Dark Patterns Reconsidered: A Cross-Taxonomic and Conceptual Mapping for Ethical Interface Design

Authors

  • Onni Meirezaldi Universitas Brawijaya, Indonesia

DOI:

https://doi.org/10.35917/tb.v26i1.586

Keywords:

dark patterns, deceptive design, conceptual definition, ethical design, consumer protection

Abstract

Dark patterns, manipulative interface strategies that steer users toward actions contrary to their interests, have become ingrained in commerce, social media, and data‑collection flows. Research and regulation still lack a shared vocabulary for identifying and addressing them. This study aims to close that gap by proposing a comprehensive conceptual definition and a reconciled taxonomic map that clarifies how dark patterns operate and why they negatively impact users. Based on a directed literature review of 54 peer‑reviewed sources indexed in Scopus, the analysis identifies four foundational elements: manipulative intent, information asymmetry, constrained choice, and exploitation of cognitive bias. It combines them into a single definition that unites legal, psychological, and HCI perspectives. It then cross‑compares the leading taxonomies of Brignull et al., Grey et al., Mathur et al., and Zagal et al., demonstrating agreement on five mechanism families, namely Obstruction, Sneaking, Interface Interference, Forced Action, Nagging, while highlighting differing focuses on functional harms. To address this issue, the article introduces a two‑dimensional grid that overlays those mechanisms with four functional areas: Finance, Privacy, Time Capture, and Psychological Pressure, creating a flexible framework capable of classifying both traditional and emerging dark‑pattern strategies. The resulting model offers scholars a stable analytical framework for theory building, supplies regulators with enforceable categories for consumer protection, and equips designers with a diagnostic tool for auditing interface ethics. The study establishes a conceptual foundation for future empirical measurement, automated detection, and evidence‑based policy to foster a more transparent and autonomy‑respecting digital ecosystem by unifying disparate definitions and rationalizing taxonomies.

Downloads

Download data is not yet available.

References

Akbar, N. F., & Nurmahdi, D. A. (2019). Analysis of Perceived Usefulness, Perceived Ease of Use and Service Quality on User Satisfaction in Using Snaapp Communication Application in Ignatius Slamet Riyadi Karawang Elementary School. Saudi Journal of Business and Management Studies, 4(11), 849-855. https://doi.org/10.36348/sjbms.2019.v04i11.005

Baxter, K. A., Sachdeva, N., & Baker, S. (2025). The Application of Cognitive Load Theory to the Design of Health and Behavior Change Programs: Principles and Recommendations. Health Education & Behavior, 10901981251327185. https://doi.org/10.1177/10901981251327185

Bessant, C., Ong, L. L., Cook, L. A., Hoy, M. G., Pereira, B., Fox, A., Nottingham, E., Steinberg, S., & Gan, P. (2023, Oct 18-21). Exploring Parents' Knowledge of Dark Design and Its Impact on Children's Digital Well-Being. AoIR2023: The 24th Annual Conference of the Association of Internet Researchers, Philadelphia, PA, USA. https://doi.org/10.5210/spir.v2023i0.13395

Brignull, H., Leiser, M., Santos, C., & Doshi, K. (2023). Deceptive patterns – user interfaces designed to trick you. Retrieved April 25 from https://www.deceptive.design/

Brunk, K. H. (2010). Exploring origins of ethical company/brand perceptions — A consumer perspective of corporate ethics. Journal of Business Research, 63(3), 255-262. https://doi.org/https://doi.org/10.1016/j.jbusres.2009.03.011

Di Porto, F., & Egberts, A. (2023). The collective welfare dimension of dark patterns regulation. Eur Law J, 29(1-2), 114-141. https://doi.org/10.1111/eulj.12478

Esposito, F., & Ferreira, T. M. C. (2024). Addictive Design as an Unfair Commercial Practice: The Case of Hyper-Engaging Dark Patterns. European Journal of Risk Regulation, 15(4), 999-1016. https://doi.org/10.1017/err.2024.8

Gray, C. M., Santos, C., Bielova, N., Toth, M., & Clifford, D. (2021). Dark Patterns and the Legal Requirements of Consent Banners: An Interaction Criticism Perspective. Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, Yokohama, Japan. https://doi.org/10.1145/3411764.3445779

