Published Online:March 2025
Product Name:The IUP Journal of Information Technology
Product Type:Article
Product Code:IJIT030325
DOI:10.71329/IUPJIT/2025.21.1.49-75
Author Name:Suriya Kumari A, S Mani, A Arumugam and Candida Smitha
Availability:YES
Subject/Domain:Engineering
Download Format:PDF
Pages:49-75
The paper studies the effects of artificial intelligence (AI) on the impulse buying behavior of internet consumers in Bengaluru. The purpose is to look at AI recommendations and gullibility concept to determine how they might be used to make people buy impulsively. The study made an online survey of 500 shoppers to measure the impacts of AI recommendation on impulse buying, and then used 30 in-depth interviews to elaborate on psychological and emotional arousal. The results revealed a relationship between AI suggestions and impulse buying. Targeted recommendations, perceived convenience, and dynamic pricing were found to be some of the main stimuli. In addition to basic social needs, fear of missing out (FOMO), loss aversion, thrill and social reward led to heightened impulsivity and frequent impulse purchases. The implications are twofold: In the case of consumers, an awareness of the increasing role of AI in purchases means consumers are making purchases more deliberately. In particular, the study highlights the need for ethical scrutiny of e-commerce platforms, ensuring that AI algorithms are designed to benefit users without exploiting psychological vulnerabilities. The conclusions contribute to the ongoing discourse on technological advancements and consumer rights, emphasizing the importance of further research to evaluate the measurable effects of regulatory measures on consumer welfare.
A major aspect that has shaped the consumption experience in e-commerce business is artificial intelligence (AI). One of its uses is where machine learning (ML) algorithms perform a vital function of enhancing shoppers’ experience by constantly sorting through consumers’ data like site visits, buying patterns, and other attributes (Chen et al., 2021c; Zhang & Zhao, 2022a).