Revisiting the Stimulus, Organism, Response Model in AI-Driven Tourism: A Multi-Path Analysis of Personalization, Perception, and Privacy

Arnas Hasanuddin; Achmad Ansari Hasanuddin; Askari Hasanuddin; Sitti Mujahida Baharuddin

doi:10.14203/STIPM.2025.428

Revisiting the Stimulus, Organism, Response Model in AI-Driven Tourism: A Multi-Path Analysis of Personalization, Perception, and Privacy

Arnas Hasanuddin, Achmad Ansari Hasanuddin, Askari Hasanuddin, Sitti Mujahida Baharuddin

Abstract

This study revisits the Stimulus, Organism, Response (S-O-R) model to examine the psychological mechanisms underlying tourists’ responses to AI-based personalization on digital tourism platforms. Drawing on a sample of 360 Indonesian respondents collected via online survey, the research investigates how AI-driven personalization influences three organismic states perceived value, trust, and privacy concern and how these states affect tourists’ behavioral intentions. Using partial least squares structural equation modeling (PLS-SEM), the results reveal that personalization significantly enhances perceived value and trust, while also reducing privacy concern. Each of these organismic responses, in turn, significantly shapes behavioral intention, confirming the relevance of the extended S-O-R framework in the AI tourism context. Theoretically, the study contributes to tourism literature by integrating both positive and negative psychological reactions into a unified explanatory model, highlighting personalization’s dual role as both functional and ethical stimulus. Practically, the findings offer insights for tourism platforms to design AI services that are not only adaptive and efficient but also transparent and trust-enhancing. Limitations include the study’s cross-sectional design and geographic concentration, pointing to future research directions involving longitudinal analysis, cross-cultural comparisons, and exploration of moderating variables such as digital literacy and cultural norms.

Full Text:

PDF

References

Adilović, E. (2024). The role of perceived risk and trust in “consumers” participation in tourism-based sharing economy services [Thesis, [E. Adilović]]. http://ebiblioteka.efsa.unsa.ba/xmlui/handle/EFSA/800

Aggarwal, R., Dhingra, S., & Mittal, S. (2024). What reduces the privacy concerns of the customers towards the use of location-based advertising? An empirical investigation. Journal of Location Based Services, 18(2), 139–161. https://doi.org/10.1080/17489725.2023.2256701

Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-T

Amil, Y. (2024). The Impact of AI-Driven Personalization Tools on Privacy Concerns and Consumer Trust in E-commerce. https://matheo.uliege.be/handle/2268.2/21371

Asif, F., Jamshed, K., & Shah, S. H. A. (2025). Prospects and Challenges for AI and Technological Innovation in the European Travel and Tourism Sector: Impact, Effect and Sustainable Development Goals. In Technological Innovation and AI for Sustainable Development in Events and Festivals (pp. 140–161). https://doi.org/10.1079/9781836990185.0012

Asthana, S., Im, J., Chen, Z., & Banovic, N. (2024). “I know even if you don’t tell me”: Understanding Users’ Privacy Preferences Regarding AI-based Inferences of Sensitive Information for Personalization. Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems, 1–21. https://doi.org/10.1145/3613904.3642180

Bhuiyan, M. S. (2024). The Role of AI-Enhanced Personalization in Customer Experiences. Journal of Computer Science and Technology Studies, 6(1), Article 1. https://doi.org/10.32996/jcsts.2024.6.1.17

Blut, M., Chaney, D., Lunardo, R., Mencarelli, R., & Grewal, D. (2024). Customer Perceived Value: A Comprehensive Meta-analysis. Journal of Service Research, 27(4), 501–524. https://doi.org/10.1177/10946705231222295

Chen, T., Liu, F., Shen, X.-L., Wu, J., & Liu, Y. (2025). Conceptualization of privacy concerns and their influence on consumers’ resistance to AI-based recommender systems in e-commerce. Industrial Management & Data Systems, 125(5), 1844–1868. https://doi.org/10.1108/IMDS-03-2024-0251

Cheng, L., Han, J., & Nasirov, J. (2024). Ethical considerations related to personal data collection and reuse: Trust and transparency in language and speech technologies. International Journal of Legal Discourse, 9(2), 217–235. https://doi.org/10.1515/ijld-2024-2010

Chin, W. W. (1998). The Partial Least Squares Approach to Structural Equation Modeling. In Modern Methods for Business Research. Psychology Press.

