Pengaruh Algoritma TikTok terhadap Pola Konsumsi Konten Generasi Z di Indonesia: Studi Analisis Perilaku dan Strategi Engagement
DOI:
https://doi.org/10.51903/q72r8m61Keywords:
Tiktok, Algorithm, Generation Z, Content Consumption Patterns, User EngagementAbstract
TikTok, one of the most popular platforms, has earned Generation Z's affection in Indonesia. Through personalization, the app's intelligent algorithms force its users to consume content and interact. Even though such algorithms contain elements that may influence user consumption and interaction, the research done locally on the subject is rather scarce. A major concern in this study is how the TikTok algorithm impacts content consumption and engagement strategies used by Generation Z in Indonesia. The study followed a quantitative survey method among purposively sampled 450 active TikTok users of Generation Z. The data collection involved an online Likert scale questionnaire, which was processed through multiple linear regression analysis. It was found that the TikTok algorithm does affect patterns of content consumption significantly (β = 0.512; R² = 0.262) and engagement strategies (β = 0.431; R² = 0.186). 78.4% of respondents used the For You page to discover various content within the entertainment category (67.3%) as a prime focus, followed by light education (45.8%) and those that show viral trends (42.7%). The study ends up entering the literature by trying to put together technical algorithm analysis with behavioral analysis of digital consumption in the case of Indonesia's generation Z, thereby enriching the literature in algorithmic media theory. While most users engage with the content by liking videos (85.1%), fewer users share videos (39.6%): the fact is that users mostly engaged in the study using only one interaction form. An example of these practical implications is how the platform developers, marketers, and policy-makers will set about optimizing retention strategy and creating engagement with millennial or young users.
References
Abderahman Rejeb et al. (2024). Foundations and knowledge clusters in TikTok (Douyin) research: evidence from bibliometric and topic modelling analyses. Multimedia Tools and Applications (Vol. 83). Springer US. https://doi.org/10.1007/s11042-023-16768-x
Angelica Goetzen et al. (2023). Likes and Fragments: Examining Perceptions of Time Spent on TikTok, 1–10. Retrieved from http://arxiv.org/abs/2303.02041
Anwar, M. S., Schoenebeck, G., & Dhillon, P. S. (2024). Filter Bubble or Homogenization? Disentangling the Long-Term Effects of Recommendations on User Consumption Patterns. WWW 2024 - Proceedings of the ACM Web Conference, 123–134. https://doi.org/10.1145/3589334.3645497
Boeker, M., & Urman, A. (2022). An Empirical Investigation of Personalization Factors on TikTok. WWW 2022 - Proceedings of the ACM Web Conference 2022 (Vol. 1). Association for Computing Machinery. https://doi.org/10.1145/3485447.3512102
Bojic, L. (2024). AI alignment: Assessing the global impact of recommender systems. Futures, 160(October 2023), 103383. https://doi.org/10.1016/j.futures.2024.103383
Chueca Del Cerro, C. (2024). The power of social networks and social media’s filter bubble in shaping polarisation: an agent-based model. Applied Network Science, 9(1). https://doi.org/10.1007/s41109-024-00679-3
Corsi, G. (2024). Evaluating Twitter’s algorithmic amplification of low-credibility content: an observational study. EPJ Data Science, 13(1). https://doi.org/10.1140/epjds/s13688-024-00456-3
Creative, I., Sultan, U. P., & Malim, T. (2024). A Bibliometric Perspective on Research of Personalized Recommendation Systems and the Filter Bubble Effect in 2024. Journal of Technology and Humanities, 2(5), 29–37. https://doi.org/10.53797/jthkks.v5i2.4.2024
Dwivedi, Y. K., Jeyaraj, A., Hughes, L., Davies, G. H., Ahuja, M., Albashrawi, M. A., … Walton, P. (2024). “Real impact”: Challenges and opportunities in bridging the gap between research and practice – Making a difference in industry, policy, and society. International Journal of Information Management, 78(xxxx). https://doi.org/10.1016/j.ijinfomgt.2023.102750
Fu, M., Fraser, B., & Arcodia, C. (2024). Digital natives on the rise: A systematic literature review on Generation Z’s engagement with RAISA technologies in hospitality services. International Journal of Hospitality Management, 122(August), 103885. https://doi.org/10.1016/j.ijhm.2024.103885
Gagrčin, E., Naab, T. K., & Grub, M. F. (2024). Algorithmic media use and algorithm literacy: An integrative literature review. New Media and Society. https://doi.org/10.1177/14614448241291137
Gerbaudo, P. (2024). TikTok and the algorithmic transformation of social media publics: From social networks to social interest clusters. New Media and Society, 1–25. https://doi.org/10.1177/14614448241304106
