Analysis of the Effect of AI Personalization on User Trust and Perceived Price Value and Its Implications for Subscription Intention to Music Streaming Services
DOI:
https://doi.org/10.59261/jequi.v8i2.291Keywords:
Trust, Personalization, Artificial Intelligence (AI), Platform Streaming Musik, Price Value, Intention to SubscribeAbstract
Background: This study examines the influence of artificial intelligence (AI)-based personalization systems and user trust on the intention to subscribe to music streaming platforms in Indonesia.
Objective: The research objectives include analyzing the impact of personalization on Price Value, Performance Expectancy, and Trust in AI; as well as the mediating effect of trust and expectation on Intention to Subscribe.
Methods: The research method is quantitative using a questionnaire survey distributed purposively to active users of Spotify and YouTube Music in Indonesia. The sample was determined using the Slovin formula, resulting in 420 valid respondents consisting of both free tier and premium tier users. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) via SmartPLS software.
Results: The results show that AI Personalization has a significant effect on Trust in AI and Price Value. Trust in AI is proven to be an important mediator that increases Performance Expectancy and Effort Expectancy. It was found that Price Value is the most dominant determinant influencing subscription intention, followed by Performance Expectancy and Habit.
Conclusion: This study concludes that algorithm transparency and user control are key to building trust, which ultimately drives the perceived value worth subscribing to.
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References
Ávila Torres, V., & Beer, D. (2025). Music streaming, cultural consumption and the everyday routines of algorithm management: Exploring how trust and objective setting shape everyday encounters with algorithmic systems. European Journal of Cultural Studies, 28(6), 1778–1798. https://doi.org/10.1177/13675494251329239
APJII. (2024). Survei Penetrasi Internet Indonesia 2024.
Barata, M. L., & Coelho, P. S. (2021). Music streaming services: understanding the drivers of customer purchase and intention to recommend. Heliyon, 7(8). https://doi.org/10.1016/j.heliyon.2021.e07783
Berente, N., Gu, B., Recker, J., & Santhanam, R. (2021). Managing artificial intelligence. In MIS quarterly (Vol. 45, Issue 3, pp. 1433–1450). Management Information Systems Research Center, University of Minnesota. https://doi.org/10.25300/misq/2021/16274
Chang, V., Yang, Y., Xu, Q. A., & Xiong, C. (2021). Factors influencing consumer intention to subscribe to the premium music streaming services in China. Journal of Global Information Management (JGIM), 29(6), 1–25. https://doi.org/10.4018/JGIM.20211101.oa17
Cheng, Y., Sharma, S., Sharma, P., & Kulathunga, K. (2020). Role of personalization in continuous use intention of Mobile news apps in India: Extending the UTAUT2 model. Information, 11(1), 33. https://doi.org/10.3390/info11010033
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
Dongre, S., & Mehta, S. (2023). DeLT Net: Unveiling Sponsor Segments in YouTube Videos with DistilBert, LSTM, and DeiT fusion models. 2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT), 1–7. https://doi.org/10.1109/ICCCNT56998.2023.10308354
Elsafty, A., & Boghdady, A. (2022). The cognitive determinants influencing consumer purchase-intention towards subscription video on demand (SVoD): case of Egypt. International Journal of Marketing Studies, 14(1), 95. https://doi.org/10.5539/ijms.v14n1p95
Google. (2025). A new milestone for Youtube Music and Premium. https://blog.google/intl/en-in/feed/a-new-milestone-for-youtube-music-and-premium/
Hair Jr, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2021). A primer on partial least squares structural equation modeling (PLS-SEM). Sage publications.
Halim, E., Buana, M. K., Hartono, H., & 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
Herlocker, J. L., Konstan, J. A., Terveen, L. G., & Riedl, J. T. (2004). Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems (TOIS), 22(1), 5–53. https://doi.org/10.1145/963770.96377
IFPI. (2024). from IFPI. Use of this report, IFPI Global Music Report (2024 edition) is governed by the Global Music Report Terms of Use (gmr.ifpi.org/terms-of-use) and Global Music Report Content Usage Rules (gmr.ifpi.org/content-usage). Designed By Data Design.
Jakpat. (2024). Indonesia Mobile Entertainment & Social Media Trends 2024 Premium report.
Khutami, J. Q., Asmara, N. A. P., Auliana, L., Raharja, S. J., & Hakim, M. A. (2024). Pengaruh Artificial Intelligence Terhadap Customer Experience (Survey pada Pengguna Aplikasi Spotify di Jatinangor). Jurnal Ilmu Administrasi Bisnis, 13(2), 510–517. https://doi.org/10.14710/jiab.2024.42229
Mäntymäki, M., Islam, A. K. M. N., & Benbasat, I. (2020). What drives subscribing to premium in freemium services? A consumer value‐based view of differences between upgrading to and staying with premium. Information Systems Journal, 30(2), 295–333. https://doi.org/10.1111/isj.12262
Risanti, C., Suryanto, T. L. M., & Pratama, A. (2022). Analisis Faktor Keputusan Berlangganan pada Subscription Video on Demand Menggunakan Metode UTAUT2. Jutisi: Jurnal Ilmiah Teknik Informatika Dan Sistem Informasi, 11(3), 525–536. https://doi.org/10.35889/jutisi.v11i3.914
Saufi, M., Rofi’i, A., & Firdaus, D. R. (2023). The Analysis of User Intention to Subscribe Netflix Using UTAUT Framework. Journal of Information System, Technology and Engineering, 1(1), 16–20.
Schedl, M., Zamani, H., Chen, C.-W., Deldjoo, Y., & Elahi, M. (2018). Current challenges and visions in music recommender systems research. International Journal of Multimedia Information Retrieval, 7(2), 95–116. https://doi.org/10.1007/s13735-018-0154-2
Seifert, R., Denk, J., Clement, M., Kandziora, M., & Meyn, J. (2024). Conversion in music streaming services. Journal of Interactive Marketing, 59(2), 201–219.
Statista. (2023). Music streaming market revenue in Indonesia. Retrieved from https://www.statista.com/forecasts/1343452/Music-streaming-market-revenue-indonesia
Sousa, S. (2024). A Systematic Literature Review of User Trust in AI-Enabled Systems: An HCI Perspective. International Journal of Human–Computer Interaction. https://doi.org/10.1080/10447318.2022.2138826
Spotify. (2021). Shareholder Letter Q4 2020.
Syahraihan, A. M., Aras, M., Maahirah, D. B., Rizki, Z. A., & Lay, M. (2024). The Effect of Premium Service Music Streaming Towards Consumer Satisfaction of Z Generation in Jakarta. Jurnal Indonesia Sosial Teknologi, 5(5). https://doi.org/10.59141/jist.v5i5.1050
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
Venkatesh, V., Thong, J. Y. L., & 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.
Wongras, P., & Tanantong, T. (2023). An extended UTAUT model for analyzing users’ Acceptance factors for artificial Intelligence adoption in human resource recruitment: A case study of Thailand. https://doi.org/10.20944/preprints202311.1612.v1
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