Hidaka, S., Kobuki, S., Watanabe, M., & Seaborn, K. (2023). Linguistic Dead-Ends and Alphabet Soup: Finding Dark Patterns in Japanese Apps. Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, Hamburg, Germany. https://doi.org/10.1145/3544548.3580942

Hilton, M. (2023). Dark Patterns and User Mental Health: Identifying Theoretical Impacts of Deceptive Design on Vulnerable Demographics. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 67(1), 2124-2127. https://doi.org/10.1177/21695067231199684

Khair, M. S. (2025). Analyzing Factors Influencing Subscription Decisions for Exclusive Content on Instagram. International Student Conference on Business, Education, Economics, Accounting, and Management (ISC-BEAM), 3(1), 234-248. https://doi.org/10.21009/ISC-BEAM.013.15

Kirkman, D., Vaniea, K., & Woods, D. W. (2023, 3-7 July 2023). DarkDialogs: Automated detection of 10 dark patterns on cookie dialogs. 2023 IEEE 8th European Symposium on Security and Privacy (EuroS&P), Delft, Netherlands. https://doi.org/10.1109/EuroSP57164.2023.00055

Kitkowska, A., Högberg, J., & Wästlund, E. (2022, January, 3-7). Barriers to a well-functioning digital market: Exploring dark patterns and how to overcome them. 55th Hawaii International Conference on System Sciences, Manoa, University of Hawai'i.

Kollmer, T., & Eckhardt, A. (2023). Dark Patterns. Business & Information Systems Engineering, 65(2), 201-208. https://doi.org/10.1007/s12599-022-00783-7

Kompaniets, A., & Chemerys, H. (2019). Generalization of the experience of using research on psychology of behavior for designing UX design software products. Ukrainian Journal of Educational Studies and Information Technology, 7(3), 1-10. https://doi.org/10.32919/uesit.2019.03.01

Krauß, V., Saeghe, P., Boden, A., Khamis, M., McGill, M., Gugenheimer, J., & Nebeling, M. (2024). What Makes XR Dark? Examining Emerging Dark Patterns in Augmented and Virtual Reality through Expert Co-Design. ACM Trans. Comput.-Hum. Interact., 31(3), Article 32. https://doi.org/10.1145/3660340

Krisam, C., Dietmann, H., Volkamer, M., & Kulyk, O. (2021). Dark Patterns in the Wild: Review of Cookie Disclaimer Designs on Top 500 German Websites. Proceedings of the 2021 European Symposium on Usable Security, Karlsruhe, Germany. https://doi.org/10.1145/3481357.3481516

Lachheb, A., Abramenka-Lachheb, V., Moore, S., & Gray, C. (2023). The role of design ethics in maintaining students' privacy: A call to action to learning designers in higher education. British Journal of Educational Technology, 54(6), 1653-1670. https://doi.org/https://doi.org/10.1111/bjet.13382

Leiser, M. R., Kosta, E., Leenes, R., & Kamara, I. (2022). Chapter 10: Dark patterns: The case for regulatory pluralism between the European Unions consumer and data protection regimes. In E. Kosta, R. Leenes, & I. Kamara (Eds.), Research Handbook on EU Data Protection Law (pp. 240-269). Edward Elgar Publishing. https://doi.org/10.4337/9781800371682.00019

Lu, Y., Zhang, C., Yang, Y., Yao, Y., & Li, T. J.-J. (2024). From Awareness to Action: Exploring End-User Empowerment Interventions for Dark Patterns in UX. Proc. ACM Hum.-Comput. Interact., 8(CSCW1), Article 59. https://doi.org/10.1145/3637336

Luguri, J., & Strahilevitz, L. J. (2021). Shining a Light on Dark Patterns. Journal of Legal Analysis, 13(1), 43-109. https://doi.org/10.1093/jla/laaa006

Machuletz, D., & Böhme, R. (2020). Multiple purposes, multiple problems: A user study of consent dialogs after GDPR. Privacy Enhancing Technologies, https://doi.org/10.2478/popets-2020-0037

Mathur, A., Acar, G., Friedman, M. J., Lucherini, E., Mayer, J., Chetty, M., & Narayanan, A. (2019). Dark Patterns at Scale: Findings from a Crawl of 11K Shopping Websites. Proc. ACM Hum.-Comput. Interact., 3(CSCW), Article 81. https://doi.org/10.1145/3359183

