Keywords
Algorithmic recommendations
Privacy and ethics
Submitted : 2025-01-21
Accepted : 2025-02-05
Published : 2025-02-20
Abstract
This paper examines the impact of algorithmic recommendations and data-driven marketing on consumer engagement and business performance. By leveraging large volumes of user data, businesses can deliver personalized content that enhances user experiences and increases conversion rates. However, the growing reliance on these technologies introduces significant risks, including privacy violations, algorithmic bias, and ethical concerns. This paper explores these challenges and provides recommendations for businesses to mitigate associated risks while optimizing marketing strategies. It highlights the importance of transparency, fairness, and user control in ensuring responsible and effective data-driven marketing.
References
Liu Y, 2021, Analysis of Consumer Demand in the Advertising Industry under the Development of the Internet Big Data and the Enterprise Precision Marketing. 2021 5th International Conference on Economics, Management Engineering and Education Technology (ICEMEET 2021), 2021: 157–161. https://doi.org/19,25236/icemeet.2021.039
Panesar A, Saini I, 2024, Ethical Dimensions of AI Integration in Influencer Campaigns: Balancing Authenticity, Targeting Precision, and User Privacy, in Dutta S, Rocha Á, Dutta PK, et al., (eds), Advances in Data Analytics for Influencer Marketing: An Interdisciplinary Approach. Springer, Cham. https://doi.org/10.1007/978-3-031-65727-6_24
Yang Z, 2022, Research on Personalized Product Recommendation Algorithm for User Implicit Behavior Feedback. Proceedings of the 12th International Conference on Computer Engineering and Networks. Springer, Singapore. https://doi.org/10.1007/978-981-19-6901-0_149
Hoque S, Hossain MA, 2023, Social Media Stickiness in the Z Generation: A Study Based on the Uses and Gratifications Theory, 11(4): 92–108. https://doi.org/10.1633/JISTaP.2023.11.4.6
Siregar Y, Kent A, Peirson-Smith A, et al., 2023, Disrupting the Fashion Retail Journey: Social Media and GenZ’s Fashion Consumption. International Journal of Retail & Distribution Management, 51(7): 862–875.
Jahagirdar AD, Morankar H, 2023, The Impact of Advertising on Consumer Behaviour: A Study on Various Advertising Types and Effectiveness. International Journal of Research and Analytical Review, 10(3): 716–744.
Murshed NA, Ugurlu E, 2023, Navigating the Digital Marketplace: A Holistic Model Integrating Social Media Engagement and Consumer Behavior Factors to Enhance Online Shopping Adoption. Journal of Theory and Applied Management, 16: 542–559. https://doi.org/10.20473/jmtt.v16i3.52059
Vasilopoulou C, Theodorakopoulos L, Igoumenakis G, 2023, The Promise and Peril of Big Data in Driving Consumer Engagement. Technium Social Sciences Journal, 45(1): 489–499. https://doi.org/10.47577/tssj.v45i1.9133
Noman AA, Akter UH, Pranto TH, et al., 2022, Machine Learning and Artificial Intelligence in Circular Economy: A Bibliometric Analysis and Systematic Literature Review. Annals of Emerging Technologies in Computing, 6(2): 13–40. https://doi.org/10.33166/AETiC.2022.02.002
Dietrich T, Hurley E, Kassirer J, et al., 2022, 50 Years of Social Marketing: Seeding Solutions for the Future. European Journal of Marketing, 56(5): 1434–1463. https://doi.org/10.1108/EJM-06-2021-0447
Davis Noll BA, Revesz RL, 2019, Regulation in Transition. Minnesota Law Review, 104. http://dx.doi.org/10.2139/ssrn.3348569
Xing S, Liu F, Wang Q, et al., 2019, A Hierarchical Attention Model for Rating Prediction by Leveraging User and Product Reviews. Neurocomputing, 332(C): 417–427. https://doi.org/10.1016/j.neucom.2018.12.027
Al-Dmour R, Al-Dmour H, Al-Dmour A, 2024, The Crucial Role of EWOM: Mediating the Impact of Marketing Mix Strategies on International Students’ Study Destination Decision. SAGE Open, 14(2): 972–995. https://doi.org/10.1177/21582440241247661
Sykes M, Rosenberg-Yunger ZRS, Quigley M, et al., 2024, Exploring the Content and Delivery of Feedback Facilitation Co-interventions: A Systematic Review. Implementation Sci, 19: 37. https://doi.org/10.1186/s13012-024-01365-9
Cai J, 2023, Research on the Influencing Factors of Consumer Buying Behavior. Highlights in Science, Engineering and Technology, 61(1): 119–127. https://doi.org/10.54097/hset.v61i.10281
Zhang Y, Gosline R, 2023, Human Favoritism, Not AI Aversion: People’s Perceptions (and Bias) Toward Generative AI, Human Experts, and Human–GAI Collaboration in Persuasive Content Generation. Judgment and Decision Making, 18: e41. https://doi.org/10.1017/jdm.2023.37
Chu J, 2023, The Effects of Personalized Advertisements on Consumer Decision-Making Behavior. International Journal of High School Research, 5(3): 54–59. https://doi.org/10.36838/v5i3.11
Masana SNDS, Rudrapati GS, Gudiseva K, et al., 2024, Temporal Data Mining on the HighSeas: AIS Insights from BigDataOcean. Machine Intelligence, Tools, Applications, 2024: 394–402. https://doi.org/10.1007/978-3-031-65392-6_34
Obucic E, Poturak M, Keco D, 2023, Predicting User Engagement of Facebook Post Images in Leading Universities: A Machine Learning Approach. Revue d’Intelligence Artificielle, 37(4): 1039–1045. https://doi.org/10.18280/ria.370426
Ozkan Ozen YD, Sezer D, Ozbiltekin M, et al., 2022, Risks of Data-driven Technologies in Sustainable Supply Chain Management. Management of Environmental Quality: An International Journal, 34(4): 926–942. https://doi.org/10.1108/MEQ-03-2022-0051