature. Journal of Hospitality & Tourism Research, 46(4), 667–695. https://doi.org/10.1177/1096348021994194
Google Scholar
DOI: https://doi.org/10.1177/1096348021994194
Eisingerich, A.B., Marchand, A., Fritze, M.P., & Dong, L. (2019). Hook vs. hope: How to enhance customer engagement through gamification. International Journal of Research in Marketing, 36(2), 200–215. https://doi.org/10.1016/j.ijresmar.2019.02.003
Google Scholar
DOI: https://doi.org/10.1016/j.ijresmar.2019.02.003
Elmohandes, N., & Marghany, M. (2024). Effective or ineffective? Using ChatGPT for staffing in the hospitality industry. European Journal of Tourism Research, 36, Article 3617. https://doi.org/10.54055/ejtr.v36i.3286
Google Scholar
DOI: https://doi.org/10.54055/ejtr.v36i.3286
Fadhil, A., & Schiavo, G. (2019). Designing for health chatbots. arXiv – Cornell University. https://doi.org/10.48550/arXiv.1902.09022
Google Scholar
Feine, J., Morana, S., & Gnewuch, U. (2019). Measuring service encounter satisfaction with customer service chatbots using sentiment analysis. In Proceedings of the 14th International Conference on Wirtschaftsinformatik (WI2019), Siegen, Germany, February 24–27, 2019 (pp. 1115–1129). AIS. eLibrary. https://aisel.aisnet.org/wi2019/track10/papers/2/
Google Scholar
Fryer, L.K., Ainley, M., Thompson, A., Gibson, A., & Sherlock, Z. (2017). Stimulating and sustaining interest in a language course: An experimental comparison of chatbot and human task partners. Computers in Human Behavior, 75, 461–468. https://doi.org/10.1016/j.chb.2017.05.045
Google Scholar
DOI: https://doi.org/10.1016/j.chb.2017.05.045
Gao, J., & Pan, Y. (2022). Evaluating influencing factors of tourists’ experiences with smart tour guide system: A mixed method research. Sustainability, 14(23), Article 16320. https://doi.org/10.3390/su142316320
Google Scholar
DOI: https://doi.org/10.3390/su142316320
Go, E., & Sundar, S.S. (2019). Humanizing chatbots: The effects of visual, identity and conversational cues on humanness perceptions. Computers in Human Behavior, 97, 304–316. https://doi.org/10.1016/j.chb.2019.01.020
Google Scholar
DOI: https://doi.org/10.1016/j.chb.2019.01.020
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, 75(4), 625–636. https://doi.org/10.1108/TR-02-2019-0062
Google Scholar
DOI: https://doi.org/10.1108/TR-02-2019-0062
Guo, Q., Zhu, D., Lin, M.-T.(B.)., Li, F.(S.)., Kim, P.B., Du, D., & Shu, Y. (2023). Hospitality employees’ technology adoption at the workplace: Evidence from a meta-analysis. International Journal of Contemporary Hospitality Management, 35(7), 2437–2464. https://doi.org/10.1108/IJCHM-06-2022-0701
Google Scholar
DOI: https://doi.org/10.1108/IJCHM-06-2022-0701
Hill, J., Ford, W.R., & Farreras, I.G. (2015). Real conversations with artificial intelligence: A comparison between human–human online conversations and human–chatbot conversations. Computers in Human Behavior, 49, 245–250. https://doi.org/10.1016/j.chb.2015.02.026
Google Scholar
DOI: https://doi.org/10.1016/j.chb.2015.02.026
Hinson, R.E., Osabutey, E.L.C., & Kosiba, J.P. (2020). Exploring the dialogic communication potential of selected African destinations’ place websites. Journal of Business Research, 116, 690–698. https://doi.org/10.1016/j.jbusres.2018.03.033
Google Scholar
DOI: https://doi.org/10.1016/j.jbusres.2018.03.033
