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Journal of Emerging Technologies and Innovation Management

ISSN: 3107-8001 (Online) | Frequency: Half-yearly | Type: Open Access | Peer Review: Double Blind

Editor in Chief : Dr. Akshat Agrawal

About : The Journal of Emerging Technologies and Innovation Management is a bi-annual, peer-reviewed, national, e-journal, committed to advancing the understanding and integration of cutting-edge technological advancements into modern management practices. As an interdisciplinary, peer-reviewed journal, it Read more

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Examining Consumer Attitudes toward Personalised AI-Generated Product Recommendations among College Students1

  • Minal Bhagat* Minal Bhagat Corresponding author Amity Institute of Psychology and Allied Sciences, Amity University Uttar Pradesh, Noida, India ,  
  • Vanini Bharadwaj Vanini Bharadwaj Amity Institute of Psychology and Allied Sciences, Amity University Uttar Pradesh, Noida, India ,  
  • Shambhawi Sahoo Shambhawi Sahoo Amity Institute of Psychology and Allied Sciences, Amity University Uttar Pradesh, Noida, India
Received: March 21, 2025
Accepted: April 30, 2025
Published: June 16, 2025
Volume: 1 (1) | Page: 12-20

Abstract

Artificial Intelligence (AI) has significantly reshaped digital marketing, offering businesses innovative ways to boost customer engagement through personalised and automated suggestions. Drawing on the Technology Acceptance Model (Davis, 1989), this study examines how perceived usefulness and perceived ease of use influence users’ acceptance of new technologies in the context of AI-driven individualised product recommendations. It also investigates the relationship between Attitude Toward Using AI (ATU) and Purchase Intention (PI) regarding AI-generated recommendations. Based on a sample of college students, the findings reveal a meaningful connection between ease of use and perceived value, as well as a positive relationship between users’ attitudes toward AI and their intention to make purchases. These insights underscore the importance of user perception in shaping engagement and offer practical implications for marketing professionals, business strategists, and policymakers seeking to strengthen trust and optimise user experiences within AI-powered recommendation systems.

Keywords: Artificial Intelligence, Perceived ease of use, attitude, purchase intention, product recommendation

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