The Illusion of Control: User Perceptions of Privacy and Data Agency on Social Media Platforms After GDPR
Downloads
Background. The General Data Protection Regulation (GDPR), implemented in 2018, aimed to enhance user privacy and give individuals more control over their personal data on social media platforms. However, despite these regulatory changes, concerns about data privacy and user agency persist. This study explores how users perceive their privacy and data control on social media platforms post-GDPR, focusing on whether the regulation has led to a genuine increase in user agency or if it remains an illusion.
Purpose. The research aims to assess the gap between perceived and actual control over personal data, as well as the factors that influence these perceptions.
Method. Using a mixed-methods approach, the study combines surveys and in-depth interviews with social media users across various platforms.
Results. The findings reveal that while users feel more empowered by the GDPR’s transparency requirements, many still feel limited in their ability to effectively control their data. Privacy settings remain confusing, and the complexity of consent mechanisms leaves users uncertain about the full extent of their data usage.
Conclusion. The study concludes that, while GDPR has improved transparency, it has not fully addressed the underlying issues of user agency, suggesting the need for further regulatory refinement to provide users with meaningful control over their data.
Algunmeeyn, A., & Mrayyan, M. T. (2025). A Cross-Sectional Online Study of the Use of Artificial Intelligence in Nursing Research as Perceived by Nursing Students. Sage Open Nursing, 11, 23779608251330866. https://doi.org/10.1177/23779608251330866
Alhomoud, F. K. (2025). The use of modern e-health services including telemedicine and telepharmacy for remote patient care in Saudi Arabia. Journal of Family and Community Medicine, 32(1), 51–58. https://doi.org/10.4103/jfcm.jfcm_246_24
Ali, M., Arunasalam, A., & Farrukh, H. (2025). Understanding Users’ Security and Privacy Concerns and Attitudes Towards Conversational AI Platforms. 2025 IEEE Symposium on Security and Privacy (SP), 298–316. https://doi.org/10.1109/SP61157.2025.00241
Bauerová, R., & Halaška, M. (2025). Unlocking the metaverse: Determinants of voluntary adoption in e-commerce. Sustainable Futures, 9, 100436. https://doi.org/10.1016/j.sftr.2025.100436
Bhatnagr, P. (2026). Virtual influencers on Instagram: A text mining study of consumer sentiments in China. Journal of Modelling in Management, 21(1), 392–418. https://doi.org/10.1108/JM2-01-2025-0024
Choudhury, A., Shahsavar, Y., & Shamszare, H. (2025). User Intent to Use DeepSeek for Health Care Purposes and Their Trust in the Large Language Model: Multinational Survey Study. JMIR Human Factors, 12, e72867–e72867. https://doi.org/10.2196/72867
Churruca, K., Foo, D., Turner, A., Crameri, E., Saba, M., Spanos, S., Vickers, M., Braithwaite, J., & Ellis, L. A. (2025). Users’ Perspectives of Direct-to-Consumer Telemedicine Services: Survey Study. JMIR Formative Research, 9, e68619–e68619. https://doi.org/10.2196/68619
Ding, L., Antonucci, G., & Venditti, M. (2025). Unveiling user responses to AI-powered personalised recommendations: A qualitative study of consumer engagement dynamics on Douyin. Qualitative Market Research: An International Journal, 28(2), 234–255. https://doi.org/10.1108/QMR-11-2023-0151
Ellis, J. R., Dellavalle, N. S., Hamer, M. K., Akerson, M., Andazola, M., Moore, A. A., Campbell, E. G., & DeCamp, M. (2025). The Halo Effect: Perceptions of Information Privacy Among Healthcare Chatbot Users. Journal of the American Geriatrics Society, 73(5), 1472–1483. https://doi.org/10.1111/jgs.19393
Fares, O. H. (2025). “That is scary!”: Consumer perceptions and discourses on ChatGPT. Qualitative Market Research: An International Journal, 28(3), 452–473. https://doi.org/10.1108/QMR-07-2024-0122
Hettler, F. M., Schumacher, J.-P., Anton, E., Eybey, B., & Teuteberg, F. (2025). Understanding the user perception of digital nudging in platform interface design. Electronic Commerce Research, 25(3), 2097–2134. https://doi.org/10.1007/s10660-024-09825-6
Howell, B. (2025). WEIRD? Institutions and consumers’ perceptions of artificial intelligence in 31 countries. AI & SOCIETY, 40(6), 4409–4431. https://doi.org/10.1007/s00146-025-02217-w
Huang, Y., & Liu, L. (2025). The impact of algorithm awareness on the acceptance of personalized social media content recommendation based on the technology acceptance model. Acta Psychologica, 259, 105383. https://doi.org/10.1016/j.actpsy.2025.105383
Huynh, M.-T. (2024). Using generative AI as decision-support tools: Unraveling users’ trust and AI appreciation. Journal of Decision Systems, 1–32. https://doi.org/10.1080/12460125.2024.2428166