Hollebeek, L.D., & Belk, R. (2021). Consumers’ technology-facilitated brand engagement and wellbeing: Positivist TAM/PERMA- vs. Consumer Culture Theory perspectives. International Journal of Research in Marketing, 38(2), 387–401. https://doi.org/10.1016/j.ijresmar.2021.03.001
Google Scholar
DOI: https://doi.org/10.1016/j.ijresmar.2021.03.001
Hollebeek, L.D., Srivastava, R.K., & Chen, T. (2019). S-D logic–informed customer engagement: Integrative framework, revised fundamental propositions, and application to CRM. Journal of the Academy of Marketing Science, 47, 161–185. https://doi.org/10.1007/s11747-016-0494-5
Google Scholar
DOI: https://doi.org/10.1007/s11747-016-0494-5
Huang, S.(S.)., Weiler, B., & Assaker, G. (2015). Effects of interpretive guiding outcomes on tourist satisfaction and behavioral intention. Journal of Travel Research, 54(3), 344–358. https://doi.org/10.1177/0047287513517426
Google Scholar
DOI: https://doi.org/10.1177/0047287513517426
Huang, Y.-C., Chang, L.L., Yu, C.-P., & Chen, J. (2019). Examining an extended technology acceptance model with experience construct on hotel consumers’ adoption of mobile applications. Journal of Hospitality Marketing & Management, 28(8), 957–980. https://doi.org/10.1080/19368623.2019.1580172
Google Scholar
DOI: https://doi.org/10.1080/19368623.2019.1580172
Iskender, A. (2023). Holy or unholy? Interview with open AI’s ChatGPT. European Journal of Tourism Research, 34, Article 3414. https://doi.org/10.54055/ejtr.v34i.3169
Google Scholar
DOI: https://doi.org/10.54055/ejtr.v34i.3169
Ivanov, S., & Webster, C. (Eds.). (2019). Robots, artificial intelligence, and service automation in travel, tourism and hospitality. Emerald Publishing. https://doi.org/10.1108/978-1-78756-687-320191014
Google Scholar
DOI: https://doi.org/10.1108/9781787566873
Jiang, H., Cheng, Y., Yang, J., & Gao, S. (2022). AI-powered chatbot communication with customers: Dialogic interactions, satisfaction, engagement, and customer behavior. Computers in Human Behavior, 134, Article 107329. https://doi.org/10.1016/j.chb.2022.107329
Google Scholar
DOI: https://doi.org/10.1016/j.chb.2022.107329
Kim, J.(J.)., & Fesenmaier, D.R. (2017). Sharing tourism experiences: The posttrip experience. Journal of Travel Research, 56(1), 28–40. https://doi.org/10.1177/0047287515620491
Google Scholar
DOI: https://doi.org/10.1177/0047287515620491
Kim, J.Y., Chung, N., & Ahn, K.M. (2019). The impact of mobile tour information services on destination travel intention. Information Development, 35(1), 107–120. https://doi.org/10.1177/0266666917730437
Google Scholar
DOI: https://doi.org/10.1177/0266666917730437
Kounavis, C.D., Kasimati, A.E., & Zamani, E.D. (2012). Enhancing the tourism experience through mobile augmented reality: Challenges and prospects. International Journal of Engineering Business Management, 4. https://doi.org/10.5772/51644
Google Scholar
DOI: https://doi.org/10.5772/51644
Krajňák, T. (2021). The effects of terrorism on tourism demand: A systematic review. Tourism Economics, 27(8), 1736–1758. https://doi.org/10.1177/1354816620938900
Google Scholar
DOI: https://doi.org/10.1177/1354816620938900
Lai, I.K.W. (2015). Traveler acceptance of an app-based mobile tour guide. Journal of Hospitality & Tourism Research, 39(3), 401–432. https://doi.org/10.1177/1096348013491596
Google Scholar
DOI: https://doi.org/10.1177/1096348013491596
Lai, W.-C., & Hung, W.-H. (2017). Constructing the smart hotel architecture: A case study in Taiwan. In ICEB 2017 Proceedings (Dubai, UAE). AIS. eLibrary. https://aisel.aisnet.org/iceb2017/12
