The Impact of AI Personalization on Customer Engagement on TikTok: The Role of Trust, Perceived Usefulness, and Usage Intention
DOI:
https://doi.org/10.38035/jimt.v7i4.8190Keywords:
AI Personalization, Customer Engagement, TikTok, SOR ModelAbstract
Amid the rapid integration of artificial intelligence (AI) in social media, this study examines the effect of AI-driven personalization on customer engagement on TikTok, with trust, perceived usefulness, and usage intention as mediating variables. Using the Stimulus–Organism–Response (SOR) framework, data were collected from 238 Indonesian users aged 18–34 and analyzed using Structural Equation Modeling (SEM). The results show that AI personalization has significant positive effects on trust, perceived usefulness, and usage intention. These variables, in turn, significantly influence customer engagement. Mediation analysis indicates that trust and usage intention fully mediate the relationship between AI personalization and engagement, while perceived usefulness acts as a partial mediator. Furthermore, Multi-Group Analysis (MGA) reveals no significant differences across gender and education, indicating model consistency across demographic groups. This study highlights that in fast-paced platforms like TikTok, trust and behavioral intention play a more critical role than functional value in driving engagement. Practically, the findings emphasize the need for platforms to enhance transparency and user control alongside algorithmic sophistication.
References
Adawiyah, S. R., Purwandari, B., Eitiveni, I., & Purwaningsih, E. H. (2024). The Influence of AI and AR Technology in Personalized Recommendations on Customer Usage Intention: A Case Study of Cosmetic Products on Shopee. Applied Sciences, 14(13), 5786. https://doi.org/10.3390/app14135786
Ameen, N., Tarhini, A., Reppel, A., & Anand, A. (2021). Customer experiences in the age of artificial intelligence. Computers in Human Behavior, 114, 106548. https://doi.org/10.1016/j.chb.2020.106548
Asyraff, M. A., Hanafiah, M. H., Aminuddin, N., & Mahdzar, M. (2023). Adoption of the Stimulus-Organism-Response (S-O-R) Model in Hospitality and Tourism Research: Systematic Literature Review and Future Research Directions Adoption of the Stimulus-Organism-Response (S-O-R) model in hospitality and tourism research: Systematic literature review and future research directions. Asia-Pacific Journal of Innovation in Hospitality and Tourism (APJIHT), 12(1).
Bag, S., Srivastava, G., Bashir, M. M. Al, Kumari, S., Giannakis, M., & Chowdhury, A. H. (2022). Journey of customers in this digital era: Understanding the role of artificial intelligence technologies in user engagement and conversion. Benchmarking: An International Journal, 29(7), 2074–2098. https://doi.org/10.1108/BIJ-07-2021-0415
Bitrián, P., Buil, I., & Catalán, S. (2021). Enhancing user engagement: The role of gamification in mobile apps. Journal of Business Research, 132, 170–185. https://doi.org/10.1016/j.jbusres.2021.04.028
Bougie, Roger., & Sekaran, Uma. (2020). Research methods for business : a skill-building approach. John Wiley & Sons, Inc.
Bryman, Alan., & Bell, Emma. (2015). Business research methods. Oxford University Press.
Chen, Y., Prentice, C., Weaven, S., & Hisao, A. (2022). The influence of customer trust and artificial intelligence on customer engagement and loyalty – The case of the home-sharing industry. Frontiers in Psychology, 13. https://doi.org/10.3389/fpsyg.2022.912339
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
Cohen, Jacob. (1988). Statistical power analysis for the behavioral sciences. Psychology Press, Taylor & Francis Group.
