Algorithmic Mediation and Consumer Agency: Toward a Theory of Choice in AI-Driven Digital Markets

Artificial intelligence (AI) sits right at the intersection of consumers and markets these days. Algorithms run the show across e-commerce sites, search engines, streaming apps, social media, travel sites, financial tools, and even the latest crop of generative AI assistants. They filter, rank, recommend, personalize, predict—sometimes they even make choices for you. Sure, these tools cut down the avalanche of information and streamline transactions, but they also mess with how people figure out what they like and how they decide.
This paper lays out a theory for how algorithms shape consumer agency, weaving together research from consumer behavior, recommender systems, behavioral economics, algorithmic governance, autonomy, and responsible AI. The main point: agency in digital markets isn’t just about picking from a bunch of options. Real agency depends on whether people can actually spot their true alternatives, understand their choices, stick with preferences they genuinely care about, resist algorithmic nudges, go beyond just following recommendations, and really influence the systems that craft their options.
The paper breaks this down into four ways algorithms mediate things: they set up what options you see, guess what you like, steer your behavior, and gradually mold your preferences. From here, the paper introduces the idea of “bounded algorithmic agency”—meaning people still make deliberate choices, but always inside a digital world built and constantly tweaked by platforms and AI. The model says algorithmic systems open the door to efficiency—less time searching, sharper decisions—but also shrink agency by making people feel less in control, killing off chances to explore, and looping back into their preferences.
It flags transparency, the power to challenge recommendations, system setup, preference matching, user skill, and the importance of the decision as core factors that shape these effects. The paper offers ten hypotheses for researchers to dig into and wraps up with this: AI-driven markets demand a fresh take on consumer choice—one that goes beyond the end decision and zeroes in on the systems deciding what’s visible, what counts, and what’s even possible to pick in the first place.

algorithmic mediation, consumer agency, artificial intelligence, consumer choice, recommender systems, personalization, autonomy, choice architecture, digital markets, generative AI

1. Adomavicius, G., Bockstedt, J. C., Curley, S. P., & Zhang, J. (2013). Do recommender systems manipulate consumer preferences? A study of anchoring effects. Information Systems Research, 24(4), 956–975. https://doi.org/10.1287/isre.2013.0497 [Google Scholar] [Crossref]

2. André, Q., Carmon, Z., Wertenbroch, K., Crum, A., Frank, D., Goldstein, W., Huber, J., van Boven, L., Weber, B., & Yang, H. (2018). Consumer choice and autonomy in the age of artificial intelligence and Big Data. Customer Needs and Solutions, 5, 28–37. https://doi.org/10.1007/s40547-017-0085-8 [Google Scholar] [Crossref]

3. Dietvorst, B. J., Simmons, J. P., & Massey, C. (2015). Algorithm aversion: People erroneously avoid algorithms after seeing them err. Journal of Experimental Psychology: General, 144(1), 114–126. https://doi.org/10.1037/xge0000033 [Google Scholar] [Crossref]

4. European Parliament & Council of the European Union. (2022). Regulation (EU) 2022/2065 of the European Parliament and of the Council of 19 October 2022 on a single market for digital services and amending Directive 2000/31/EC (Digital Services Act). Official Journal of the European Union, L 277, 1–102. [Google Scholar] [Crossref]

5. Fink, L., Newman, L., & Haran, U. (2024). Let me decide: Increasing user autonomy increases recommendation acceptance. Computers in Human Behavior, 156, 108244. https://doi.org/10.1016/j.chb.2024.108244 [Google Scholar] [Crossref]

6. Joghee, M. V., G., K., Jayamala, C., Lavaraju, B., Manimalathi, P., & Singh, V. K. (2025). Environmental sustainability and economic performance: Evaluating the financial impact on the Indian economy. Revista Latinoamericana de la Papa, 29(1), 409–418. https://papaslatinas.org/ [Google Scholar] [Crossref]

7. Logg, J. M., Minson, J. A., & Moore, D. A. (2019). Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes, 151, 90–103. https://doi.org/10.1016/j.obhdp.2018.12.005 [Google Scholar] [Crossref]

8. Milano, S., Taddeo, M., & Floridi, L. (2020). Recommender systems and their ethical challenges. AI & Society, 35(4), 957–967. https://doi.org/10.1007/s00146-020-00950-y [Google Scholar] [Crossref]

9. Mills, S., & Sætra, H. S. (2024). The autonomous choice architect. AI & Society, 39(2), 583–595. https://doi.org/10.1007/s00146-022-01486-z [Google Scholar] [Crossref]