Google Scholar
Lee, S., & Choi, J. (2017). Enhancing user experience with conversational agent for movie recommendation: Effects of self-disclosure and reciprocity. International Journal of Human-Computer Studies, 103, 95–105. https://doi.org/10.1016/j.ijhcs.2017.02.005
Google Scholar
DOI: https://doi.org/10.1016/j.ijhcs.2017.02.005
Leung, R. (2019). Smart hospitality: Taiwan hotel stakeholder perspectives. Tourism Review, 74(1), 50–62. https://doi.org/10.1108/TR-09-2017-0149
Google Scholar
DOI: https://doi.org/10.1108/TR-09-2017-0149
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. https://doi.org/10.1016/j.tourman.2019.02.006
Google Scholar
DOI: https://doi.org/10.1016/j.tourman.2019.02.006
Li, M., Yin, D., Qiu, H., & Bai, B. (2021). A systematic review of AI technology-based service encounters: Implications for hospitality and tourism operations. International Journal of Hospitality Management, 95, Article 102930. https://doi.org/10.1016/j.ijhm.2021.102930
Google Scholar
DOI: https://doi.org/10.1016/j.ijhm.2021.102930
Li, S., & Xiao, Q. (2020). Classification and improvement strategy for design features of mobile tourist guide application: A Kano-IPA approach. Mobile Information Systems, (1), Article 8816130. https://doi.org/10.1155/2020/8816130
Google Scholar
DOI: https://doi.org/10.1155/2020/8816130
Lin, M.-T.(B.)., Zhu, D., Liu, C., & Kim, P.B. (2022). A systematic review of empirical studies of pro-environmental behavior in hospitality and tourism contexts. International Journal of Contemporary Hospitality Management, 34(11), 3982–4006. https://doi.org/10.1108/IJCHM-12-2021-1478
Google Scholar
DOI: https://doi.org/10.1108/IJCHM-12-2021-1478
Liu, D., Tong, C., Liu, Y., Yuan, Y., & Ju, C. (2016). Examining the adoption and continuous usage of context-aware services: An empirical study on the use of an intelligent tourist guide. Information Development, 32(3), 608–621. https://doi.org/10.1177/0266666914563358
Google Scholar
DOI: https://doi.org/10.1177/0266666914563358
Liu, Y., Du, H., Niyato, D., Kang, J., Xiong, Z., Miao, C., Shen, X.(S.)., & Jamalipour, A. (2023). Blockchain-empowered lifecycle management for AI-generated content (AIGC) products in edge networks. arXiv – Cornell University. https://doi.org/10.48550/arXiv.2303.02836
Google Scholar
DOI: https://doi.org/10.36227/techrxiv.22178126
McKercher, B., & Darcy, S. (2018). Re-conceptualizing barriers to travel by people with disabilities. Tourism Management Perspectives, 26, 59–66. https://doi.org/10.1016/j.tmp.2018.01.003
Google Scholar
DOI: https://doi.org/10.1016/j.tmp.2018.01.003
Mondal, S., Das, S., & Vrana, V.G. (2023). How to bell the cat? A theoretical review of generative artificial intelligence towards digital disruption in all walks of life. Technologies, 11(2), Article 44. https://doi.org/10.3390/technologies11020044
Google Scholar
DOI: https://doi.org/10.3390/technologies11020044
Morosan, C. (2012). Theoretical and empirical considerations of guests’ perceptions of biometric systems in hotels: Extending the technology acceptance model. Journal of Hospitality & Tourism Research, 36(1), 52–84. https://doi.org/10.1177/1096348010380601
Google Scholar
DOI: https://doi.org/10.1177/1096348010380601
Nautiyal, R., Albrecht, J.N., & Nautiyal, A. (2023). ChatGPT and tourism academia. Annals of Tourism Research, 99, Article 103544. https://doi.org/10.1016/j.annals.2023.103544