Corrêa, S. C. H., Soares, J. L., Christino, J. M. M., Gosling, M. de S., & Gonçalves, C. A. (2020). The influence of YouTubers on followers’ use intention. Journal of Research in Interactive Marketing, 14(2), 173–194. https://doi.org/10.1108/JRIM-09-2019-0154
Dash, G., & Paul, J. (2021). CB-SEM vs PLS-SEM methods for research in social sciences and technology forecasting. Technological Forecasting and Social Change, 173, 121092. https://doi.org/10.1016/j.techfore.2021.121092
Davenport, T., Guha, A., Grewal, D., & Bressgott, T. (2020). How artificial intelligence will change the future of marketing. Journal of the Academy of Marketing Science, 48(1), 24–42. https://doi.org/10.1007/s11747-019-00696-0
Davis, F., Francis Gnanasekar, M. B., & Parayitam, S. (2021). Trust and product as moderators in online shopping behavior: evidence from India. South Asian Journal of Marketing, 2(1), 28–50. https://doi.org/10.1108/SAJM-02-2021-0017
Duarte, F. (2025). TikTok User Age, Gender, & Demographics (2025). Exploding Topics. https://explodingtopics.com/blog/tiktok-demographics
Gao, Y., & Liu, H. (2023). Artificial intelligence-enabled personalization in interactive marketing: a customer journey perspective. Journal of Research in Interactive Marketing, 17(5), 663–680. https://doi.org/10.1108/JRIM-01-2022-0023
Gligorea, I., Cioca, M., Oancea, R., Gorski, A.-T., Gorski, H., & Tudorache, P. (2023). Adaptive Learning Using Artificial Intelligence in e-Learning: A Literature Review. Education Sciences, 13(12), 1216. https://doi.org/10.3390/educsci13121216
Hair, J., & Alamer, A. (2022). Partial Least Squares Structural Equation Modeling (PLS-SEM) in second language and education research: Guidelines using an applied example. Research Methods in Applied Linguistics, 1(3), 100027. https://doi.org/10.1016/j.rmal.2022.100027
Hair, J. F. ., Hult, G. T. M. ., Ringle, C. M. ., Sarstedt, Marko., Danks, N. P. ., & Ray, Soumya. (2021). Partial least squares structural equation modeling (PLS-SEM) using R : a workbook. Springer.
Halim, E., Buana, M. K., Hartono, H., Ferdianto, & Hebrard, M. (2022). Analysis of AI-enabled Service Quality and Personalization to Continuous Usage Intention. 2022 International Conference on Information Management and Technology (ICIMTech), 699–704. https://doi.org/10.1109/ICIMTech55957.2022.9915042
Hochreiter, V., Benedetto, C., & Loesch, M. (2023). The Stimulus-Organism-Response (S-O-R) Paradigm as a Guiding Principle in Environmental Psychology: Comparison of its Usage in Consumer Behavior and Organizational Culture and Leadership Theory. Journal of Entrepreneurship and Business Development, 3(1), 7–16. https://doi.org/10.18775/jebd.31.5001
Huang, M.-H., & Rust, R. T. (2021). A strategic framework for artificial intelligence in marketing. Journal of the Academy of Marketing Science, 49(1), 30–50. https://doi.org/10.1007/s11747-020-00749-9
Kang, H., & Lou, C. (2022). AI agency vs. human agency: understanding human–AI interactions on TikTok and their implications for user engagement. Journal of Computer-Mediated Communication, 27(5). https://doi.org/10.1093/jcmc/zmac014
Kemp, S. (2025). DIGITAL 2025: INDONESIA. DATAREPORTAL. https://datareportal.com/reports/digital-2025-indonesia
Kock, N. (2015). Common Method Bias in PLS-SEM. International Journal of E-Collaboration, 11(4), 1–10. https://doi.org/10.4018/ijec.2015100101
Kock, N., & Hadaya, P. (2018). Minimum sample size estimation in PLS‐SEM: The inverse square root and gamma‐exponential methods. Information Systems Journal, 28(1), 227–261. https://doi.org/10.1111/isj.12131
Kumar, M. M., Kesharwani, A., Gautam, V., & Sinha, P. (2022). Stimulus-Organism-Response (S-O-R) Model Application in Examining the Effectiveness of Public Service Advertisements. INTERNATIONAL JOURNAL OF BUSINESS, 27(2), 2022. https://sanevax.org/media-
Marjerison, R. K., Dong, H., Kim, J.-M., Zheng, H., Zhang, Y., & Kuan, G. (2025). Understanding User Acceptance of AI-Driven Chatbots in China’s E-Commerce: The Roles of Perceived Authenticity, Usefulness, and Risk. Systems, 13(2), 71. https://doi.org/10.3390/systems13020071
Matusin, I. O., Matusin, A. R., Nasution, C. F., & Irma, D. (2023). The Effect of Social Media Marketing on Consumer Engagement and Electronic Word-Of-Mouth. International Journal of Social Science and Human Research, 06(02). https://doi.org/10.47191/ijsshr/v6-i2-06
Mehrabian, A., & Russell, J. A. (1974). An Approach to Environmental Psychology. The MIT Press.
