NAVIGATING ALGORITHMIC AVERSION: CONSUMER TRUST AND ADOPTION OF AI-GENERATED RECOMMENDATIONS IN HIGH-INVOLVEMENT CATEGORIES
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Ririn Laila Ulfah
Lukmanul Hakim
Dahrul Aman Harahap
This article examines the phenomenon of algorithmic aversion and the factors influencing consumer trust and adoption of AI-generated recommendations, particularly in high-involvement categories such as finance, healthcare, and education. As artificial intelligence becomes increasingly embedded in decision-making processes, consumers are often faced with the challenge of relying on algorithmic systems for complex and high-stakes decisions. Using a narrative literature review, this study synthesizes insights from marketing, information systems, and consumer psychology to explore the dual nature of consumer responses, characterized by both algorithmic aversion and appreciation. The findings identify key themes, including the role of trust, perceived competence, transparency, and explainability in shaping acceptance of AI recommendations. The study also highlights the mechanisms underlying algorithmic aversion, such as perceived lack of empathy, sensitivity to errors, and threats to autonomy. In addition, several moderating factors are identified, including involvement level, consumer expertise, trust in technology, and cultural context. A conceptual framework is proposed to illustrate how these factors interact to influence adoption, reliance, or rejection of AI-generated recommendations. The article contributes by integrating trust theory with algorithmic decision-making and offers practical implications for designing human-centered AI systems.
Adeyemi, A., Karieren, O., Olugbile, H., Okwe, V. I., & Haroun, F. (2025). A review on algorithm aversion, appreciation, and investor return beliefs. International Journal of Science and Research Archive. https://doi.org/10.30574/ijsra.2025.16.1.1968
Arora, S., & Thota, S. R. (2024). Using Artificial Intelligence with Big Data Analytics for Targeted Marketing Campaigns. International Journal of Advanced Research in Science, Communication and Technology, 593–602. https://doi.org/10.48175/ijarsct-18967
Babadoğan, B. (2024). Unveiling the Power of AI-Driven Personalization: Transforming Consumer Behavior in the Age of Digital Marketing. Next Frontier For Life Sciences and AI. https://doi.org/10.62802/fj43xy18
Burton, J., Stein, M.-K., & Jensen, T. (2019). A systematic review of algorithm aversion in augmented decision making. Journal of Behavioral Decision Making. https://doi.org/10.1002/bdm.2155
Chintalapati, S., & Pandey, S. (2021). Artificial intelligence in marketing: A systematic literature review. International Journal of Market Research. https://doi.org/10.1177/14707853211018428
Gahlawat, M., Dadhich, G., & Reddy, S. (2025). Environmental Awareness and Sustainable Consumption: A Comprehensive Bibliometric Analysis of Consumer Behavior Literature. Economic Sciences. https://doi.org/10.69889/t1ffpd54
Gungunawat, A., Khandelwal, N., & Gupta, N. (2024). AI-Powered Personalization in Digital Marketing: Transforming Consumer Engagement and Strategy. RESEARCH REVIEW International Journal of Multidisciplinary, 9(11), 183–191. https://doi.org/10.31305/rrijm.2024.v09.n11.026
Haleem, A., Javaid, M., Asim Qadri, M., Pratap Singh, R., & Suman, R. (2022). Artificial intelligence (AI) applications for marketing: A literature-based study. International Journal of Intelligent Networks, 3, 119–132. https://doi.org/10.1016/j.ijin.2022.08.005
Hou, Y. T.-Y., & Jung, M. F. (2021). Who is the Expert? Reconciling Algorithm Aversion and Algorithm Appreciation in AI-Supported Decision Making. Proc. ACM Hum. Comput. Interact. https://doi.org/10.1145/3479864
Islam, M. A., Fakir, S. I., Masud, S. Bin, Hossen, Md. D., Islam, Md. T., & Siddiky, M. R. (2024). Artificial intelligence in digital marketing automation: Enhancing personalization, predictive analytics, and ethical integration. Edelweiss Applied Science and Technology. https://doi.org/10.55214/25768484.v8i6.3404
