Title:
Analysis of AI-driven Consumer Choice, Leading to Market Diversity Decrease

Authors:
Udita Saha, Sujata Joshi, Menachem Domb, Israel

Abstract:

Through AI-based recommendation systems, consumer decision-making has changed tremendously, with increasing concerns about possible choice homogenization. This study has carried out a bibliographic review of the existing literature to delineate the intellectual structure, emerging trends, and research trajectories in this domain. Using available information from the Scopus database, we apply Bibliometric techniques: co-authorship networks, citation analysis, and keyword co-occurrence mapping to identify impactful works, major contributors, and many more. The findings provide a rapidly growing research interest in algorithmic personalization, behavioral economics, and the unintended consequences plugged in by recommendation systems, filter bubbles, and constricted diversity in consumer choices. Through the retrospective vision of scholarly discourse, this study reflects research gaps and future directions in framing AI-based personalization frameworks that enable individual preferences to beat market diversity. The results also contribute greatly to understanding how algorithmic curation influences consumer behavior and open further interdisciplinary research between AI ethics, digital marketing, and computational social science.

DOI: http://dx.doi.org/10.51505/ijaemr.2025.1302