Molina, M. D., & Sundar, S. S. (2022). When AI moderates online content: effects of human collaboration and interactive transparency on user trust. Journal of Computer-Mediated Communication, 27(4). https://doi.org/10.1093/jcmc/zmac010
Nagy, S., & Hajdu, N. (2021). Consumer Acceptance of the Use of Artificial Intelligence in Online Shopping: Evidence From Hungary. Www.Amfiteatrueconomic.Ro, 23(56), 155. https://doi.org/10.24818/EA/2021/56/155
Pan, J., Ishak, N. A., & Qin, Y. (2024). The application of Moore’s online learning interactions model in learning outcomes: The SOR (stimulus-organism-response) paradigm perspective. Heliyon, 10(7), e28505. https://doi.org/10.1016/j.heliyon.2024.e28505
Saunders, M. N. K. ., Lewis, Philip., & Thornhill, Adrian. (2023). Research methods for business students. Pearson.
Shang, Y., Rehman, H., Mehmood, K., Xu, A., Iftikhar, Y., Wang, Y., & Sharma, R. (2022). The Nexuses Between Social Media Marketing Activities and Consumers’ Engagement Behaviour: A Two-Wave Time-Lagged Study. Frontiers in Psychology, 13. https://doi.org/10.3389/fpsyg.2022.811282
Sugiyono. (2018). METODE PENELITIAN KUANTITATIF. Alfabeta.
Sung, E. (Christine), Bae, S., Han, D.-I. D., & Kwon, O. (2021). Consumer engagement via interactive artificial intelligence and mixed reality. International Journal of Information Management, 60, 102382. https://doi.org/10.1016/j.ijinfomgt.2021.102382
Teepapal, T. (2025). AI-driven personalization: Unraveling consumer perceptions in social media engagement. Computers in Human Behavior, 165. https://doi.org/10.1016/j.chb.2024.108549
Varsha P. S., Akter, S., Kumar, A., Gochhait, S., & Patagundi, B. (2021). The Impact of Artificial Intelligence on Branding. Journal of Global Information Management, 29(4), 221–246. https://doi.org/10.4018/JGIM.20210701.oa10
Vindytia, M., & Balqiah, T. E. (2024). AI Marketing Impact on Consumer Behavior: An SOR Model Analysis of Online Food Delivery Services. Jurnal Dinamika Manajemen, 15(2), 215–228. https://doi.org/10.15294/jdm.v15i2.6758
Walters, W. H. (2021). Survey design, sampling, and significance testing: Key issues. The Journal of Academic Librarianship, 47(3), 102344. https://doi.org/10.1016/j.acalib.2021.102344
Wang, C., Ahmad, S. F., Bani Ahmad Ayassrah, A. Y. A., Awwad, E. M., Irshad, M., Ali, Y. A., Al-Razgan, M., Khan, Y., & Han, H. (2023). RETRACTED: An empirical evaluation of technology acceptance model for Artificial Intelligence in E-commerce. Heliyon, 9(8), e18349. https://doi.org/10.1016/j.heliyon.2023.e18349
Wang, W., Chen, Z., & Kuang, J. (2025). Artificial Intelligence-Driven Recommendations and Functional Food Purchases: Understanding Consumer Decision-Making. Foods, 14(6), 976. https://doi.org/10.3390/foods14060976
Zhou, R. (2024). Understanding the Impact of TikTok’s Recommendation Algorithm on User Engagement. International Journal of Computer Science and Information Technology, 3(2), 201–208. https://doi.org/10.62051/ijcsit.v3n2.
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