Jin, F., & Zhang, X. (2023). Artificial intelligence or human: when and why consumers prefer AI recommendations. Information Technology & People, 38(1), 279–303. https://doi.org/10.1108/itp-01-2023-0022
Jin, X., & Li, J. (2025). The Influence of ‘Algorithm Aversion’ and ‘Algorithm Appreciation’ Among Consumers in Unstructured Tasks. International Journal of Consumer Studies. https://doi.org/10.1111/ijcs.70133
Kopalle, P. K., Gangwar, M., Kaplan, A., Ramachandran, D., Reinartz, W., & Rindfleisch, A. (2022). Examining artificial intelligence (AI) technologies in marketing via a global lens: Current trends and future research opportunities. International Journal of Research in Marketing, 39(2), 522–540. https://doi.org/10.1016/j.ijresmar.2021.11.002
Koricina, J., & Spálová, L. (2025). How Trust in AI Recommendations Emerges: Insights from Experimental Studies. Media & Marketing Identity. https://doi.org/10.34135/mmidentity-2025-37
Leela.Vemula, Dr. H., Telangana, H., Khatri, Dr. S., Coordinator, A., Vijayalakshmi, D., Hatole, Dr. S., & India, A. (2025). Artificial Intelligence in Consumer Decision-Making: A Review of. Journal of Informatics Education and Research. https://doi.org/10.52783/jier.v5i2.2631
Li, M. Z., & Stodolska, M. (2021). Beyond luxury consumption: The meaning and meaning-making mechanism in conspicuous outbound tourism. Journal of Leisure Research. https://doi.org/10.1080/00222216.2021.1949651
Lim, W. M. (2022). The Sustainability Pyramid: A Hierarchical Approach to Greater Sustainability and the United Nations Sustainable Development Goals With Implications for Marketing Theory, Practice, and Public Policy. Australasian Marketing Journal. https://doi.org/10.1177/18393349211069152
Mariani, M. M., Perez‐Vega, R., & Wirtz, J. (2021). AI in marketing, consumer research and psychology: A systematic literature review and research agenda. Psychology & Marketing. https://doi.org/10.1002/mar.21619
Oğuz, A. (2024). Consumer Behavior in the Era of AI-Driven Marketing. Human-Computer Interaction. https://doi.org/10.62802/h9frxh42
Pagala, I., Asir, M., Mere, K., Puji Lestari, U., & Siddiqa, H. (2024). Consumer Behavior in the Age of AI: The Role of Personalized Marketing and Data Analytics in Shaping Purchase Decisions. Dinasti International Journal of Education Management And Social, 5(6), 1898–1907. https://doi.org/10.38035/dijemss.v5i6.2947
Potwora, M., Vdovichena, O., Semchuk, D., Lipych, L., & Saienko, V. (2024). The use of artificial intelligence in marketing strategies: Automation, personalization and forecasting. Journal of Management World. https://doi.org/10.53935/jomw.v2024i2.275
Sahu, Dr. J. K., & Anjana Dr. Chamoli, Mr. D. S. (2025). Personalized Marketing in the Digital Age: The Role of AI in Consumer Behavior Analytics. European Economic Letters (EEL). https://doi.org/10.52783/eel.v15i3.3415
Shen, Y. (2024). Impact Factors for Sustainable Consumption in Todays World. Advances in Economics, Management and Political Sciences. https://doi.org/10.54254/2754-1169/114/2024bj0147
Siepmann, C., Holthoff, L. C., & Kowalczuk, P. (2021). Conspicuous consumption of luxury experiences: an experimental investigation of status perceptions on social media. Journal of Product & Brand Management, 31(3), 454–468. https://doi.org/10.1108/jpbm-08-2020-3047
Sifat, A. I. (2025). The algorithmic consumer: A conceptual investigation of AI’s influence on consumer preferences and decisions. Asian Management and Business Review. https://doi.org/10.20885/ambr.vol5.iss2.art14
Ubal, V., Lisjak, M., & Mende, M. (2024). Cracking the consumers’ code: A framework for understanding the artificial intelligence-consumer interface. Current Opinion in Psychology. https://doi.org/10.1016/j.copsyc.2024.101832
Wu, S., & Gan, X. (2025). Literature Review on Algorithm Aversion: Definition, Antecedents, and Mitigation. Journal of Global Economy, Business and Finance, 7(10), 38–43. https://doi.org/10.53469/jgebf.2025.07(10).08
Zerilli, J., Bhatt, U., & Weller, A. (2022). How transparency modulates trust in artificial intelligence. Patterns. https://doi.org/10.1016/j.patter.2022.100455
