Perceptions of artificial intelligence applications, trust, satisfaction, and smart tourism technology acceptance behavior: A systematic literature review
Vol. 135 No. 5C (2026), Original Article
Vol. 135 No. 5C (2026)

Perceptions of artificial intelligence applications, trust, satisfaction, and smart tourism technology acceptance behavior: A systematic literature review

Original Article
https://doi.org/10.26459/hueunijed.v135i5C.8481
Published 2026-08-17
Huynh Vu Chi
School of Hospitality and Tourism - Hue University, 22 Lam Hoang St., Hue City, Viet Nam
https://orcid.org/0009-0005-2891-7157
Ha Nam Khanh Giao
Vietnam Aviation Academy, 104 Nguyen Van Troi St., Phu Nhuan Ward, Ho Chi Minh City, Vietnam
Ho Quoc Dung
School of Engineering and Technology - Hue University, 01 Dien Bien Phu St., Hue, Vietnam
Nguyen Ngoc Huan
HCMC University of Foreign Languages - Information Technology, 828 Su Van Hanh St., Hoa Hung Ward, Ho Chi Minh City, Vietnam
PDF (Vietnamese)

Keywords

cảm nhận về ứng dụng AI
chấp nhận công nghệ du lịch thông minh
phương pháp PRISMA
tổng quan nghiên cứu có hệ thống (SLR)
trí tuệ nhân tạo (AI) perception of AI application
smart tourism technology acceptance
PRISMA method
systematic literature review (SLR)
artificial Intelligence (AI)

Abstract

This study aims to systematize the concepts and research trends concerning perceptions of artificial intelligence (AI) applications and their influence on the acceptance behavior of AI-based smart tourism technologies (STTs), thereby proposing a conceptual research framework grounded in the Stimulus-Organism-Response (SOR) model. A systematic literature review combined with bibliometric analysis is conducted on publications indexed in the Scopus database from 2018 to March 2026, following the PRISMA protocol. By integrating the Technology Acceptance Model (TAM), Service-Dominant Logic (SDL), and the SOR framework, perceptions of AI applications are conceptualized as a second-order multidimensional construct comprising six dimensions: intelligence, usefulness, ease of use, interactivity, personalization, and AI ethics. The study elucidates the mediating roles of trust and satisfaction in the mechanism through which perceptions of AI applications influence tourists’ acceptance behavior toward STTs. The findings contribute to the theoretical understanding of AI acceptance in smart tourism and provide a robust foundation for future empirical research in this domain.

https://doi.org/10.26459/hueunijed.v135i5C.8481
PDF (Vietnamese)

