Algorithmic Personalization, Free Will, and Psychological Agency among Generation Z

Authors

  • Lia Triastiani Magister of Psychology Paramadina University Jakarta, Indonesia
  • Nur Anisa Magister of Psychology Paramadina University Jakarta, Indonesia

DOI:

https://doi.org/10.55885/jprsp.v6i3.1096

Keywords:

Algorithmic, Personalization, Critical Reflection, Generation Z, Free Will, Psychological Agency

Abstract

The rapid growth of social media has transformed how Generation Z accesses and processes information through algorithmic personalization. While recommendation algorithms improve the efficiency and relevance of content delivery, they also raise concerns regarding their influence on autonomous decision-making and critical reflection. This study explores how algorithmic personalization shapes Generation Z’s information environment and examines its implications from philosophical and psychological perspectives. Using a qualitative literature review, twenty-five national and international publications were systematically analyzed through thematic analysis. The findings indicate that algorithmic personalization reinforces filter bubbles and echo chambers, limiting exposure to diverse viewpoints and reducing opportunities for reflective thinking. Philosophically, these conditions challenge the concept of free will by questioning the extent to which decisions remain autonomous when information is algorithmically curated. Psychologically, algorithmic environments may weaken psychological agency by influencing intentionality, self-regulation, and self-reflection. However, the study argues that algorithms do not eliminate human autonomy; rather, they reshape the conditions under which it is exercised. Consequently, algorithmic awareness, digital literacy, and critical reflection become essential for maintaining autonomous decision-making in digital environments. Integrating philosophical and psychological perspectives provides a more comprehensive understanding of human autonomy and agency in the era of algorithmic personalization.

References

Ahmmad, M., Shahzad, K., Iqbal, A., & Latif, M. (2025). Trap of social media algorithms: A systematic review of research on filter bubbles, echo chambers, and their impact on youth. Societies, 15(11), 301. https://doi.org/10.3390/soc15110301

Anisti, A., Sidarta, V., Imran, M., & Syatir, S. (2024). Tantangan Literasi Digital Generasi Z: Kajian Sistematic Lirature Review. Media Bahasa, Sastra, Dan Budaya Wahana, 30(2), 152-161. https://doi.org/10.33751/wahana.v30i2.11870

Areeb, Q. M., Nadeem, M., Sohail, S. S., Imam, R., Doctor, F., Himeur, Y., Hussain, A., & Amira, A. (2023). Filter bubbles in recommender systems: Fact or fallacy—A systematic review. WIREs Data Mining and Knowledge Discovery, 13(6), e1512. https://doi.org/10.1002/widm.1512

Bak-Coleman, J. B., Alfano, M., Barfuss, W., Bergstrom, C. T., Centeno, M. A., Couzin, I. D., Donges, J. F., Galesic, M., Gersick, A. S., Jacquet, J., Kao, A. B., Moran, R. E., Romanczuk, P., Rubenstein, D. I., Tombak, K. J., Van Bavel, J. J., & Weber, E. U. (2021). Stewardship of global collective behavior. Proceedings of the National Academy of Sciences, 118(27), e2025764118. https://doi.org/10.1073/pnas.2025764118

Bandura, A. (2001). Social cognitive theory: An agentic perspective. Annual Review of Psychology, 52, 1–26. https://doi.org/10.1146/annurev.psych.52.1.1

Bandura, A. (2006). Toward a psychology of human agency. Perspectives on Psychological Science, 1(2), 164–180. https://doi.org/10.1111/j.1745-6916.2006.00011.x

Boeker, M., & Urman, A. (2022, April). An empirical investigation of personalization factors on TikTok. In Proceedings of the ACM web conference 2022 (pp. 2298-2309). https://doi.org/10.1145/3485447.3512102

Bojic, L. (2024). AI alignment: Assessing the global impact of recommender systems. Futures, 160, 103383. https://doi.org/10.1016/j.futures.2024.103383

