Exploring the Factors Influencing User Attitudes and AI Chatbot Use in the Tourism Sector: Evidence from Indonesia
DOI:
https://doi.org/10.20414/jed.v8i1.14881Keywords:
AI Chatbot, Tourism, Technology Acceptance Model, Attitude, Intention to UseAbstract
Purpose: This study investigates the influence of perceived ease of use, perceived usefulness, perceived trust, anthropomorphism, and personalization on user attitudes, and how these attitudes affect the intention to use AI chatbots in the tourism sector.
Method: A quantitative, descriptive-explanatory approach was employed through an online survey distributed to individuals who had prior experience using AI chatbots for travel purposes. A total of 278 valid responses were collected using purposive sampling. The research model was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS.
Result: The findings indicate that perceived ease of use, perceived usefulness, perceived trust, anthropomorphism, and personalization all positively and significantly influence user attitudes toward AI chatbots. Furthermore, attitude was found to exert a strong and significant impact on the intention to use AI chatbots for tourism, confirming its role as a key mediating variable. Among the antecedents, personalization emerged as the most influential factor in shaping user attitudes.
Practical Implications for Economic Growth and Development: This study offers valuable insights for AI developers and tourism stakeholders to design more user-centered, trustworthy, and personalized chatbot services. Enhancing the adoption of AI chatbots can improve the efficiency of tourism services, support digital transformation, increase tourist satisfaction, and contribute to sustainable economic growth in the tourism sector.
Originality/Value: This study extends the Technology Acceptance Model (TAM) framework by incorporating perceived trust, personalization, and anthropomorphism in the context of AI chatbot adoption from the tourists' perspective in Indonesia.
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Bakri, A. A., Hasanah, N., & Lasmiatun, K. (2024). Financial technology innovation and banking industry transformation: A literature study on financial markets. MULTIFINANCE Jurnal Ekonomi, Manajemen Dan Perbankan, 230–238. http://altinriset.com/journal/index.php/multifinance
Bhatt, V., Hiteshi Ajmera, A., & Nayak, K. (2021). An empirical study on analyzing a user’s intention towards using mobile wallets; Measuring the mediating effect of perceived attitude and perceived trust. Turkish Journal of Computer and Mathematics Education, 12(10).
Chang, H. (2023). The effect of AI chatbot-based tourism English instruction on intercultural communicative competence. STEM Journal, 24(2), 15–30. https://doi.org/10.16875/stem.2023.24.2.15
Chen, T., Guo, W., Gao, X., & Liang, Z. (2021). AI-based self-service technology in public service delivery: User experience and influencing factors. Government Information Quarterly, 38(4). https://doi.org/10.1016/j.giq.2020.101520
Choung, H., David, P., & Ross, A. (2023). Trust in AI and its role in the acceptance of AI technologies. International Journal of Human-Computer Interaction, 39(9), 1727–1739. https://doi.org/10.1080/10447318.2022.2050543
Ding, H., Guo, T., Hohwü-Christensen, A., Hu, X., Li, Z., & van Wijlick, L. (2025). AI and appification in the travel sector. ASI Sprint Report.
Fahlevi, R., & Sinambela, F. A. (2024). Faktor yang mempengaruhi niat penggunaan aplikasi dompet digital secara berkelanjutan oleh generasi Z. Jurnal Riset Manajemen, 11(1), 26–42.
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research.