10. Mukherjee, S., Salman, S. A., Sukdeo, N. I., Singh, V. K., & Chowdary, V. G. R. (2026). Climate awareness and behaviour of Gen Z travellers: Pathways to achieving Sustainable Development Goals in hospitality and tourism. International Journal of Tourism Cities. Advance online publication. https://doi.org/10.1080/20565607.2026.2663903 [Google Scholar] [Crossref]

11. Rohden, S. F., & Espartel, L. B. (2024). Consumer reactions to technology in retail: Choice uncertainty and reduced perceived control in decisions assisted by recommendation agents. Electronic Commerce Research, 24(2), 901–923. https://doi.org/10.1007/s10660-024-09808-7 [Google Scholar] [Crossref]

12. Roy, R., Das, T., Pal, D., Chowdary, V. G. R., Singh, V. K., Mukherjee, S., & Babakerkhell, M. D. (2026). Understanding metaverse use in educational settings: Evidence from students and teachers. Cogent Education, 13(1), Article 2689762. https://doi.org/10.1080/2331186X.2026.2689762 [Google Scholar] [Crossref]

13. Singh, V. K., Alkhwaldi, A. F., Abu-AlSondos, I. A., Salameh, A. A., Panigrahi, R. R., & Mukherjee, S. (2026). Modelling the effectiveness of technology adoption in NBFCs for the achievement of digital transformation: A structural equation modelling approach. Scientific Culture, 12(1.1), 147–164. https://doi.org/10.5281/zenodo.11425120 [Google Scholar] [Crossref]

14. Singh, V. K., Shaikh, Z. H., Sinha, K., Panigrahi, R. R., Mukherjee, S., & Chowdary, V. G. R. (2026). From technological revolution to digital transformation in non-banking financial companies: A future research direction. Journal of Open Innovation: Technology, Market, and Complexity, 12(1), Article 100712. https://doi.org/10.1016/j.joitmc.2025.100712 [Google Scholar] [Crossref]

15. Singh, V. K., Sinha, K., Sahdeo, S. N., Choudhary, N., & Gupta, R. (2025). Impact of AI-driven risk-based pricing on loan approval efficiency in rural non-banking financial services. Advances in Consumer Research, 2(4), 3374–3381. https://acr-journal.com/article/impact-of-ai-driven-risk-based-pricing-on-loan-approval-efficiency-in-rural-non-banking-financial-services-1431/ [Google Scholar] [Crossref]

16. Singh, V. K., & Sinha, K. (2026). The effectiveness of technology adoption among the customers of Cholamandalam Investment & Finance Company Limited in Jharkhand: An empirical assessment. Canadian Journal of Marketing Research, 16(3), 222–242. https://doi.org/10.61336/r4twgh10 [Google Scholar] [Crossref]

17. Thaler, R. H., & Sunstein, C. R. (2008). Nudge: Improving decisions about health, wealth, and happiness. Yale University Press. [Google Scholar] [Crossref]

18. Toshi, K., Verma, A., Chauhan, S. K., & Singh, V. K. (2026). Healthcare ecosystem innovation in emerging smart cities: Co-opetition and public-private partnerships in Ranchi District. Minnesota Journal of Business Law and Entrepreneurship, 3, 1102–1135. https://kommerstad.org/journal/article/view/699 [Google Scholar] [Crossref]

19. Tripathy, S., Singh, V. K., Rejeb, A., et al. (2026). Meta-analytic study of human-AI co-decision systems and workforce intelligence in enhancing sustainability performance in circular supply chains. Discover Sustainability. https://doi.org/10.1007/s43621-026-04157-x [Google Scholar] [Crossref]

20. Wertenbroch, K., Schrift, R. Y., Alba, J. W., Barasch, A., Bhattacharjee, A., Giesler, M., Knobe, J., Lehmann, D. R., Matz, S., Nave, G., Parker, J. R., Puntoni, S., Zheng, Y., & Zwebner, Y. (2020). Autonomy in consumer choice. Marketing Letters, 31(4), 429–439. https://doi.org/10.1007/s11002-020-09521-z [Google Scholar] [Crossref]

21. Yeung, K. (2017). ‘Hypernudge’: Big Data as a mode of regulation by design. Information, Communication & Society, 20(1), 118–136. https://doi.org/10.1080/1369118X.2016.1186713 [Google Scholar] [Crossref]

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