Google Scholar
DOI: https://doi.org/10.1016/j.annals.2023.103544
Neuhofer, B., Buhalis, D., & Ladkin, A. (2015). Smart technologies for personalized experiences: A case study in the hospitality domain. Electronic Markets, 25(3), 243–254. https://doi.org/10.1007/s12525-015-0182-1
Google Scholar
DOI: https://doi.org/10.1007/s12525-015-0182-1
Niu, H. (2023). The effect of intelligent tour guide system based on attraction positioning and recommendation to improve the experience of tourists visiting scenic spots. Intelligent Systems with Applications, 19, Article 200263. https://doi.org/10.1016/j.iswa.2023.200263
Google Scholar
DOI: https://doi.org/10.1016/j.iswa.2023.200263
Peres, R., Correia, A., & Moital, M. (2011). The indicators of intention to adopt mobile electronic tourist guides. Journal of Hospitality and Tourism Technology, 2(2), 120–138. https://doi.org/10.1108/17579881111154236
Google Scholar
DOI: https://doi.org/10.1108/17579881111154236
Pillai, R., & Sivathanu, B. (2020). Adoption of AI-based chatbots for hospitality and tourism. International Journal of Contemporary Hospitality Management, 32(10), 3199–3226. https://doi.org/10.1108/IJCHM-04-2020-0259
Google Scholar
DOI: https://doi.org/10.1108/IJCHM-04-2020-0259
Prasetya, S.A., Erwin, A., & Galinium, M. (2018). Implementing Indonesian language chatbot for ecommerce site using artificial intelligence markup language (AIML). Prosiding Seminar Nasional Pakar 2018, 1, 313–322. https://doi.org/10.25105/pakar.v0i0.2652
Google Scholar
DOI: https://doi.org/10.25105/pakar.v0i0.2652
Rashkin, H., Smith, E.M., Li, M., & Boureau, Y.-L. (2019). Towards empathetic open-domain conversation models: A new benchmark and dataset. In A. Korhonen, D. Traum & L. Màrquez (Eds.), Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (pp. 5370–5381). Association for Computational Linguistics. https://aclanthology.org/P19-1534.pdf
Google Scholar
DOI: https://doi.org/10.18653/v1/P19-1534
Reinartz, W., Wiegand, N., & Imschloss, M. (2019). The impact of digital transformation on the retailing value chain. International Journal of Research in Marketing, 36(3), 350–366. https://doi.org/10.1016/j.ijresmar.2018.12.002
Google Scholar
DOI: https://doi.org/10.1016/j.ijresmar.2018.12.002
Ruiz-Alba, J.L., & Martín-Penã, M.-L. (2020). Guest editorial. Journal of Business & Industrial Marketing, 35(3), 385–389. https://doi.org/10.1108/JBIM-03-2020-419
Google Scholar
DOI: https://doi.org/10.1108/JBIM-03-2020-419
Rutz, O., Aravindakshan, A., & Rubel, O. (2019). Measuring and forecasting mobile game app engagement. International Journal of Research in Marketing, 36(2), 185–199. https://doi.org/10.1016/j.ijresmar.2019.01.002
Google Scholar
DOI: https://doi.org/10.1016/j.ijresmar.2019.01.002
Santini, F. de O., Ladeira, W., Jr., & Sampaio, C.H. (2018). The role of satisfaction in fashion marketing: A meta-analysis. Journal of Global Fashion Marketing, 9(4), 305–321. https://doi.org/10.1080/20932685.2018.1503556
Google Scholar
DOI: https://doi.org/10.1080/20932685.2018.1503556
Schamari, J., & Schaefers, T. (2015). Leaving the home turf: How brands can use webcare on consumer-generated platforms to increase positive consumer engagement. Journal of Interactive Marketing, 30(1), 20–33. https://doi.org/10.1016/j.intmar.2014.12.001
Google Scholar
DOI: https://doi.org/10.1016/j.intmar.2014.12.001