George, R. (2025). Tourism and hospitality marketing principles. In Marketing Tourism and Hospitality (pp. 3–29). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-65983-6_1
Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24. https://doi.org/10.1108/EBR-11-2018-0203
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8
Hoang, Y. N., Chen, S. H., Chang, C. C., Lin, A. W., Nguyen, T. H., Hung, L. X., Hoang, T. V., Ho, D. K. N., Lin, W. L., Yao, C. Y., Handayani, D., & Chang, J. S. (2025). Trust predicts actual use of AI chatbot as a virtual nutrition assistant among dietetic students in Taiwan: A path analysis. Journal of Human Nutrition and Dietetics, 38(6). https://doi.org/10.1111/jhn.70156
Istiqomah, P., & Alfansi, L. (2023). Navigating style: Exploring the influence of perceived benefit and perceived ease of use on attitude towards use in AI-enhanced fashion e-commerce. Journal of Entrepreneurship and Business, 5(1), 1–14. https://doi.org/10.24123/jeb.v5i1.6070
Kang, S., Choi, Y., & Kim, B. (2024). Impact of motivation factors for using generative AI services on continuous use intention: Mediating trust and acceptance attitude. Social Sciences, 13(9). https://doi.org/10.3390/socsci13090475
Khotama, F. W., & Yulianton, H. (2025). Implementasi chatbot berbasis Dialogflow dengan metode Natural Language Processing untuk rekomendasi tempat wisata di Kabupaten Kulonprogo. Jurnal Riset Sistem Informasi Dan Teknik Informatika (JURASIK), 10, 367–378. https://tunasbangsa.ac.id/ejurnal/index.php/jurasik
Kim, J., Giroux, M., & Lee, J. C. (2021). When do you trust AI? The effect of number presentation detail on consumer trust and acceptance of AI recommendations. Psychology and Marketing, 38(7), 1140–1155. https://doi.org/10.1002/mar.21498
Kodors, S., Kanepe, G., Zeps, D., Zarembo, I., & Litavniece, L. (2024). Rapid development of chatbot for tourism promotion in Latgale. Vide. Tehnologija. Resursi - Environment, Technology, Resources, 2, 179–182. https://doi.org/10.17770/etr2024vol2.8060
Kronemann, B., Kizgin, H., Rana, N., & Dwivedi, Y. K. (2023). How AI encourages consumers to share their secrets? The role of anthropomorphism, personalization, and privacy concerns and avenues for future research. Spanish Journal of Marketing - ESIC, 27(1), 2–19. https://doi.org/10.1108/SJME-10-2022-0213
Liehner, L. G., Hick, A., Biermann, H., Brauner, P., & Ziefle, M. (2023). Perceptions, attitudes, and trust toward artificial intelligence — An assessment of public opinion. Artificial Intelligence and Social Computing, 72. https://doi.org/10.54941/ahfe1003271
Liu, C. Y., Tao, Y., Hou, W., Niu, H., Liu, X., Xie, T., & Li, Y. (2023). Association between attachment and hoarding behavior: Mediation of anthropomorphism and moderation of hoarding beliefs among Chinese adolescents. PsyCh Journal, 12(1), 128–136. https://doi.org/10.1002/pchj.610
Liu, Y. W., Hu, B., Li, Z., & Lai, Y. L. (2022). Effects of personalization and source expertise on users' health beliefs and usage intention toward health chatbots: Evidence from an online experiment. Digital Health, 8. https://doi.org/10.1177/20552076221129718
Lopes, J. M., Silva, L. F., & Massano-Cardoso, I. (2024). AI meets the shopper: Psychosocial factors in ease of use and their effect on e-commerce purchase intention. Behavioral Sciences, 14(7). https://doi.org/10.3390/bs14070616
Ma, J., Wang, P., Li, B., Wang, T., Pang, X. S., & Wang, D. (2025). Exploring user adoption of ChatGPT: A technology acceptance model perspective. International Journal of Human-Computer Interaction, 41(2), 1431–1445. https://doi.org/10.1080/10447318.2024.2314358
Mavrych, V., Yaqinuddin, A., & Bolgova, O. (2025). Claude, ChatGPT, Copilot, and Gemini performance versus students in different topics of neuroscience. Advances in Physiology Education, 430–437.
Na, S., Heo, S., Choi, W., Kim, C., & Whang, S. W. (2023). Artificial intelligence (AI)-based technology adoption in the construction industry: A cross-national perspective using the technology acceptance model. Buildings, 13(10). https://doi.org/10.3390/buildings13102518
Naufal, A. B., Sudianto, S., & Al Fachri, M. A. (2023). Implementation of chatbot system on tourism objects in Banyumas Regency with AIML and Chatterbot. Jurnal Teknik Elektro Dan Komputasi (ELKOM), 5(2), 191–195. https://doi.org/10.32528/elkom.v5i2.18615
Ndunagu, J. N., Ezeanya, C. U., Onuorah, B. O., Onyeakazi, J. C., & Ukwandu, E. (2025). A chatbot student support system in open and distance learning institutions. Computers, 14(3). https://doi.org/10.3390/computers14030096
Nguyen, H. T., Tran, T. T., Nham, P. T., Nguyen, N. U. B., & Le, A. D. (2023). AI chatbot for tourist recommendations: A case study in Vietnam. Applied Computer Systems, 28(2), 232–244. https://doi.org/10.2478/acss-2023-0023
Nja, C. O., Idiege, K. J., Uwe, U. E., Meremikwu, A. N., Ekon, E. E., Erim, C. M., Ukah, J. U., Eyo, E. O., Anari, M. I., & Cornelius-Ukpepi, B. U. (2023). Adoption of artificial intelligence in science teaching: From the vantage point of the African science teachers. Smart Learning Environments, 10(1). https://doi.org/10.1186/s40561-023-00261-x
Okamoto, K. (2025). A battle of the chatbots: An exploratory comparison of ChatGPT, Gemini, Copilot, Claude, Perplexity, and HuggingChat. Practical Academic Librarianship: The International Journal of the SLA Academic Division, 15(1). http://journals.tdl.org/pal
Pahlevi, R., Zulpahmi, Z., Al-Azizah, U. S., & Hasibuan, A. A. (2023). Adoption of fintech services for sharia bank users in Indonesia: An extended TAM approach. Equilibrium: Jurnal Ekonomi Syariah, 11(1), 27. https://doi.org/10.21043/equilibrium.v11i1.19641
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
Pitanatri, U. M., & Wijaya, N. (2021). Pengaruh perceived ease of use, perceived of usefulness dan financial risk terhadap intention to use pada booking.com di Kabupaten Badung. JUMPA, 8(1).
