Advanced International Journal of Multidisciplinary Research

E-ISSN: 2584-0487 •   Impact Factor: 9.11

An Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 4 Issue 6 November-December 2026 Submit your research before last 3 days of December to publish your research paper in the issue of November-December.
Submit Research Paper Join as a Reviewer

Plagiarism is checked by the leading plagiarism checker TurnItIn

Call for Paper

Volume 4 Issue 6
November-December 2026

Submit your research paper

Indexing Partners
Academia Advanced Sciences Index Bielefeld Academic Search Engine CiteSeer DRJI Google Scholar Independent Search Engine & Directory Network ISI (International Scientific Indexing) Issuu Mendeley Research Networks
RefSeek ResearcherId - Thomson Reuters ResearchGate Scribd Semantic Scholar UTeM - Universiti Teknikal Malaysia Melaka Wiki for Call for Papers WorldCat Zenodo

AI-Driven Product Recommendations and Consumer Purchase Behaviour on Myntra: A Descriptive Study of Users in Bengaluru.

Author(s) Dr. Kesavan R, Mr. Hemanth SR, Ms. Mary Magadalane A
Country India
Abstract Online fashion retailers increasingly rely on AI-driven recommendations to help shoppers navigate extensive assortments, yet the value of such suggestions depends on more than algorithmic relevance. Consumers also weigh brand credibility, reviews, price, trust and the handling of their personal data. This study describes how Myntra users perceive AI-driven recommendations, focusing on reported purchase behaviour, acceptance, purchase intention, satisfaction and overall shopping experience. A descriptive, cross-sectional survey was administered through a structured questionnaire to 121 Myntra users in Bengaluru, recruited by convenience sampling and analysed with frequencies and percentages; no inferential or causal claims are advanced. Recommendations influenced the purchase decisions of 76.03% of respondents at least sometimes, and 78.51% reported a moderate to very high level of influence. Browsing history and previous purchases were jointly the most influential recommendation types (27.27% each). Brand reputation was the leading acceptance factor (30.58%), whereas privacy concerns were the most frequently reported reason for rejection (27.27%). A total of 58.68% were likely or very likely to purchase products recommended according to their preferences. Satisfaction was reported by 52.07% of respondents, with 40.50% neutral; 63.64% rated their post-recommendation shopping experience as good or excellent, and 91.74% judged AI-supported shopping to be the same as or better than manual searching. Recommendations appear to support product discovery and purchase consideration, but their perceived usefulness rests on relevance, trust and responsible data practice. Because the sample was drawn by convenience, the findings should not be generalised to all Myntra users.
Keywords AI-driven product recommendations; consumer purchase behaviour; personalisation; purchase intention; customer satisfaction; Myntra
Discipline Business Administration
Published In Volume 4, Issue 6, November-December 2026
Published On 2026-11-04
View / Download Full-text Research PDF File

Share this



E-ISSN 2584-0487
Downloads
Cover Page Vol 2 Isu 5 Cover Page Vol 2 Isu 4 Cover Page Vol 2 Isu 3 Research Paper Format

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA Open Access

About AIJMR
Fees & Payment
Current Issue
Publication Archive 		Submit Research Paper
Track Submission Status
Publication Guidelines
Publication Ethics
Peer Review & Plagiarism 		Join as a Reviewer
Editors & Reviewers
Reviewer Referral Program
Get Reviewer Membership Certi. 		Website/Journal Policies
Usage Policy
Content Policies
Privacy Policy
Contact Us +91-9687-828-838 editor@aijmr.com