The Impact of AI-Generated Product Recommendations on Consumer Purchase Intentions: A Secondary Data Analysis

Volume 11, Issue 2, 2026

International Journal of Commerce and Management Studies, ISSN 2456-3684

Paper Title

Author Name and Affiliation

Netra Nilesh Wyawahare

Department of BBA, Dr. D. Y. Patil Arts, Commerce, and Science College, Pimpri, Pune, Maharashtra, India, Email: netra.wyawahare@dypvp.edu.in

Abstract

Artificial Intelligence (AI) has advanced so quickly that it has completely changed digital marketing techniques, especially when it comes to using AI-generated product recommendation systems. These systems analyze consumer behavior, preferences, browsing history, and purchase patterns to offer customized product recommendations, which enhance customer experience and boost sales success. As e-commerce platforms proliferate across the globe, it is more vital than ever for marketers, academics, and developers of technology to determine the efficacy of AI recommendations in changing consumer buy intentions.

The research investigates the impact of AI-generated product suggestions on the purchase intents of consumers by doing a thorough secondary data analysis of academic articles, industry reports, market surveys, and white papers from 2019 to 2026. The research is based on the top academic databases such as Scopus, Web of Science, Google Scholar, and industrial sources such as McKinsey, Deloitte, PwC, Statista.

The study finds that suggestion quality, customisation, consumer trust, and perceived utility are important drivers of consumer purchase intentions. Our results indicate that highly tailored and precise AI recommendations improve purchasing choices by increasing perceived relevance and minimizing the costs of information search. However, algorithmic privacy concerns,

Consumer trust and adoption continue to be influenced by openness and data security.

This research integrates theoretical perspectives from the Technology Acceptance Model (TAM), Theory of Planned Behaviour (TPB), Stimulus-Organism-Response (SOR) framework and Information Processing Theory, contributing to the burgeoning literature of AI-enabled marketing. Practical ramifications for e-commerce platforms, marketers and AI developers interested in optimizing recommendation systems while maintaining consumer trust are offered.

Keywords

Artificial Intelligence, Product Recommendation Systems, Consumer Purchase Intention, Personalization, Consumer Trust, E-commerce, Digital Marketing

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DOI

DOI: 10.67061/ijcams.2026.vol.11.issue.02.8118

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