Potdevin, D., Clavel, C., & Sabouret, N. (2021). A virtual tourist counselor expressing intimacy behaviors: A new perspective to create emotion in visitors and offer them a better user experience. International Journal of Human-Computer Studies, 150. https://doi.org/10.1016/j.ijhcs.2021.102612
Purwianti, L., Nurjanah, L., Katherine, K., & Chen, R. (2024). The impact of TAM, social influence, and information quality on purchase intention in e-commerce. Jurnal Organisasi Dan Manajemen, 20(2), 187–206. https://doi.org/10.33830/jom.v20i2.9123.2024
Rafiq, F., Dogra, N., Adil, M., & Wu, J. Z. (2022). Examining consumer’s intention to adopt AI-chatbots in tourism using partial least squares structural equation modeling method. Mathematics, 10(13). https://doi.org/10.3390/math10132190
Rudolph, J., Tan, S., & Tan, S. (2023). War of the chatbots: Bard, Bing Chat, ChatGPT, Ernie and beyond. The new AI gold rush and its impact on higher education. Journal of Applied Learning and Teaching, 6(1), 364–389. https://doi.org/10.37074/jalt.2023.6.1.23
Scarpi, D. (2024). Strangers or friends? Examining chatbot adoption in tourism through psychological ownership. Tourism Management, 102. https://doi.org/10.1016/j.tourman.2023.104873
Sudaryanto, M. R., Hendrawan, M. A., & Andrian, T. (2023). The effect of technology readiness, digital competence, perceived usefulness, and ease of use on accounting students artificial intelligence technology adoption. E3S Web of Conferences, 388. https://doi.org/10.1051/e3sconf/202338804055
Sujood, A. R., Arwab, M., & Hamid, S. (2023). Post-pandemic intention of the tourism and hospitality (T&H) industry employees towards the use of information technology. Tourism and Hospitality Management, 29(2), 279–295. https://doi.org/10.20867/thm.29.2.12
Talha, M., Nasreen, D. F., Farooq, L., & Fatima, D. (2025). Evaluating the perceived usefulness and ease of adoption of artificial intelligence tools in teaching. Journal of Social Signs Review, 3(5). https://socialsignsreivew.com/index.php/12/f
Vidarshika, W., Dayapathirana, N., & Ranasinghe, A. (2025). Understanding AI chatbot adoption in education: The role of perceived usefulness, ease of use, and anthropomorphic tendencies. Proceedings - International Research Conference on Smart Computing and Systems Engineering, SCSE 2025. https://doi.org/10.1109/SCSE65633.2025.11031020
Wube, H. D., Esubalew, S. Z., Weldesellasie, F. F., & Debelee, T. G. (2022). Text-based chatbot in the financial sector: A systematic literature review. Data Science in Finance and Economics, 2(3), 232–259. https://doi.org/10.3934/dsfe.2022011
Xu, X., & Chen, Y. (2025). Exploring customer intention of chatbot in tourism and hospitality sectors: A systematic review and way forward. Tourism Recreation Research. https://doi.org/10.1080/02508281.2025.2486314
Zhang, B., Zhu, Y., Deng, J., Zheng, W., Liu, Y., Wang, C., & Zeng, R. (2023). "I am here to assist your tourism": Predicting continuance intention to use AI-based chatbots for tourism. Does gender really matter? International Journal of Human-Computer Interaction, 39(9), 1887–1903. https://doi.org/10.1080/10447318.2022.2124345
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