Schuetzler, R.M., Grimes, G.M., & Giboney, J.S. (2019). The effect of conversational agent skill on user behavior during deception. Computers in Human Behavior, 97, 250–259. https://doi.org/10.1016/j.chb.2019.03.033
Google Scholar
DOI: https://doi.org/10.1016/j.chb.2019.03.033
Shin, H.H., Jeong, M., So, K.K.F., & DiPietro, R. (2022). Consumers’ experience with hospitality and tourism technologies: Measurement development and validation. International Journal of Hospitality Management, 106, Article 103297. https://doi.org/10.1016/j.ijhm.2022.103297
Google Scholar
DOI: https://doi.org/10.1016/j.ijhm.2022.103297
Shumanov, M., & Johnson, L. (2021). Making conversations with chatbots more personalized. Computers in Human Behavior, 117, Article 106627. https://doi.org/10.1016/j.chb.2020.106627
Google Scholar
DOI: https://doi.org/10.1016/j.chb.2020.106627
Sia, P.Y.-H., Saidin, S.S., & Iskandar, Y.H.P. (2022). Systematic review of mobile travel apps and their smart features and challenges. Journal of Hospitality and Tourism Insights, 6(5), 2115–2138. https://doi.org/10.1108/JHTI-02-2022-0087
Google Scholar
DOI: https://doi.org/10.1108/JHTI-02-2022-0087
Stokel-Walker, C., & Van Noorden, R. (2023). What ChatGPT and generative AI mean for science. Nature, 614(7947), 214–216. https://doi.org/10.1038/d41586-023-00340-6
Google Scholar
DOI: https://doi.org/10.1038/d41586-023-00340-6
Sun, W., Tang, S., & Liu, F. (2021). Examining perceived and projected destination image: A social media content analysis. Sustainability, 13(6), Article 3354. https://doi.org/10.3390/su13063354
Google Scholar
DOI: https://doi.org/10.3390/su13063354
Sundar, S.S., Go, E., Kim, H.-S., & Zhang, B. (2015). Communicating art, virtually! Psychological effects of technological affordances in a virtual museum. International Journal of Human–Computer Interaction, 31(6), 385–401. https://doi.org/10.1080/10447318.2015.1033912
Google Scholar
DOI: https://doi.org/10.1080/10447318.2015.1033912
Susnjak, T. (2022). ChatGPT: The end of online exam integrity? arXiv – Cornell University. https://doi.org/10.48550/arXiv.2212.09292
Google Scholar
Taecharungroj, V. (2023). “What can ChatGPT do?”: Analyzing early reactions to the innovative AI chatbot on Twitter. Big Data and Cognitive Computing, 7(1), Article 35. https://doi.org/10.3390/bdcc7010035
Google Scholar
DOI: https://doi.org/10.3390/bdcc7010035
Tarantino, E., De Falco, I., & Scafuri, U. (2019). A mobile personalized tourist guide and its user evaluation. Information Technology & Tourism, 21(3), 413–455. https://doi.org/10.1007/s40558-019-00150-5
Google Scholar
DOI: https://doi.org/10.1007/s40558-019-00150-5
Tsai, W.-H.S., Liu, Y., & Chuan, C.-H. (2021). How chatbots’ social presence communication enhances consumer engagement: The mediating role of parasocial interaction and dialogue. Journal of Research in Interactive Marketing, 15(3), 460–482. https://doi.org/10.1108/JRIM-12-2019-0200
Google Scholar
DOI: https://doi.org/10.1108/JRIM-12-2019-0200
Tussyadiah, I., & Miller, G. (2019). Perceived impacts of artificial intelligence and responses to positive behaviour change intervention. In J. Pesonen & J. Neidhardt (Eds.), Information and Communication Technologies in Tourism 2019: Proceedings of the International Conference in Nicosia, Cyprus, January 30–February 1, 2019 (pp. 359–370). Springer. https://doi.org/10.1007/978-3-030-05940-8_28
Google Scholar
DOI: https://doi.org/10.1007/978-3-030-05940-8_28