Mathur, A., Kshirsagar, M., & Mayer, J. (2021). What Makes a Dark Pattern... Dark? Design Attributes, Normative Considerations, and Measurement Methods. Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, Yokohama, Japan. https://doi.org/10.1145/3411764.3445610

Naslund, J. A., Aschbrenner, K. A., Kim, S. J., McHugo, G. J., Unützer, J., Bartels, S. J., & Marsch, L. A. (2017). Health behavior models for informing digital technology interventions for individuals with mental illness. Psychiatr Rehabil J, 40(3), 325-335. https://doi.org/10.1037/prj0000246

Nazarov, D., & Baimukhambetov, Y. (2022). Clustering of Dark Patterns in the User Interfaces of Websites and Online Trading Portals (E-Commerce). Mathematics, 10(18), 3219. https://doi.org/10.3390/math10183219

Nouwens, M., Liccardi, I., Veale, M., Karger, D., & Kagal, L. (2020). Dark Patterns after the GDPR: Scraping Consent Pop-ups and Demonstrating their Influence. Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, Honolulu, HI, USA. https://doi.org/10.1145/3313831.3376321

Radesky, J., Hiniker, A., McLaren, C., Akgun, E., Schaller, A., Weeks, H. M., Campbell, S., & Gearhardt, A. N. (2022). Prevalence and Characteristics of Manipulative Design in Mobile Applications Used by Children. JAMA Network Open, 5(6), e2217641-e2217641. https://doi.org/10.1001/jamanetworkopen.2022.17641

Sin, R., Harris, T., Nilsson, S., & Beck, T. (2025). Dark patterns in online shopping: do they work and can nudges help mitigate impulse buying? Behavioural Public Policy, 9(1), 61-87. https://doi.org/10.1017/bpp.2022.11

Singh, V., Vishvakarma, N. K., & Kumar, V. (2024). Unmasking user vulnerability: investigating the barriers to overcoming dark patterns in e-commerce using TISM and MICMAC analysis. Journal of Information, Communication and Ethics in Society, 22(2), 275-292. https://doi.org/10.1108/JICES-10-2023-0127

Singh, V., Vishvakarma, N. K., & Kumar, V. (2025). Profit over principles: unveiling the motivating factors behind dark patterns in e-commerce through the lens of agency theory. Journal of Enterprise Information Management, 38(3), 821-848. https://doi.org/10.1108/JEIM-08-2024-0409

van Nimwegen, C., Bergman, K., & Akdag, A. (2022, July, 11-13). Shedding light on assessing Dark Patterns: Introducing the System Darkness Scale (SDS). 35th International BCS Human-Computer Interaction Conference (HCI2022), Keele, Staffordshire. https://doi.org/10.14236/ewic/HCI2022.7

Voigt, C., Schlögl, S., & Groth, A. (2021, 07/24-29). Dark Patterns in Online Shopping: of Sneaky Tricks, Perceived Annoyance and Respective Brand Trust. HCI in Business, Government and Organizations, Cham. https://doi.org/10.1007/978-3-030-77750-0_10

White, C. (2015). The impact of motivation on customer satisfaction formation: a self-determination perspective. European Journal of Marketing, 49(11/12), 1923-1940. https://doi.org/10.1108/EJM-08-2014-0501

Zagal, J. P., Björk, S., & Lewis, C. (2013, May 14-17). Dark patterns in the design of games. International Conference on Foundations of Digital Games, Chania, Crete, Greece.

Zhang, K., & Gao, Y. (2022, 2022/11/19). Analysis of Research Hotspots of Cognitive Load from the Perspective of Product Design Based on Measurement System, Cause Analysis, and Regulation Strategy. Proceedings of the 2022 International Conference on Science Education and Art Appreciation (SEAA 2022), https://doi.org/10.2991/978-2-494069-05-3_80

Downloads

Published

2025-07-28

How to Cite

Meirezaldi, O. (2025). Dark Patterns Reconsidered: A Cross-Taxonomic and Conceptual Mapping for Ethical Interface Design. Telaah Bisnis, 26(1), 11–25. https://doi.org/10.35917/tb.v26i1.586

Issue

Section

Articles

Citation Check

Similar Articles

1 2 3 > >> 

You may also start an advanced similarity search for this article.