Chou, Y.-H., Hsiao, K.-L., Lu, Y.-T., & Su, Y.-S. (2025). Exploring the factors affecting the intention to use AI systems in the health-care field with social cognitive theory: A two-stage SEM-ANN approach. Chinese Management Studies. https://doi.org/10.1108/CMS-10-2024-0746

Cloarec, J. (2020). The personalization–privacy paradox in the attention economy. Technological Forecasting and Social Change, 161, 120299. https://doi.org/10.1016/j.techfore.2020.120299

Elshaer, I. A., Azazz, A. M. S., Fayyad, S., Aljoghaiman, A., Fathy, E. A., & Fouad, A. M. (2025). From Asymmetry to Satisfaction: The Dynamic Role of Perceived Value and Trust to Boost Customer Satisfaction in the Tourism Industry. Tourism and Hospitality, 6(2), 68. https://doi.org/10.3390/tourhosp6020068

Erensoy, A., Mathrani, A., Schnack, A., Elms, J., & Baghaei, N. (2024). Consumer behavior in immersive virtual reality retail environments: A systematic literature review using the stimuli-organisms-responses (S-O-r) model. Journal of Consumer Behaviour, 23(6), 2781–2811. https://doi.org/10.1002/cb.2374

Ezekwe, C. I. (2025). INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS). SSRN Electronic Journal. https://doi.org/10.2139/ssrn.5065151

Fakfare, P., Manosuthi, N., Lee, J.-S., Jin, M., Han, H., & Kim, J. J. (2024). Data vulnerability and privacy risk among hotel guests who share personal data. Journal of Vacation Marketing, 13567667241276213. https://doi.org/10.1177/13567667241276213

Guo, X., Deng, H., Zhang, S., & Chen, G. (2020). Signals of Competence and Warmth on E-Commerce Platforms. Data and Information Management, 4(2), 81–93. https://doi.org/10.2478/dim-2020-0010

Han, H., Fakfare, P., Manosuthi, N., Lee, J.-S., & Kim, J. J. (2025). Optimum combination impact of data privacy on customer trust and willingness to accept personal information collection in the lodging sector. Journal of Hospitality and Tourism Technology. https://doi.org/10.1108/JHTT-06-2024-0377

Hanif, R., Astuti, W., & Sunardi, S. (2024). The Mediating Role of Customer Satisfaction in the Effect of Perceived Enjoyment on Customer Trust in Online Investment Application. Innovation Business Management and Accounting Journal, 3(1), Article 1. https://doi.org/10.56070/ibmaj.2024.003

Hassan, N., Abdelraouf, M., & El-Shihy, D. (2025). The moderating role of personalized recommendations in the trust–satisfaction–loyalty relationship: An empirical study of AI-driven e-commerce. Future Business Journal, 11(1), 66. https://doi.org/10.1186/s43093-025-00476-z

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

Huang, A., Ozturk, A. B., Zhang, T., de la Mora Velasco, E., & Haney, A. (2024). Unpacking AI for hospitality and tourism services: Exploring the role of perceived enjoyment on future use intentions. International Journal of Hospitality Management, 119, 103693. https://doi.org/10.1016/j.ijhm.2024.103693

Jayapal, J. (2025). Unveiling the Impact of AI-Driven Personalization on Customer Loyalty in Online Shopping: The Moderating Effects of Privacy Concerns. Journal of Promotion Management, 31(5), 865–894. https://doi.org/10.1080/10496491.2025.2525099

Kanaparthi, V. (2024). AI-based Personalization and Trust in Digital Finance (No. arXiv:2401.15700). arXiv. https://doi.org/10.48550/arXiv.2401.15700

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), Article 9. https://doi.org/10.3390/socsci13090475

Keskimäki, A. (2025). The Privacy Paradox: Exploring the role of privacy concerns and perceived benefits in mobile app usage.