Bonicalzi, S., De Caro, M., & Giovanola, B. (2023). Artificial intelligence and autonomy: On the ethical dimension of recommender systems. Topoi, 42, 819–832. https://doi.org/10.1007/s11245-023-09922-5

Botes, M. (2023). Autonomy and the social dilemma of online manipulative behavior. AI and Ethics, 3, 315–323. https://doi.org/10.1007/s43681-022-00157-5

Cinelli, M., De Francisci Morales, G., Galeazzi, A., Quattrociocchi, W., & Starnini, M. (2021). The echo chamber effect on social media. Proceedings of the National Academy of Sciences, 118(9), e2023301118. https://doi.org/10.1073/pnas.2023301118

del Valle, J. I., & Lara, F. (2024). AI-powered recommender systems and the preservation of personal autonomy. AI & Society, 39, 2479–2491. https://doi.org/10.1007/s00146-023-01720-2

Dennett, D. C. (2015). Elbow room, new edition: The varieties of free will worth wanting. mit Press.

Eder, M., & Sehl, A. (2025). Being aware of algorithmic personalization? Insights from three European countries. Information, Communication & Society. https://doi.org/10.1080/1369118X.2025.2553015

Ernst, J. (2024). Understanding algorithmic recommendations: A qualitative study on children’s algorithm literacy in Switzerland. Information, Communication & Society, 27, 1945–1961. https://doi.org/10.1080/1369118X.2024.2382224

Fink, L., Newman, L., & Haran, U. (2024). Let me decide: Increasing user autonomy increases recommendation acceptance. Computers in Human Behavior, 156, 108244. https://doi.org/10.1016/j.chb.2024.108244

Grossetti, Q., du Mouza, C., Travers, N., & Constantin, C. (2021). Reducing the filter bubble effect on Twitter by considering communities for recommendations. International Journal of Web Information Systems, 17(6), 728–752. https://doi.org/10.1108/IJWIS-06-2021-0065

Guess, A. M., Malhotra, N., Pan, J., Barberá, P., Allcott, H., Brown, T., Crespo-Tenorio, A., Dimmery, D., Freelon, D., Gentzkow, M., González-Bailón, S., Kennedy, E., Kim, Y. M., Lazer, D., Moehler, D., Nyhan, B., Rivera, C. V., Settle, J., Thomas, D. R., ... Tucker, J. A. (2023). How do social media feed algorithms affect attitudes and behavior in an election campaign? Science, 381(6656), 398–404. https://doi.org/10.1126/science.abp9364

Hardiman, F. B. (2021). Manusia dalam prahara revolusi digital. Diskursus - Jurnal Filsafat dan Teologi STF Driyarkara, 17(2), 177–192. https://doi.org/10.36383/diskursus.v17i2.252

Hidayatulloh, T. (2025). From sanad to algorithm: Authority, rationality, and participatory interpretation of uṣūl al-dīn in cyberspace. Ilmu Ushuluddin, 12(2), 183–205. https://doi.org/10.15408/iu.vi.50233

Holm, S. (2023). Statistical evidence and algorithmic decision-making. Synthese, 201, 318. https://doi.org/10.1007/s11229-023-04246-8

Hua, X., Qiao, W., Yu, Y., & Zulkifli, M. F. (2026). Navigating user agency in the algorithmic era: A cross-cultural study of perceived autonomy and media consumption intentions in China and South Korea. Acta Psychologica, 268, 107271. https://doi.org/10.1016/j.actpsy.2026.107271

Joseph, J. (2025). The algorithmic self: How AI is reshaping human identity, introspection, and agency. Frontiers in Psychology, 16, 1645795. https://doi.org/10.3389/fpsyg.2025.1645795

Kaluža, J. (2021). Habitual generation of filter bubbles: Why is algorithmic personalisation problematic for the democratic public sphere? Javnost – The Public, 28(3), 267–283. https://doi.org/10.1080/13183222.2021.2003052