References

  1. Alam, M. N., Turi, J. A., Bhuiyan, A. B., Kharusi, S. A., Oyenuga, M., Zulkifli, N., & Iqbal, J. (2024). Factors influencing intention for reusing virtual reality (VR) at theme parks: the mediating role of visitors satisfaction. Cogent Social Sciences, 10(1)., 2298898 https://doi.org/10.1080/23311886.2023.2298898
  2. Ashrafi, D. M., & Easmin, R. (2023). The Role of Innovation Resistance and Technology Readiness in the Adoption of QR Code Payments Among Digital Natives: A Serial Moderated Mediation Model. International Journal of Business Science and Applied Management, 18(1), 19-45. http://dx.doi.org/10.69864/ijbsam.18-1.169
  3. Berakon, I., Wibowo, M. G., Nurdany, A., & Aji, H. M. (2023). An expansion of the technology acceptance model applied to the halal tourism sector. Journal of Islamic Marketing, 14(1), 289-316. https://doi.org/10.1108/JIMA-03-2021-0064
  4. Buhalis, D., & Amaranggana, A. (2015). Smart tourism destinations enhancing tourism experience through personalisation of services. In I. Tussyadiah, & A. Inversini (Eds.), Information and communication technologies in tourism 2015 (pp. 377–389). Springer. https://doi.org/10.1007/978-3-319-14343-9_28
  5. 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
  6. Camilleri, M. A., & Filieri, R. (2023). Customer satisfaction and loyalty with online consumer reviews: Factors affecting revisit intentions. International Journal of Hospitality Management, 114, 103575. https://doi.org/10.1016/j.ijhm.2023.103575
  7. Carranza, R., Díaz, E., Sánchez-Camacho, C., & Martín-Consuegra, D. (2021). e-Banking adoption: an opportunity for customer value co-creation. Frontiers in psychology, 11. https://doi.org/10.3389/fpsyg.2020.621248
  8. Chang, J. Y. S., Cheah, J. H., Lim, X. J., & Morrison, A. M. (2023). One pie, many recipes: The role of artificial intelligence chatbots in influencing Malaysian solo traveler purchase intentions. Tourism Management Perspectives, 49, 101191. https://doi.org/10.1016/j.tmp.2023.101191
  9. Chen, L., Wu, X., Liu, Y., Hu, Z., Chen, X., & Zhang, Z. (2025). Integrating TAM–ISSM–SOR framework to explore how virtual tourism experience influences tourists’ travel intention. Scientific Reports, 15(1), 38826. https://doi.org/10.1038/s41598-025-24793-z
  10. Chuang, C. M. (2023). The conceptualization of smart tourism service platforms on tourist value co-creation behaviours: An integrative perspective of smart tourism services. Humanities and Social Sciences Communications, 10(1), 367. https://doi.org/10.1057/s41599-023-01867-9
  11. Citak, J., Owoc, M. L., & Weichbroth, P. (2021). A note on the applications of artificial intelligence in the hospitality industry: preliminary results of a survey. Procedia Computer Science, 192, 4552-4559. https://doi.org/10.1016/j.procs.2021.09.233
  12. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340. https://doi.org/10.2307/249008
  13. Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133, 285-296. https://doi.org/10.1016/j.jbusres.2021.04.070
  14. Elisa Sousa, A., Pais, S., & Sofia Viana, A. (2024). Applications of artificial intelligence in tourism and hospitality: A systematic literature review. In International conference on management, tourism and technologies (pp. 291-302). Springer, Cham. https://doi.org/10.1007/978-3-031-44131-8_29
  15. Gajdošík, T., & Marciš, M. (2019, April). Artificial intelligence tools for smart tourism development. In Computer Science On-line Conference (pp. 392-402). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-19810-7_39
  16. 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
  17. Gogni, V., & Picasso, E. (2025). Towards sustainable agriculture: a staged innovation model. Quality & Quantity, 1-16. https://doi.org/10.1007/s11135-025-02130-w
  18. Gretzel, U., Sigala, M., Xiang, Z., & Koo, C. (2015). Smart tourism: foundations and developments. Electronic Markets, 25(3), 179-188. https://doi.org/10.1007/s12525-015-0196-8
  19. Gursoy, D., Chi, O. H., Lu, L., & Nunkoo, R. (2019). Consumers acceptance of artificially intelligent (AI) device use in service delivery. International journal of information management, 49, 157-169. https://doi.org/10.1016/j.ijinfomgt.2019.03.008
