When AI Agents Choose for Consumers: Rethinking Brand Competition in Delegated Markets
DOI:
https://doi.org/10.66203/directiva.01303Keywords:
consumer-based brand equity, autonomous AI agents, agent-mediated consideration access, preference translation fidelity, machine-readable brand eligibility, delegated selection discretionAbstract
Autonomous artificial intelligence (AI) agents are increasingly capable of selecting product alternatives on consumers’ behalf, raising questions about how established brand advantages translate into competitive consideration. Although brand equity, consideration set, and algorithmic intermediation research explain consumer preferences and technologically mediated exposure, their integration provides a limited account of brand inclusion under delegated selection authority. This conceptual article develops Brand Relevance in Delegated Gatekeeping Environments (BRIDGE) Theory to explain when and how consumer-based brand equity translates into agent-mediated brand consideration access. Integrating four theoretical perspectives, the study specifies three distinct mechanisms: preference translation fidelity, machine-readable brand eligibility, and agentic consideration filtering. Six propositions explain how these mechanisms condition brand inclusion, while delegated selection discretion, product attribute evaluability, consumer verification intensity, and agent objective alignment delimit the theory's applicability. BRIDGE advances a middle-range explanation distinguishing consumer-derived brand value from inclusion in agent-generated consideration sets, without presuming systematic brand displacement. The framework provides a foundation for empirical investigations of delegated purchasing, competitive exclusion, and the evolving conditions of brand relevance in AI-mediated markets.
References
Alba, J., Lynch, J., Weitz, B., Janiszewski, C., Lutz, R., Sawyer, A., & Wood, S. (1997). Interactive Home Shopping: Consumer, Retailer, and Manufacturer Incentives to Participate in Electronic Marketplaces. Journal of Marketing, 61(3), 38. https://doi.org/10.2307/1251788
Bakos, J. Y. (1997). Reducing Buyer Search Costs: Implications for Electronic Marketplaces. Management Science, 43(12), 1676–1692. https://doi.org/10.1287/mnsc.43.12.1676
Bar-Gill, O., & Sunstein, C. R. (2025). Consumer-Side Algorithms. In Algorithmic Harm (pp. 81–86). Oxford University PressNew York. https://doi.org/10.1093/oso/9780197778197.003.0008
Bergner, A. S., Hildebrand, C., & Häubl, G. (2023). Machine Talk: How Verbal Embodiment in Conversational AI Shapes Consumer–Brand Relationships. Journal of Consumer Research, 50(4), 742–764. https://doi.org/10.1093/jcr/ucad014
Bilgihan, A., Ostinelli, M. (Max), Lorenz, M., & Zhang, Y. (2026). The AI Agent in the Room: Rethinking Consumer Decision‐Making in the Age of Autonomous AI. Psychology & Marketing, 43(10), 2509–2516. https://doi.org/10.1002/mar.70165
Calvano, E., Calzolari, G., Denicolò, V., & Pastorello, S. (2024). Artificial intelligence recommendations: evidence, issues, and policy. Oxford Review of Economic Policy, 40(4), 843–853. https://doi.org/10.1093/oxrep/grae048
Campbell, M. C., & Keller, K. L. (2003). Brand Familiarity and Advertising Repetition Effects. Journal of Consumer Research, 30(2), 292–304. https://doi.org/10.1086/376800
Campbell, M. C., & Price, L. L. (2021). Three Themes for the Future of Brands in a Changing Consumer Marketplace. Journal of Consumer Research, 48(4), 517–526. https://doi.org/10.1093/jcr/ucab060
Castelo, N., Bos, M. W., & Lehmann, D. R. (2019). Task-Dependent Algorithm Aversion. Journal of Marketing Research, 56(5), 809–825. https://doi.org/10.1177/0022243719851788
Chakravarti, A., & Janiszewski, C. (2003). The Influence of Macro‐Level Motives on Consideration Set Composition in Novel Purchase Situations. Journal of Consumer Research, 30(2), 244–258. https://doi.org/10.1086/376803
Chang, W., & Park, J. (2024). A comparative study on the effect of ChatGPT recommendation and AI recommender systems on the formation of a consideration set. Journal of Retailing and Consumer Services, 78, 103743. https://doi.org/10.1016/j.jretconser.2024.103743
Compiani, G., Lewis, G., Peng, S., & Wang, P. (2024). Online Search and Optimal Product Rankings: An Empirical Framework. Marketing Science, 43(3), 615–636. https://doi.org/10.1287/mksc.2022.0071
Davenport, T., Guha, A., Grewal, D., & Bressgott, T. (2020). How artificial intelligence will change the future of marketing. Journal of the Academy of Marketing Science, 48(1), 24–42. https://doi.org/10.1007/s11747-019-00696-0
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
Donnelly, R., Kanodia, A., & Morozov, I. (2024). Welfare Effects of Personalized Rankings. Marketing Science, 43(1), 92–113. https://doi.org/10.1287/mksc.2023.1441
Dzyabura, D., & Hauser, J. R. (2019). Recommending Products When Consumers Learn Their Preference Weights. Marketing Science, 38(3), 417–441. https://doi.org/10.1287/mksc.2018.1144
Erdem, T., & Swait, J. (2004). Brand Credibility, Brand Consideration, and Choice. Journal of Consumer Research, 31(1), 191–198. https://doi.org/10.1086/383434
