The Impact of Dark Patterns versus Ethical Design in AI-Based Recommendation Systems A Field Study on E-Commerce Users

المؤلفون

  • Fathi A. Hamhoum University of Azzawia, Azzawia, Libya Faculty of Science,Department of Computer Science المؤلف
  • Haneen Nouri Al-Kanjari Al-Juaidi University of Azzawia, Azzawia, Libya Faculty of Science,Department of Computer Science المؤلف

DOI:

https://doi.org/10.65405/2wvb6e22

الكلمات المفتاحية:

Dark Patterns, Ethical Design, AI Recommendation Systems, Consumer Trust, Loyalty, E-commerce.

الملخص

This study examines the influence of dark patterns versus ethical design in AI-driven recommendation systems on consumer choice, trust, and loyalty in e-commerce environments. The study is motivated by the increasing adoption of AI recommendation systems and the growing ethical concerns associated with persuasive and manipulative interface practices. A descriptive analytical approach was adopted, and data were collected through an online questionnaire with 35 valid responses. The questionnaire measured four main constructs: dark patterns, ethical design, consumer trust, and loyalty. The descriptive results show that dark patterns achieved a moderate overall mean of 3.41, indicating that practices such as false scarcity messages and countdown timers can influence purchase decisions. Ethical design achieved a higher mean of 3.84, while consumer trust recorded 3.77 and loyalty achieved the highest mean of 4.13. Reliability analysis showed strong internal consistency, with an overall Cronbach’s alpha of 0.913. The findings suggest that while dark patterns may generate short-term behavioral influence, ethical design appears to be more sustainable in strengthening consumer trust and long-term loyalty. The study recommends that e-commerce platforms adopt transparent, explainable, and user-centered AI recommendation practices and reduce manipulative interface strategies that undermine consumer autonomy.

التنزيلات

تنزيل البيانات ليس متاحًا بعد.

المراجع

Adomavicius, G., & Tuzhilin, A. (2005). Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions. IEEE Transactions on Knowledge and Data Engineering, 17(6), 734–749.

Floridi, L., & Cowls, J. (2019). A Unified Framework of Five Principles for AI in Society. Harvard Data Science Review, 1(1).

Gray, C. M., Kou, Y., Battles, B., Hoggatt, J., & Toombs, A. L. (2018). The Dark (Patterns) Side of UX Design. Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, 1–14.

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. Proceedings of the ACM on Human-Computer Interaction, 3(CSCW), 1–32.

O’Neil, C. (2016). Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. Crown Publishing Group.

Alnnale, T. (2026). Predictive Governance in Digital Enterprises: An LSTM-Enhanced Deep Learning Framework for Economic Optimization of IT Incident Management Using Enriched Process Logs. Al-Farooq Journal of Sciences, 2(3), 86-113.

Ricci, F., Rokach, L., & Shapira, B. (Eds.). (2022). Recommender Systems Handbook (3rd ed.). Springer.

Shin, D. (2021). The Effects of Explainability and Causability on Perception, Trust, and Acceptance: Implications for Explainable AI. International Journal of Human–Computer Studies, 146, 102551.

Wachter, S., Mittelstadt, B., & Russell, C. (2017). Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR. Harvard Journal of Law & Technology, 31(2), 841–887.

التنزيلات

منشور

2026-06-21

كيفية الاقتباس

The Impact of Dark Patterns versus Ethical Design in AI-Based Recommendation Systems A Field Study on E-Commerce Users. (2026). مجلة العلوم الشاملة, 11(41), 1208-1218. https://doi.org/10.65405/2wvb6e22