  20. Hameed, I., Akram, U., & Ashraf, A. (2025). Consumers' usage of mobile payment systems: an application of the innovation resistance and coping theory on the tourism sector. Kybernetes, 54(1), 391-413. https://doi.org/10.1108/K-03-2023-0495
  21. Hamid, M. A., Rahmat, N., & Azmadi, A. S. A. (2023). Stakeholders perception of smart tourism technology for tourism destination. International Journal of Academic Research in Business and Social Sciences, 13(4), 615-623. https://dx.doi.org/10.6007/IJARBSS/v13-i4/16624
  22. Hamzah, M. I., Ramli, F. A. A., & Shaw, N. (2023). The moderating influence of brand image on consumers' adoption of QR-code e-wallets. Journal of Retailing and Consumer Services, 73, 103326. https://doi.org/10.1016/j.jretconser.2023.103326
  23. Henriques, H. J. G., de Almeida, C. R., & Ramos, C. M. Q. (2024). The application of artificial intelligence in the tourism industry: a systematic literature review based on Prisma methodology. Journal of Tourism, Sustainability and Well-Being, 12(1), 65-86. https://doi.org/10.34623/hkqk-ht95
  24. Huang, M. H., & Rust, R. T. (2018). Artificial intelligence in service. Journal of Service Research, 21(2), 155-172. https://doi.org/10.1177/1094670517752459
  25. Jeong, M., & Shin, H. H. (2020). Tourists’ experiences with smart tourism technology at smart destinations and their behavior intentions. Journal of Travel Research, 59(8), 1464-1477. https://doi.org/10.1177/0047287519883034
  26. Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature machine intelligence, 1(9), 389-399. https://doi.org/10.1038/s42256-019-0088-2
  27. Kannan, R., Atchaya, M., & Bhavya, G. (2024, April). Wildlife Accident Prevention on Rail Track Using AI Technology. In 2024 Third International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE) (pp. 01-05). IEEE. https://doi.org/10.1109/ICDCECE60827.2024.10548920
  28. Koo, I., Zaman, U., Ha, H., & Nawaz, S. (2025). Assessing the interplay of trust dynamics, personalization, ethical AI practices, and tourist behavior in the adoption of AI-driven smart tourism technologies. Journal of Open Innovation: Technology, Market, and Complexity, 11(1), 100455. https://doi.org/10.1016/j.joitmc.2024.100455
  29. Lai, W. C., & Hung, W. H. (2018). A framework of cloud and AI based intelligent hotel. In: Proceedings of the International Conference on Electronic Business (ICEB). https://aisel.aisnet.org/iceb2018/99
  30. MacKenzie, S. B., Podsakoff, P. M., & Podsakoff, N. P. (2011). Construct measurement and validation procedures in MIS and behavioral research: Integrating new and existing Techniques1. MIS quarterly, 35(2), 293-A5. https://doi.org/10.2307/23044045
  31. Marasco, A., Buonincontri, P., Van Niekerk, M., Orlowski, M., & Okumus, F. (2018). Exploring the role of next-generation virtual technologies in destination marketing. Journal of Destination Marketing & Management, 9, 138-148. https://doi.org/10.1016/j.jdmm.2017.12.002
  32. Mehrabian, A., & Russell, J. A. (1974). An approach to environmental psychology. the MIT Press.
  33. Mohammad Nazri, N., Mokhsin, M., Abdul Razak, F. H., Nordin, N., & Mohd Som, M. H. (2022). User acceptance of immersive virtual reality (ivr) on information dissemination for nature tourism in Malaysia. Journal of Information and Knowledge Management (JIKM), 1, 28-37.
  34. Narayanan, S., Ramakrishnan, R., Durairaj, E., & Das, A. (2023). Artificial intelligence revolutionizing the field of medical education. Cureus, 15(11). https://doi.org/10.7759/cureus.49604
  35. Ngah, A. H., Alghizzawi, M., Zaheer, M., & RAHI, S. (2023). Investigating the role of innovativeness, technology acceptance model and theory of planned behavior towards adoption of QR mobile payment system. International Journal of Business Innovation and Research, 1(1).
  36. Osman, S., Jabaruddin, N., Zon, A. S., Jifridin, A. A., & Zolkepli, A. K. (2021). Factors influencing the use of E-wallet among millennium tourist. Journal of Information Technology Management, 13(3).
  37. Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., ... & Moher, D. (2021). The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. bmj, 372. https://doi.org/10.1136/bmj.n71