Vendemia, M.A. (2017). When do consumers buy the company? Perceptions of interactivity in company-consumer interactions on social networking sites. Computers in Human Behavior, 71, 99–109. https://doi.org/10.1016/j.chb.2017.01.046
Google Scholar
DOI: https://doi.org/10.1016/j.chb.2017.01.046
Walters, G., Jiang, Y., & Li, S. (2025). Physiological measurements in hospitality and tourism research: A systematic review and new theoretical directions. Journal of Hospitality & Tourism Research, 49(3), 417–432. https://doi.org/10.1177/10963480231199990
Google Scholar
DOI: https://doi.org/10.1177/10963480231199990
Wang, D., & Cheung, C. (2024). Decent work in tourism and hospitality – a systematic literature review, classification, and research recommendations. International Journal of Contemporary Hospitality Management, 36(7), 2194–2213. https://doi.org/10.1108/IJCHM-10-2022-1263
Google Scholar
DOI: https://doi.org/10.1108/IJCHM-10-2022-1263
Wang, R., Yang, F., Zheng, S., & Sundar, S.S. (2016). Why do we pin? New gratifications explain unique activities in Pinterest. Social Media + Society, 2(3). https://doi.org/10.1177/2056305116662173
Google Scholar
DOI: https://doi.org/10.1177/2056305116662173
Wong, I.A., Lian, Q.L., & Sun, D. (2023). Autonomous travel decision-making: An early glimpse into ChatGPT and generative AI. Journal of Hospitality and Tourism Management, 56, 253–263. https://doi.org/10.1016/j.jhtm.2023.06.022
Google Scholar
DOI: https://doi.org/10.1016/j.jhtm.2023.06.022
Wong, I.A., Lin, S.K., Lin, Z.(CJ)., & Xiong, X. (2022). Welcome to stay-at-home travel and virtual attention restoration. Journal of Hospitality and Tourism Management, 51, 207–217. https://doi.org/10.1016/j.jhtm.2022.03.016
Google Scholar
DOI: https://doi.org/10.1016/j.jhtm.2022.03.016
Wu, H.-C., & Cheng, C.-C. (2018). Relationships between technology attachment, experiential relationship quality, experiential risk and experiential sharing intentions in a smart hotel. Journal of Hospitality and Tourism Management, 37, 42–58. https://doi.org/10.1016/j.jhtm.2018.09.003
Google Scholar
DOI: https://doi.org/10.1016/j.jhtm.2018.09.003
Xiang, Z., Magnini, V.P., & Fesenmaier, D.R. (2015). Information technology and consumer behavior in travel and tourism: Insights from travel planning using the internet. Journal of Retailing and Consumer Services, 22, 244–249. https://doi.org/10.1016/j.jretconser.2014.08.005
Google Scholar
DOI: https://doi.org/10.1016/j.jretconser.2014.08.005
Xu, W., & Zhang, X. (2021). Online expression as well-be(com)ing: A study of travel blogs on Nepal by Chinese female tourists. Tourism Management, 83, Article 104224. https://doi.org/10.1016/j.tourman.2020.104224
Google Scholar
DOI: https://doi.org/10.1016/j.tourman.2020.104224
Yao, Y., Jia, G., & Hou, Y. (2021). Impulsive travel intention induced by sharing conspicuous travel experience on social media: A moderated mediation analysis. Journal of Hospitality and Tourism Management, 49, 431–438. https://doi.org/10.1016/j.jhtm.2021.10.012
Google Scholar
DOI: https://doi.org/10.1016/j.jhtm.2021.10.012
Zhou, G., Chu, G., Li, L., & Meng, L. (2020). The effect of artificial intelligence on China’s labor market. China Economic Journal, 13(1), 24–41. https://doi.org/10.1080/17538963.2019.1681201
Google Scholar
DOI: https://doi.org/10.1080/17538963.2019.1681201
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Funding data
-
Fundação para a Ciência e a Tecnologia
Grant numbers UID/04011: CETRAD