Khamaj, A., & Ali, A. M. (2024). Adapting user experience with reinforcement learning: Personalizing interfaces based on user behavior analysis in real-time. Alexandria Engineering Journal, 95, 164–173. https://doi.org/10.1016/j.aej.2024.03.045

Kim, S. Y., & Kim, J. (2025). The impact of AI recommendation quality on service satisfaction: The moderating roles of standardization and customization. Journal of Services Marketing, 39(4), 365–386. https://doi.org/10.1108/JSM-05-2024-0214

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

Lin, H.-C., Liu, X., Huang, Y., & Chen, H.-Y. (2023). Determinants of continued use of tourism and hospitality e-commerce platforms and the role of information transparency. Current Issues in Tourism, 26(19), 3140–3159. https://doi.org/10.1080/13683500.2022.2147052

Menard, P., & Bott, G. J. (2025a). Artificial intelligence misuse and concern for information privacy: New construct validation and future directions. Information Systems Journal, 35(1), 322–367. https://doi.org/10.1111/isj.12544

Menard, P., & Bott, G. J. (2025b). Artificial intelligence misuse and concern for information privacy: New construct validation and future directions. Information Systems Journal, 35(1), 322–367. https://doi.org/10.1111/isj.12544

Ng, K. C., Zhang, X., Thong, J. Y. L., & Tam, K. Y. (2021). Protecting Against Threats to Information Security: An Attitudinal Ambivalence Perspective. Journal of Management Information Systems, 38(3), 732–764. https://doi.org/10.1080/07421222.2021.1962601

Nugroho, I. S., Priyanto, D., & Purnama, Y. (2024). Exploring the Role of Artificial Intelligence (AI) in Designing and Optimizing Personalization Strategies to Enhance Customer Experience in the Tourism Industry. Technology and Society Perspectives (TACIT), 2(3), Article 3. https://doi.org/10.61100/tacit.v2i3.247

Palamidovska-Sterjadovska, N., Prodanova, J., & Ciunova-Shuleska, A. (2024). Why do customers value m-banking apps? A stimulus-organism-response perspective. Spanish Journal of Marketing - ESIC, 28(4), 481–499. https://doi.org/10.1108/SJME-01-2023-0024

Prasetya, A. A., & Kuswati, R. (2025). Online Impulse Buying: The Application of Srimulus-Organism-Response Model. Indonesian Interdisciplinary Journal of Sharia Economics (IIJSE), 8(2), Article 2.

Rafi, K. N., Gani, M. O., Hashim, N. M. H. N., Rahman, M. S., & Masukujjaman, M. (2024). The influence of 360-degree VR videos on tourism web usage behaviour: The role of web navigability and visual interface design quality. Tourism Review, 80(3), 725–741. https://doi.org/10.1108/TR-06-2023-0383

Rahaman, M. A., Amin, S. N., Rahman, Md. A., & Amin, Md. N. (2025). Security Concerns, Public perceptions and Safety Initiatives at Tourist Destinations in Bangladesh: Effective Tourist Police Interventions for Sustainable Tourism. Sustainable Futures, 101004. https://doi.org/10.1016/j.sftr.2025.101004

Rinne, K. J. A. (2025). AI-Powered Customer Engagement: How Personalization, Chatbots, and Recommendation Systems Influence E-commerce Customer Satisfaction? [fi=AMK-opinnäytetyö|sv=YH-examensarbete|en=Bachelor’s thesis|]. http://www.theseus.fi/handle/10024/891700