Godard, R., & Holtzman, S. (2024). Are active and passive social media use related to mental health, well-being, and social support outcomes? A meta-analysis of 141 studies. Journal of Computer-Mediated Communication, 29(1). https://doi.org/10.1093/jcmc/zmad055
Hazrati, N., & Ricci, F. (2024). Choice models and recommender systems' effects on users’ choices. User Modeling and User-Adapted Interaction, 34(1), 109–145. https://doi.org/10.1007/s11257-023-09366-x
He, J., Liang, X., & Xue, J. (2024). Unraveling the Influential Mechanisms of Smart Interactions on Stickiness Intention: A Privacy Calculus Perspective. Journal of Theoretical and Applied Electronic Commerce Research, 19(4), 2582–2604. https://doi.org/10.3390/jtaer19040124
Jenny Ratna Suminar et al. (2024). Predicting pro-environmental behavior among Generation Z in Indonesia: the role of family norms and exposure to social media information. Frontiers in Communication, 9(December), 1–11. https://doi.org/10.3389/fcomm.2024.1461609
Jung, H., Dai, W., & Albarracín, D. (2024). How Social Media Algorithms Shape Offline Civic Participation: A Framework of Social-Psychological Processes. Perspectives on Psychological Science, 19(5), 767–780. https://doi.org/10.1177/17456916231198471
Karan Vombatkere et al. (2024a). TikTok and the Art of Personalization: Investigating Exploration and Exploitation on Social Media Feeds. WWW 2024 - Proceedings of the ACM Web Conference, 3789–3797. https://doi.org/10.1145/3589334.3645600
Karan Vombatkere et al. (2024b). TikTok and the Art of Personalization: Investigating Exploration and Exploitation on Social Media Feeds. WWW 2024 - Proceedings of the ACM Web Conference (Vol. 1). Association for Computing Machinery. https://doi.org/10.1145/3589334.3645600
Leonov Yaroslav. (2023). Analysis of the Influence of Psychological Factors on Consumer Behavior and the Decision-making Process. Economic Affairs (New Delhi), 68(3), 1643–1651. https://doi.org/10.46852/0424-2513.3.2023.29
Lewandowsky, S., Robertson, R. E., & DiResta, R. (2024). Challenges in Understanding Human-Algorithm Entanglement During Online Information Consumption. Perspectives on Psychological Science, 19(5), 758–766. https://doi.org/10.1177/17456916231180809
Lim, C., & Kim, S. (2024). Examining factors influencing the user’s loyalty to the algorithmic news recommendation service. Humanities and Social Sciences Communications, 11(1), 1–15. https://doi.org/10.1057/s41599-023-02516-x
Liu, Y., Xu, Y., & Zhou, S. (2024). Based Personalized Recommendation Systems : Behavior Data-Driven UI Design, 0, 42–46. https://doi.org/10.54254/2755-2721/112/2024.17905
Metzler, H., & Garcia, D. (2024). Social Drivers and Algorithmic Mechanisms on Digital Media. Perspectives on Psychological Science, 19(5), 735–748. https://doi.org/10.1177/17456916231185057
Michelle Faverio & Olivia Sidoti. (2024). Michelle Faverio and Olivia Sidoti (December).
Oeldorf-Hirsch, A., & Neubaum, G. (2023). Attitudinal and behavioral correlates of algorithmic awareness among German and U.S. social media users. Journal of Computer-Mediated Communication, 28(5). https://doi.org/10.1093/jcmc/zmad035
Peters, U., & Carman, M. (2024). Cultural Bias in Explainable AI Research: A Systematic Analysis. Journal of Artificial Intelligence Research, 79, 971–1000. https://doi.org/10.1613/jair.1.14888
Peukert, C., Sen, A., & Claussen, J. (2024). The Editor and the Algorithm: Recommendation Technology in Online News. Management Science, 70(9), 5816–5831. https://doi.org/10.1287/mnsc.2023.4954
Qiaochu Wang et al. (2023). Algorithmic Transparency with Strategic Users. Management Science, 69(4), 2297–2317. https://doi.org/10.1287/mnsc.2022.4475
Reynolds, C. J., & Hallinan, B. (2024). User-generated accountability: Public participation in algorithmic governance on YouTube. New Media and Society, 26(9), 5107–5129. https://doi.org/10.1177/14614448241251791
Rungruangjit, W., Chankoson, T., & Charoenpornpanichkul, K. (2023). Understanding Different Types of Followers’ Engagement and the Transformation of Millennial Followers into Cosmetic Brand Evangelists. Behavioral Sciences, 13(3). https://doi.org/10.3390/bs13030270
Sarah H. Cen et al. (2024). Measuring Strategization in Recommendation: Users Adapt Their Behavior to Shape Future Content, 203–204. https://doi.org/10.1145/3670865.3673634
Teh, W. L., Abdin, E., P.V A., Siva Kumar, F. D., Royston, K., Wang, P., … Subramaniam, M. (2023). Measuring social desirability bias in a multi-ethnic cohort sample: its relationship with self-reported physical activity, dietary habits, and factor structure. BMC Public Health, 23(1), 1–10. https://doi.org/10.1186/s12889-023-15309-3
Tim Verbeij et al. (2022). Experience sampling self-reports of social media use have comparable predictive validity to digital trace measures. Scientific Reports, 12(1), 1–11. https://doi.org/10.1038/s41598-022-11510-3
We Are Social. (2024). Digital 2024: Indonesia Overview Report, 136. Retrieved from https://n9.cl/5udw2
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