Systematic Literature Review: Business Ethics in the Use of Recommendation Algorithms in the Digital Era

Authors

  • Nuriya Fadilah Universitas Trunojoyo
  • Itaul Masarroh Universitas Trunojoyo
  • Iriani Ismail Universitas Trunojoyo

DOI:

https://doi.org/10.55606/ijemr.v5i1.606

Keywords:

Business Ethics, Digital, E-Commerce, SLR

Abstract

This study examines how recommendation algorithms, powered by big data and artificial intelligence, drive the growth of digital businesses by personalizing user experiences. Through a review of 21 studies, it identifies key ethical challenges such as algorithmic bias, privacy breaches, lack of transparency, and behavioral manipulation through dark patterns. To mitigate these issues, the literature emphasizes the importance of implementing the Fairness, Accountability, and Transparency (FAT) framework using explainable AI, user data control mechanisms, regular algorithm audits, and compliance with legal frameworks such as the GDPR. The study further highlights the need for deeper research on data governance, integration between GDPR and Indonesia’s Personal Data Protection Law (UU PDP), and more interdisciplinary and longitudinal studies, particularly in Southeast Asia. These findings underscore the necessity of maintaining a balance between technological innovation and ethical accountability to foster a sustainable and trustworthy digital ecosystem that supports both user protection and business growth.

References

bdullah, M. S. (2025). Examining the effect of AI-powered personalization on customer loyalty: A meta-analysis of e-commerce studies. Review of Applied Science and Technology, 04(02), 309–338. https://doi.org/10.63125/780spe97

Adomavicius, G., & Tuzhilin, A. (2013). Do recommender systems manipulate consumer preferences? INFORMS Information Systems Research, 24(4), 863–888. https://doi.org/10.1287/isre.2013.0497

Ahmad, B., Zaman, S. U., & Alam, S. H. (2025). Role of AI-based marketing activities in shaping brand experience, preference, and loyalty. Qlantic Journal of Social Sciences and Humanities, 6(1), 237–253. https://doi.org/10.55737/qjssh.vi-i.25320

Al-haimi, B., & Chuanjie, Z. (2025). Artificial intelligence in cross-border e-commerce: From value chain optimization to compliance-as-a-service. https://doi.org/10.2139/ssrn.5412965

Anselmo, G., Mannaioli, G., & Sardo, A. (2025). Ensuring fairness in the digital marketplace: A communicative and cognitive analysis of deceitful influencer marketing within regulatory frameworks and benchmarks. International Journal for the Semiotics of Law - Revue Internationale de Sémiotique Juridique. https://doi.org/10.1007/s11196-025-10290-z

Azzahra, P. Latiffa, Yusmaneli, & Fathurrahmad. (2023). Analysis of the use of mobile-based Aceh culinary menu applications to improve customer experience in the culinary industry. Journal Mobile Technologies (JMS), 1(2), 97–110. https://doi.org/10.59431/jms.v1i2.295

Bahiroh, E. (2025). Business ethics in the digital age: Building trust in online transactions. Indonesian Journal of Interdisciplinary Research in Science and Technology, 2(12), 1787–1794.

Burke, R. (2011). Recommender systems: An overview. AI Magazine, 32(3), 13–18. https://doi.org/10.1609/aimag.v32i3.2361

Daqar, M. A. M. A., & Smoudy, A. K. A. (2019). The role of artificial intelligence on enhancing customer experience. International Review of Management and Marketing, 9(4), 22–31. https://doi.org/10.32479/irmm.8166

de Marcellis-Warin, N., Marty, F., Thelisson, E., & Warin, T. (2022). Artificial intelligence and consumer manipulations: From consumer’s counter algorithms to firm’s self-regulation tools. AI and Ethics, 2(2), 259–268. https://doi.org/10.1007/s43681-022-00149-5

Ebrahim, T. Y., Ashely, D., Loza de Siles, E., Desai, D., Raymond, A., Hiller, J., Houser, K. A., Bagby, J., Porcaro, K., Elkin-Koren, N., Iannarone, N., Lingwall, J., Webrach, K., & Salem, J. (2021). Algorithms in business, merchant-consumer interactions, & regulation. West Virginia Law Review, 123.

Esmaeilzadeh, P. (2020). Use of AI-based tools for healthcare purposes: A survey study from consumers’ perspectives. BMC Medical Informatics and Decision Making, 20(1). https://doi.org/10.1186/s12911-020-01191-1

Fabbri, M. (2023). Social influence for societal interest: A pro-ethical framework for improving human decision making through multi-stakeholder recommender systems. AI and Society, 38(2), 995–1002. https://doi.org/10.1007/s00146-022-01467-2

Falah, A. S., & Dewi, L. S. (2025). Leveraging AI-driven personalization: The future of customer experience in digital marketing. YUME: Journal of Management, 8(1).