Kant, I. (2012). Groundwork of the metaphysics of morals (M. Gregor & J. Timmermann, Trans.; C. M. Korsgaard, Introduction). Cambridge University Press. (Original work published 1785)

Khambatta, P., Mariadassou, S., Morris, J., & Wheeler, S. C. (2023). Tailoring recommendation algorithms to ideal preferences makes users better off. Scientific Reports, 13, 9325. https://doi.org/10.1038/s41598-023-34192-x

Khulwa, C., Luthfia, A., & Aras, M. (2025). Algorithm awareness and user motivation as predictors of TikTok engagement among Generation Z in South Jakarta. Asian Journal for Public Opinion Research, 13(4), 400–422. https://doi.org/10.15206/ajpor.2025.13.4.400

Kirschner, J. (2024). The newest generational divide: Social media influence on digital natives. Visual Communication Quarterly, 31(3), 199–205. https://doi.org/10.1080/15551393.2024.2396791

Komara, D. A., & Widjaya, S. N. (2024). Memahami perilaku informasi Gen-Z dan strategi melawan disinformasi: Sebuah tinjauan literatur penggunaan media sosial. Jurnal Pustaka Ilmiah, 10(2).

Koskela, I.-M., & Paloniemi, R. (2022). Learning and agency for sustainability transformations: Building on Bandura’s theory of human agency. Environmental Education Research, 28(2), 164–178. https://doi.org/10.1080/13504622.2022.2102153

Ledwich, M., Zaitsev, A., & Laukemper, A. (2022). Radical bubbles on YouTube? Revisiting algorithmic extremism with personalised recommendations. First Monday, 27(12). https://doi.org/10.5210/fm.v27i12.12552

Lee, A. Y., Ellison, N. B., & Hancock, J. T. (2023). To use or be used? The role of agency in social media use and well-being. Frontiers in Computer Science, 5, 1123323. https://doi.org/10.3389/fcomp.2023.1123323

Ludwig, K., Grote, A., Iana, A., Alam, M., Paulheim, H., Sack, H., Weinhardt, C., & Müller, P. (2023). Divided by the algorithm? The (limited) effects of content- and sentiment-based news recommendation on affective, ideological, and perceived polarization. Social Media + Society, 9(2). https://doi.org/10.1177/08944393221149290

Madary, M. (2022). The illusion of agency in human–computer interaction. Neuroethics, 15, 16. https://doi.org/10.1007/s12152-022-09491-1

Marin, L. (2022). How to do things with information online: A conceptual framework for evaluating social networking platforms as epistemic environments. Philosophy & Technology, 35, 77. https://doi.org/10.1007/s13347-022-00569-5

Modgil, S., Singh, R. K., Gupta, S., & Dennehy, D. (2022). A confirmation bias view on social media induced polarisation during Covid-19. Information Systems Frontiers, 26, 417–441. https://doi.org/10.1007/s10796-021-10222-9

Niza, I. H., Mawarpury, M., Sulistyani, A., & Rachmatan, R. (2022). Critical thinking ability and information literacy in identifying fake news on social media users. Jurnal Psikologi Terapan dan Pendidikan, 4(1). https://doi.org/10.26555/jptp.v4i1.23357

Nurjanah, N., Abdulkarim, A., Komalasari, K., Bestari, P., & Suwandi, M. A. (2024). Critical literacy of young citizens in the digital era. Jurnal Civics: Media Kajian Kewarganegaraan, 21(2). https://doi.org/10.21831/jc.v21i2.70232

Nyhan, B., Settle, J., Thorson, E., Wojcieszak, M., Barberá, P., Chen, A., Allcott, H., Brown, T., Crespo-Tenorio, A., Dimmery, D., Freelon, D., Gentzkow, M., González-Bailón, S., Guess, A. M., Lazer, D., Malhotra, N., Moehler, D., Rivera, C. V., Settle, J., & Tucker, J. A. (2023). Like-minded sources on Facebook are prevalent but not polarizing. Nature, 620, 137–144. https://doi.org/10.1038/s41586-023-06297-w

