AI Recommendation Systems and Digital Promotion Effectiveness on TikTok Social Commerce in Indonesia
DOI:
https://doi.org/10.32492/jeetech.v7i1.7105Keywords:
Artificial intelligence, recommendation system, digital promotion, effectiveness, social commerce, TikTokAbstract
The development of artificial intelligence has brought fundamental changes to the social commerce ecosystem, particularly on the TikTok platform, which has now become one of the largest online shopping channels in Indonesia. This study aims to analyze the effect of the artificial intelligence recommendation system on the effectiveness of digital promotion on the TikTok platform in Indonesia, while considering the moderating roles of user trust and privacy concerns. The method used is quantitative, with a cross-sectional survey design involving 320 active TikTok users aged 17–45 years. The data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) through SmartPLS 4.0 software. The results show that the AI recommendation system has a positive and significant effect on both user engagement (β = 0.612) and purchase conversion (β = 0.548). User trust was found to strengthen this relationship, while privacy concerns significantly weakened it. These findings confirm that the effectiveness of AI-based promotion does not depend solely on the sophistication of the algorithm, but also on the ecosystem of trust built among the platform, marketers, and consumers. This study provides practical contributions for digital business actors in Indonesia in designing promotional strategies that are more human-centered and responsible.
References
Y. K. Dwivedi and others, “So What If ChatGPT Wrote It? Multidisciplinary Perspectives on Generative Conversational AI,” Int. J. Inf. Manage., vol. 71, p. 102642, 2023, doi: 10.1016/j.ijinfomgt.2023.102642.
M. N. Fitrianto, “Analisis Pengaruh Algoritma Rekomendasi TikTok terhadap Perilaku Konsumsi Konten,” Jurnal Publikasi Teknik Informatika, vol. 4, no. 2, pp. 68–75, 2025.
S. Kemp, “Digital 2023: Indonesia,” 2023, DataReportal.
D. Riswan, H. Eko, R. Putra, and R. N. Saputra, “Pengembangan Sistem Rekomendasi Berbasis Kecerdasan Buatan untuk Meningkatkan Pengalaman Pengguna di Platform E-Commerce,” Jurnal Komputer dan Teknologi Informasi, vol. 2, no. 3, pp. 572–580, 2024.
A. Al Marzaq, J. W. A. Kusuma, K. Riyandi, and D. Pratama, “Pengaruh Teknologi AI pada Algoritma TikTok terhadap Respons dan Penerimaan Pengguna,” Jurnal REIN, vol. 2, no. 1, pp. 9–15, 2025.
J. Zhang, G. Adomavicius, A. Gupta, and W. Ketter, “Consumption and Performance: Understanding Longitudinal Dynamics of Recommender Systems via an Agent-Based Simulation Framework,” Information Systems Research, vol. 31, no. 1, 2022, doi: 10.1287/isre.2019.0876.
M. N. Ardiansah, A. Azizah, and A. Sadida, “Social Commerce Success Impact on Business Performance Insight from TikTok Shop Phenomena in Indonesia,” Accounting Analysis Journal, vol. 13, no. 1, pp. 25–33, 2024, doi: 10.15294/aaj.v13i1.2778.
F. K. Tsabat, M. R. Firdausa, and D. Dian, “Analisis Perilaku Gen Z dalam Memilih Layanan Berbasis Digital E-Commerce,” Jurnal Inovasi Manajemen dan Kewirausahaan, vol. 6, no. 3, pp. 41–54, 2025.
L. Auliarahman, “From Impulse to Loyalty: Examining Drivers of Continuous Purchase Intention on the TikTok Live Shopping Platform,” BISMA (Bisnis dan Manajemen), vol. 17, no. 2, pp. 169–191, 2025, doi: 10.26740/bisma.v17n2.p169-191.
M. Chandraa, D. W. Sukmaningsih, and E. Sriwardiningsih, “The Impact of Live Streaming on Purchase Intention in Social Commerce in Indonesia,” Procedia Comput. Sci., vol. 234, pp. 987–995, 2024, doi: 10.1016/j.procs.2024.03.088.
W. M. Lim, T. Rasul, S. Kumar, and M. Ala, “Past, Present, and Future of Customer Engagement,” J. Bus. Res., vol. 140, pp. 439–458, 2022, doi: 10.1016/j.jbusres.2021.11.014.
Y. Sun, S. Li, and L. Yu, “The Dark Sides of AI Personal Assistant: Effects of Service Failure on User Continuance Intention,” Electronic Markets, vol. 32, no. 1, pp. 17–39, 2022, doi: 10.1007/s12525-021-00483-2.
M. Syafriani and D. Hidayah, “The Use of Influencers and TikTok’s Recommendation Algorithm on Purchase Decision and Its Implication on E-Loyalty,” Procuratio, vol. 13, no. 3, pp. 152–164, 2025, doi: 10.35145/procuratio.v13i3.5226.
N. I. Prestyasih and S. R. H. Hati, “The Role of Social Commerce Trust and Satisfaction on TikTok Consumer Purchasing Behavior,” Jurnal Ilmiah Manajemen Kesatuan, vol. 13, no. 4, pp. 2817–2826, 2025, doi: 10.37641/jimkes.v13i4.3455.
I. W. Sari, H. Erliana, C. M. Nurul Akla, and N. Yudisha, “The Effect of AI-Based Recommendation System for Material Personalization on Vocational Student Satisfaction,” G-Tech Journal, vol. 9, no. 4, pp. 2207–2217, 2025.
C. Fornell and D. F. Larcker, “Structural Equation Models with Unobservable Variables and Measurement Error: Algebra and Statistics,” Journal of Marketing Research, vol. 18, no. 3, pp. 382–388, 1981.
B. Rolando, “How TikTok Shapes and Influences Modern Consumer Shopping Patterns,” ABDIMAS, vol. 1, no. 1, pp. 1–15, 2025.
J. F. Hair, B. J. Babin, R. E. Anderson, and W. C. Black, Multivariate Data Analysis. Cengage Learning, 2022.
C. M. Ringle, M. Sarstedt, N. Sinkovics, and R. R. Sinkovics, “A Perspective on Using Partial Least Squares Structural Equation Modelling in Data Articles,” Data Brief, vol. 48, 2023, doi: 10.1016/j.dib.2023.109074.
E. A. Apriadi, R. Julianto, F. Dwiatmoko, M. Bisri, and M. Kom, Kecerdasan Buatan: Teori, Implementasi, dan Aplikasi di Era Digital. 2025.
Published
How to Cite
Issue
Section
Copyright (c) 2026 Efi Ero Sofia, Eko Aziz Apriadi, Ribut Julianto, Muawan Bisri
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