Grewal, D., Satornino, C. B., Davenport, T., & Guha, A. (2025). How generative AI Is shaping the future of marketing. Journal of the Academy of Marketing Science, 53(3), 702–722. https://doi.org/10.1007/s11747-024-01064-3
Häubl, G., & Trifts, V. (2000). Consumer Decision Making in Online Shopping Environments: The Effects of Interactive Decision Aids. Marketing Science, 19(1), 4–21. https://doi.org/10.1287/mksc.19.1.4.15178
Hauser, J. R., & Wernerfelt, B. (1990). An Evaluation Cost Model of Consideration Sets. Journal of Consumer Research, 16(4), 393–408. https://doi.org/10.1086/209225
Huang, M.-H., & Rust, R. T. (2021). A strategic framework for artificial intelligence in marketing. Journal of the Academy of Marketing Science, 49(1), 30–50. https://doi.org/10.1007/s11747-020-00749-9
Kaliyamurthy, A. K., & Schau, H. J. (2025). How Algorithms Constrain Consumer Experience. Journal of Consumer Research, 52(4), 826–847. https://doi.org/10.1093/jcr/ucaf016
Kardes, F. R., Kalyanaram, G., Chandrashekaran, M., & Dornoff, R. J. (1993). Brand Retrieval, Consideration Set Composition, Consumer Choice, and the Pioneering Advantage. Journal of Consumer Research, 20(1), 62. https://doi.org/10.1086/209333
Keller, K. L. (1993). Conceptualizing, Measuring, and Managing Customer-Based Brand Equity. Journal of Marketing, 57(1), 1–22. https://doi.org/10.1177/002224299305700101
Keller, K. L. (2021). The Future of Brands and Branding: An Essay on Multiplicity, Heterogeneity, and Integration. Journal of Consumer Research, 48(4), 527–540. https://doi.org/10.1093/jcr/ucab063
Kim, J. B., Albuquerque, P., & Bronnenberg, B. J. (2010). Online Demand Under Limited Consumer Search. Marketing Science, 29(6), 1001–1023. https://doi.org/10.1287/mksc.1100.0574
Kim, J., Kim, J. H., Kim, C., & Park, J. (2023). Decisions with ChatGPT: Reexamining choice overload in ChatGPT recommendations. Journal of Retailing and Consumer Services, 75, 103494. https://doi.org/10.1016/j.jretconser.2023.103494
Lasser, J., & Robertson, V. H. S. E. (2025). Alternative recommendation algorithms as an antitrust remedy in digital (democracy) cases. Journal of European Competition Law & Practice, 16(1), 20–29. https://doi.org/10.1093/jeclap/lpaf013
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
Longoni, C., Bonezzi, A., & Morewedge, C. K. (2019). Resistance to Medical Artificial Intelligence. Journal of Consumer Research, 46(4), 629–650. https://doi.org/10.1093/jcr/ucz013
Luo, Y., Kumar, N., & Yazdanmehr, A. (2026). AI nudging and decision quality: Evidence from randomized experiments in online recommendation setting. Decision Support Systems, 200, 114565. https://doi.org/10.1016/j.dss.2025.114565
Nedungadi, P. (1990). Recall and Consumer Consideration Sets: Influencing Choice without Altering Brand Evaluations. Journal of Consumer Research, 17(3), 263. https://doi.org/10.1086/208556
Priester, J. R., Nayakankuppam, D., Fleming, M. A., & Godek, J. (2004). The A Model: The Influence of Attitudes and Attitude Strength on Consideration and Choice. Journal of Consumer Research, 30(4), 574–587. https://doi.org/10.1086/380290
Puntoni, S., Reczek, R. W., Giesler, M., & Botti, S. (2021). Consumers and Artificial Intelligence: An Experiential Perspective. Journal of Marketing, 85(1), 131–151. https://doi.org/10.1177/0022242920953847
Rahinel, R., Otto, A. S., Grossman, D. M., & Clarkson, J. J. (2021). Exposure to Brands Makes Preferential Decisions Easier. Journal of Consumer Research, 48(4), 541–561. https://doi.org/10.1093/jcr/ucab025
Ribeiro, A., Rivero, A. J. L., & Abrantes, J. L. (2026). The impact of artificial intelligence on consumer behavior towards brands: a systematic review. Electronic Commerce Research, 26(5), 4483–4524. https://doi.org/10.1007/s10660-025-10063-7
Shapiro, S., MacInnis, D. J., & Heckler, S. E. (1997). The Effects of Incidental Ad Exposure on the Formation of Consideration Sets. Journal of Consumer Research, 24(1), 94–104. https://doi.org/10.1086/209496
Ursu, R. M. (2018). The Power of Rankings: Quantifying the Effect of Rankings on Online Consumer Search and Purchase Decisions. Marketing Science, 37(4), 530–552. https://doi.org/10.1287/mksc.2017.1072
Wan, X. (Shawn), Kumar, A., & Li, X. (2023). How Do Product Recommendations Help Consumers Search? Evidence from a Field Experiment. Management Science. https://doi.org/10.1287/mnsc.2023.4951
Wang, Y., Tao, L., & Zhang, X. X. (2025). Recommending for a Multi-Sided Marketplace: A Multi-Objective Hierarchical Approach. Marketing Science, 44(1), 1–29. https://doi.org/10.1287/mksc.2022.0238
Yoon, S.-O., & Simonson, I. (2008). Choice Set Configuration as a Determinant of Preference Attribution and Strength. Journal of Consumer Research, 35(2), 324–336. https://doi.org/10.1086/587630
Yuan, Z., Chen, A. Y., Wang, Y., & Sun, T. (2025). How Recommendation Affects Customer Search: A Field Experiment. Information Systems Research, 36(1), 84–106. https://doi.org/10.1287/isre.2022.0294
Zhou, B., & Zou, T. (2023). Competing for Recommendations: The Strategic Impact of Personalized Product Recommendations in Online Marketplaces. Marketing Science, 42(2), 360–376. https://doi.org/10.1287/mksc.2022.1388
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Copyright (c) 2026 Zulkarnaen, Nizar Hamdi, I Gst Gede Narung (Author)

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