Pabubung, M. R. (2021). Epistemologi kecerdasan buatan (AI) dan pentingnya ilmu etika dalam pendidikan interdisipliner. Jurnal Filsafat Indonesia, 4(2). https://doi.org/10.23887/jfi.v4i2.34734

Pabubung, M. R. (2023). Era kecerdasan buatan dan dampak terhadap martabat manusia dalam kajian etis. Jurnal Filsafat Indonesia, 6(1). https://doi.org/10.23887/jfi.v6i1.49293

Pariser, E. (2011). The filter bubble: What the Internet is hiding from you. Penguin Press.

Pérez-Verdugo, M., & Barandiaran, X. E. (2024). Personal autonomy and (digital) technology: An enactive sensorimotor framework. Philosophy & Technology, 36, 84. https://doi.org/10.1007/s13347-023-00683-y

Rahman, R., Mitrin, A., Azizah, P., Amelia, V., & Amalia, R. (2025). Pengaruh algoritma media sosial terhadap pola konsumsi informasi di kalangan Gen Z di Universitas Hang Tuah Pekanbaru. Pustaka Karya: Jurnal Ilmiah Ilmu Perpustakaan dan Informasi, 13(2), 351–358. https://doi.org/10.18592/pk.v13i2.18807

Robertson, R. E., Green, J., Ruck, D. J., Ognyanova, K., Wilson, C., & Lazer, D. (2023). Users choose to engage with more partisan news than they are exposed to on Google Search. Nature, 618(7964), 342–348. https://doi.org/10.1038/s41586-023-06078-5

Rydenfelt, H., Lehtiniemi, T., Haapoja, J., & Haapanen, L. (2025). Autonomy and algorithms: tracing the significance of content personalization. International Journal of Communication.

Sahebi, S., & Formosa, P. (2022). Social media and its negative impacts on autonomy. Philosophy & Technology, 35(3), 70. https://doi.org/10.1007/s13347-022-00567-7

Sari, A. P., Rizky, M. C., Aritonang, D. S., Sabiya, A., & Rajagukguk, D. S. (2025). Peran critical thinking sebagai tameng digital bagi Generasi Z. YUME: Journal of Management.

Spohr, D. (2017). Fake news and ideological polarization: Filter bubbles and selective exposure on social media. Business Information Review, 34(3), 150–160. https://doi.org/10.1177/0266382117722446

Susser, D., Roessler, B., & Nissenbaum, H. (2019). Technology, autonomy, and manipulation. Internet Policy Review, 8(2). https://doi.org/10.14763/2019.2.1410

Swart, J. (2021). Experiencing algorithms: How young people understand, feel about, and engage with algorithmic news selection on social media. Social Media + Society, 7(2). https://doi.org/10.1177/20563051211008828

Trattner, C., Jannach, D., Kwon, Y. M., Kaptan, S., & Salminen, J. (2022). Towards responsible media recommendation. AI and Ethics, 2, 49–56. https://doi.org/10.1007/s43681-021-00107-7

Vaassen, B. (2022). AI, opacity, and personal autonomy. Philosophy & Technology, 35, 88. https://doi.org/10.1007/s13347-022-00577-5

Yang, C., Xu, X., Nunes, B. P., & Siqueira, S. W. M. (2023). Bubbles bursting: Investigating and measuring the personalisation of social media searches. Telematics and Informatics, 82, 101999. https://doi.org/10.1016/j.tele.2023.101999

Downloads

Published

2026-09-16

How to Cite

Triastiani, L., & Anisa, N. (2026). Algorithmic Personalization, Free Will, and Psychological Agency among Generation Z. Journal of Public Representative and Society Provision, 6(3), 757-770. https://doi.org/10.55885/jprsp.v6i3.1096