Fayyaz, Z., Ebrahimian, M., Nawara, D., Ibrahim, A., & Kashef, R. (2020). Recommendation systems: Algorithms, challenges, metrics, and business opportunities. Applied Sciences, 10(21), 7748. https://doi.org/10.3390/app10217748

Gaglani, H., Naidu, K., Band, G., Sharma, S., & Wandhe, P. (2024). Transforming customer experience with AI-driven CRM solutions. Nanotechnology Perceptions, 20(S5). https://doi.org/10.62441/nano-ntp.v20iS5.54

Ghanem, B., Norman, S., et al. (2023). User concerns about ethical issues related to recommendation systems. DIVA Portal. https://www.diva-portal.org/smash/get/diva2:1784468/FULLTEXT01

Guttmann, M., & Ge, M. (2024). Research agenda of ethical recommender systems based on explainable AI. Procedia Computer Science, 238, 328–335. https://doi.org/10.1016/j.procs.2024.06.032

Hanna, A. (2025). Ethical and bias considerations in artificial intelligence. ScienceDirect. https://www.sciencedirect.com/science/article/pii/S0893395224002667

Jeckmans, P. (2012). Privacy in recommender systems. https://doi.org/10.1007/978-1-4471-4555-4_12

Khurana, P., & Parveen, S. (2016). Approaches of recommender system: A survey. International Journal of Computer Trends and Technology (IJCTT), 34(3), 124–129. https://doi.org/10.14445/22312803/IJCTT-V34P124

Klimecka, N., Vo, P., & Hoai, H. (2023). How does algorithmic literacy challenge the utilisation of TikTok for global marketers, taking into account ethical concerns?

Kumar, A., Joshi, A., Antara, F., Pal Singh, S., Goel, O., & Kirupa Gopalakrishna, P. (2023). Leveraging artificial intelligence to enhance customer engagement and upsell opportunities. www.iaset.us

Magrani, E., & da Silva, P. G. F. (2023). The ethical and legal challenges of recommender systems driven by artificial intelligence. In Multidisciplinary perspectives on artificial intelligence and the law (Law, Governance and Technology Series, Vol. 58). Springer. https://doi.org/10.2139/ssrn.5245835

Makanjuola, T. (2025). Content recommendation algorithm: Assessing ethical implications and their impacts on users’ trust—An investigation of digital media platforms.

Mehmood, K., Rehman, M. A., Abbass, A., & Woyo, E. (2025). Adaptive pathways: Understanding consumer adaptive behavior toward hyper-personalized fashion retailing in emerging markets. Journal of Consumer Behaviour. https://doi.org/10.1002/cb.70016

Mengist, W., Soromessa, T., & Legese, G. (2020). Ecosystem services research in mountainous regions: A systematic literature review on current knowledge and research gaps. Science of the Total Environment, 702. https://doi.org/10.1016/j.scitotenv.2019.134581

Milano, S., et al. (2020). Studi kritis atas keberpihakan sistem AI. Journal Scientific of... https://ojs.cahayamandalika.com/index.php/jomla/article/view/4670

Milvus. (2025). What are the privacy concerns with recommender systems? Retrieved August 7, 2025, from https://milvus.io/ai-quick-reference/what-are-the-privacy-concerns-with-recommender-systems

Pati, D., & Lorusso, L. N. (2018). How to write a systematic review of the literature. Health Environments Research and Design Journal, 11(1), 15–30. https://doi.org/10.1177/1937586717747384

Sacharidis, D. (2020). Building user trust in recommendations via fairness and explanations. In UMAP 2020 Adjunct - Adjunct Publication of the 28th ACM Conference on User Modeling, Adaptation and Personalization (pp. 313–314). https://doi.org/10.1145/3386392.3399995

Sari, R. C., & Sholihin, M. (Eds.). (2022). Etika bisnis di era teknologi digital. Yogyakarta: Penerbit Andi.

Sharma, S. (2024). Unraveling biases and customer heterogeneity in e-commerce recommendation systems. https://irl.umsl.edu/dissertation/1422

Suarjana, I. W. (2025). Building business ethics in digital era. Universitas Airlangga. https://manajemen.feb.unair.ac.id/attachments/article/1713/TALK%20180524_compressed.pdf

Tolulope, L. O. (2024). The role of AI-driven personalised marketing for customer retention in the European e-commerce industry (Ireland). https://libguides.ncirl.ie/business

Zhang, T., et al. (2020). Understanding the manipulation on recommender systems. Meegle. https://shhaos.github.io/papers/tifs20-recsys_mnpl

Downloads

Published

2025-09-27

How to Cite

Nuriya Fadilah, Itaul Masarroh, & Iriani Ismail. (2025). Systematic Literature Review: Business Ethics in the Use of Recommendation Algorithms in the Digital Era. International Journal of Economics and Management Research, 5(1), 50–58. https://doi.org/10.55606/ijemr.v5i1.606

Similar Articles

<< < 2 3 4 5 6 7 8 9 10 11 > >> 

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

Most read articles by the same author(s)