 The impact of recommendation systems on the retail market: the consumer perspectiveKamil Harla Chapter 3 in The Twin Digital and Green Transition, 2026, pp 39-60 from Edward Elgar Publishing Abstract: The chapter examines how AI-based recommendation systems influence consumer behavior and price changes in retail markets through the dynamic interactions of consumers, products, and algorithms. Drawing on mathematical economics and game theory, and employing simulation-based analysis, we model an environment where consumers have varying preferences, products are subject to price fluctuations, and recommendation algorithms adapt sales offers accordingly. Preliminary results suggest that effectively personalized recommendations may lead to increased market concentration and reduced price differentiation, though these outcomes depend on the degree of personalization and pricing flexibility. These findings point to the need for regulatory measures that maintain healthy market competition, as well as ethical guidelines that offer diversity and ensure fair treatment of consumers. By providing a long-term view of recommendation systems’ effects, this research offers a valuable contribution to the understanding of their impact on the retail sector. Keywords: Recommendation systems; Industrial organization; Consumer modeling; Market dynamics; Simulations; Game theory; Artificial intelligence; AI (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:elg:eechap:24685_3 Ordering information: This item can be ordered from Access Statistics for this chapter More chapters in Chapters from Edward Elgar Publishing |
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