Russell, J. A., & Mehrabian, A. (1974). Distinguishing anger and anxiety in terms of emotional response factors. Journal of Consulting and Clinical Psychology, 42(1), 79–83. https://doi.org/10.1037/h0035915

Sardianos, C., Varlamis, I., Chronis, C., Dimitrakopoulos, G., Alsalemi, A., Himeur, Y., Bensaali, F., & Amira, A. (2021). The emergence of explainability of intelligent systems: Delivering explainable and personalized recommendations for energy efficiency. International Journal of Intelligent Systems, 36(2), 656–680. https://doi.org/10.1002/int.22314

Schelenz, L., Segal, A., Adelio, O., & Gal, K. (2024). Transparency-Check: An Instrument for the Study and Design of Transparency in AI-based Personalization Systems. ACM J. Responsib. Comput., 1(1), 8:1-8:18. https://doi.org/10.1145/3636508

Shabankareh, M., Khamoushi Sahne, S. S., Nazarian, A., & Foroudi, P. (2025). The impact of AI perceived transparency on trust in AI recommendations in healthcare applications. Asia-Pacific Journal of Business Administration. https://doi.org/10.1108/APJBA-12-2024-0690

Sia, P. Y.-H., Saidin, S. S., & Iskandar, Y. H. P. (2023). 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

Sidiq, Z. F., Z, S., Wahyuzi, Z., & Ifada, Z. (2025). The Role of Artificial Intelligence in Transforming Smart Tourism: Enhancing Customer Experience and Service Personalization. Journal of Sharia Economy and Islamic Tourism, 5(2), 82–98. https://doi.org/10.31764/jseit.v5i2.30705

Sidlauskiene, J. (2022). What Drives Consumers’ Decisions to Use Intelligent Agent Technologies? A Systematic Review. Journal of Internet Commerce, 21(4), 438–475. https://doi.org/10.1080/15332861.2021.1961192

Suanpang, P., & Jamjuntr, P. (2024). Optimizing Service Scheduling by Genetic Algorithm Support Decision-Making in Smart Tourism Destinations. Decision Making: Applications in Management and Engineering, 7(1), Article 1. https://doi.org/10.31181/dmame7120241273

Tedja, B., Al Musadieq, M., Kusumawati, A., & Yulianto, E. (2024). Systematic literature review using PRISMA: Exploring the influence of service quality and perceived value on satisfaction and intention to continue relationship. Future Business Journal, 10(1), 39. https://doi.org/10.1186/s43093-024-00326-4

Wang, Z., Yuan, R., & Li, B. (2025). Are Recommendation Systems Annoying? An Empirical Study of Assessing the Impacts of AI Characteristics on Technology Well-Being. Journal of Consumer Behaviour, 24(1), 178–200. https://doi.org/10.1002/cb.2408

Wanner, D., Hashim, H. A., Srivastava, S., & Steinhauer, A. (2024). UAV avionics safety, certification, accidents, redundancy, integrity, and reliability: A comprehensive review and future trends. Drone Systems and Applications, 12, 1–23. https://doi.org/10.1139/dsa-2023-0091

Yang, D. (2024). Explaining the personalisation privacy paradox using an integrated experience model. https://doi.org/10.7488/era/4627

Yang, X., Zhang, L., & Feng, Z. (2024). Personalized Tourism Recommendations and the E-Tourism User Experience. Journal of Travel Research, 63(5), 1183–1200. https://doi.org/10.1177/00472875231187332

Yum, K., & Kim, J. (2024). The Influence of Perceived Value, Customer Satisfaction, and Trust on Loyalty in Entertainment Platforms. Applied Sciences, 14(13), Article 13. https://doi.org/10.3390/app14135763

DOI: http://dx.doi.org/10.14203/STIPM.2025.428