  38. Pangarso, A., Sisilia, K., & Peranginangin, Y. (2022). Circular economy business models in the micro, small, and medium enterprises: A review. Etikonomi, 21(2), 313-334. https://doi.org/10.15408/etk.v21i2.24052
  39. Samala, N., Katkam, B. S., Bellamkonda, R. S., & Rodriguez, R. V. (2022). Impact of AI and robotics in the tourism sector: a critical insight. Journal of Tourism Futures, 8(1), 73-87. https://doi.org/10.1108/JTF-07-2019-0065
  40. Sheikh, H., Prins, C., & Schrijvers, E. (2023). Artificial intelligence: definition and background. In Mission Ai (pp. 15-41). Springer, Cham. https://doi.org/10.1007/978-3-031-21448-6_2
  41. Shen, Y., Shamout, F. E., Oliver, J. R., Witowski, J., Kannan, K., Park, J., ... & Geras, K. J. (2021). Artificial intelligence system reduces false-positive findings in the interpretation of breast ultrasound exams. Nature Communications, 12(1), 5645. https://doi.org/10.1038/s41467-021-26023-2
  42. Sia, P. Y. H., Saidin, S. S., & Iskandar, Y. H. P. (2024). Smart mobile tourism app featuring augmented reality and big data analytics: an empirical analysis using UTAUT2 and PCT models. Journal of Science and Technology Policy Management, 15(6), 1363-1386. https://doi.org/10.1108/JSTPM-05-2022-0088
  43. Sobaih, A. E. E., Elshaer, I., Hasanein, A. M., & Abdelaziz, A. S. (2021). Responses to COVID-19: The role of performance in the relationship between small hospitality enterprises’ resilience and sustainable tourism development. International Journal of Hospitality Management, 94, 102824. https://doi.org/10.1016/j.ijhm.2020.102824
  44. Tornatzky, L. G., Fleischer, M., & Chakrabarti, A. K. (1990). The Processes of Technological Innovation, Lexington, MA: Lexington Books.
  45. 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
  46. Tussyadiah, I. P., & Park, S. (2017, December). Consumer evaluation of hotel service robots. In Information and Communication Technologies in Tourism 2018: Proceedings of the International Conference in Jönköping, Sweden, January 24-26, 2018 (pp. 308-320). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-72923-7_24
  47. Varelas, S., Georgitseas, P., Nechita, F., & Sahinidis, A. (2019). Strategic innovations in tourism enterprises through blockchain technology. In Strategic Innovative Marketing and Tourism: 7th ICSIMAT, Athenian Riviera, Greece, 2018 (pp. 885-891). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-12453-3_102
  48. Vargo, S. L., & Lusch, R. F. (2008). Service-dominant logic: continuing the evolution. Journal of the Academy of marketing Science, 36(1), 1-10. https://doi.org/10.1007/s11747-007-0069-6
  49. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view1. MIS quarterly, 27(3), 425-478. https://doi.org/10.2307/30036540
  50. Venkatesh, V., Thong, J. Y., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the Unified Theory of Acceptance and Use of Technology1. MIS Quarterly, 36(1), 157-178. https://doi.org/10.2307/41410412
  51. Wang, Y., Chung, T., & Den Yeoh, E. (2023). A hybrid SEM-ANN approach for intention to adopt metaverse using C-TAM-TPB and IDT in China. In Strategies and opportunities for technology in the metaverse world (pp. 263-293). IGI Global Scientific Publishing. https://doi.org/10.4018/978-1-6684-5732-0.ch015
  52. Yadav, J. K., Verma, D. C., Jangirala, S., & Srivastava, S. K. (2021). An IAD type framework for Blockchain enabled smart tourism ecosystem. The Journal of High Technology Management Research, 32(1), 100404. https://doi.org/10.1016/j.hitech.2021.100404
  53. 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. https://doi.org/10.1080/14616688.2020.1762118
  54. Zhang, A., Zhang, B., & Zhu, Y. (2025). Determinants of Technology Adoption in Tourism: Insights from a Systematic Literature Review. Sage Open, 15(4). https://doi.org/10.1177/21582440251388798
  55. Zhang, Y., Sotiriadis, M., & Shen, S. (2022). Investigating the impact of smart tourism technologies on tourists’ experiences. Sustainability, 14(5), 3048. https://doi.org/10.3390/su14053048
Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Copyright (c) 2026 Array