Jin, E., & Eastin, M. (2025). Towards more trusted virtual physicians: The combinative effects of healthcare chatbot design cues and threat perception on health information trust. Behaviour & Information Technology, 44(4), 829–842. https://doi.org/10.1080/0144929X.2024.2347951
Kaufman, E., & Hoffner, Y. (2025). Smart home and spaces with multiple stakeholders: Automation, conflicts, security and recommender systems. Discover Internet of Things, 5(1), 55. https://doi.org/10.1007/s43926-025-00136-2
Liang, S., & Shi, C. (2025). Understanding the role of privacy issues in AIoT device adoption within smart homes: An integrated model of privacy calculus and technology acceptance. Aslib Journal of Information Management. https://doi.org/10.1108/AJIM-11-2024-0895
Lim, J. S., Shin, D., Lee, C., Kim, J., & Zhang, J. (2025). The Role of User Empowerment, AI Hallucination, and Privacy Concerns in Continued Use and Premium Subscription Intentions: An Extended Technology Acceptance Model for Generative AI. Journal of Broadcasting & Electronic Media, 69(3), 183–199. https://doi.org/10.1080/08838151.2025.2487679
Liu, Y., & Du, Y. (2025). The Effect of Generative AI Ethics on Users’ Continuous Usage Intentions: A PLS-SEM and fsQCA Approach. International Journal of Human–Computer Interaction, 41(20), 12831–12842. https://doi.org/10.1080/10447318.2025.2465861
Lv, X., Li, J., & Wang, Q. (2025). The Dark Side of Recommendation Algorithms in Chinese Mass Short Video Apps: Effect of Perceived Over-Recommendation on Users’ Cognitive Dissonance and Discontinuance Intention. International Journal of Human–Computer Interaction, 41(11), 6701–6715. https://doi.org/10.1080/10447318.2024.2383038
Oguafor, I. V., & Nevzat, R. (2025). “We are captives to digital media surveillance” Netizens awareness and perception of social media surveillance. Information Development, 41(2), 465–478. https://doi.org/10.1177/02666669231171641
Qi, Y., Mohamad, E., Azlan, A. A., & Zhang, C. (2025). Utilization of artificial intelligence in clinical practice: A systematic review of China’s experiences. DIGITAL HEALTH, 11, 20552076251343752. https://doi.org/10.1177/20552076251343752
Ryan, K., Hogg, J., Kasun, M., & Kim, J. P. (2025). Users’ Perceptions and Trust in AI in Direct-to-Consumer mHealth: Qualitative Interview Study. JMIR mHealth and uHealth, 13, e64715. https://doi.org/10.2196/64715
Shabankareh, M., Khamoushi Sahne, S. S., Nazarian, A., & Foroudi, P. (2025). The impact of AI perceived transparency on trust in AI recommendations in healthcare applications. Asia-Pacific Journal of Business Administration. https://doi.org/10.1108/APJBA-12-2024-0690
Shakya, P., Deb, K. S., Ganesh, R., Datta, A., Verma, R., & Chadda, R. (2025). Telepsychiatry in post-pandemic India: A mixed methods exploration of patient perspectives and preferences toward telemedicine versus in-person consultations. General Hospital Psychiatry, 93, 89–99. https://doi.org/10.1016/j.genhosppsych.2025.01.005
Shi, J., Lin, C., Wang, H., Jung, S., Ta, N., Liu, R., Gao, Y., & Cao, H. (2025). Sociable robots or focused speakers? Transforming customer experience with communication style and embodiment type in smart home devices adoption. Behaviour & Information Technology, 44(14), 3559–3574. https://doi.org/10.1080/0144929X.2025.2485401
Sifaoui, A., & Segijn, C. M. (2025). Surveillance in the Advertising Landscape: Application of the DEAL Framework to Investigate Online Behavioral Advertising, Surveillance, and Users’ Responses on Instagram. Journal of Current Issues & Research in Advertising, 46(4), 524–538. https://doi.org/10.1080/10641734.2024.2419602
Sørensen, L., Sagen Johannesen, D. T., Melkas, H., & Johnsen, H. M. (2025). User Acceptance of a Home Robotic Assistant for Individuals With Physical Disabilities: Explorative Qualitative Study. JMIR Rehabilitation and Assistive Technologies, 12, e63641. https://doi.org/10.2196/63641
Vaterlaus, J. M., & Winter, M. (2025). TikTok: An exploratory study of young adults’ uses and gratifications. The Social Science Journal, 62(3), 613–632. https://doi.org/10.1080/03623319.2021.1969882
Xin, H., Liu, F., & Wei, Z. (2026). What decision-making process do mHealth users go through when faced with privacy disclosure behaviors? A dual trade-off perspective. Aslib Journal of Information Management, 78(1), 142–166. https://doi.org/10.1108/AJIM-01-2024-0051
Yang, Z., Hu, D., & Chen, X. (2025). The role of omnichannel integration and digital value in building brand trust: A customer psychological perception perspective. Internet Research, 35(3), 1029–1064. https://doi.org/10.1108/INTR-06-2023-0464
Zhang, J., Lu, C., Yi, H., & Wu, C. (2025). User-perceptional privacy protection in NILM: A differential privacy approach. Applied Energy, 382, 125233. https://doi.org/10.1016/j.apenergy.2024.125233
Copyright (c) 2025 Kabelo Dube, Kgomotso Seretse, Keaolwe Matlhomola
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
