PERCEIVED RISKS IN ONLINE APPAREL SHOPPING: A STUDY OF CONSUMER BEHAVIOR IN BHOPAL CITY
Purpose:The rapid growth of e-commerce has significantly transformed the landscape of online apparel shopping, providing consumers with the convenience of ease and a huge array of choices. However, perceived risks such as financial, product, privacy, and delivery concerns continue to affect consumer behaviour and limit full adoption. This study has aimed to identify the dimensions of perceived risk in online apparel shopping within the sociocultural and economic context of Bhopal city. The present research identifies the major risk factors and understand the influences that shape consumer perceptions and analyse strategies consumers use to mitigate those risks. Methodology:The study adopts a quantitative research approach using a structured questionnaire to gather data from 30 respondents residing in Bhopal city through convenience sampling. Data is analysed using descriptive statistics, chi-square tests, and graphical representation of trends and influences. Findings: Consumers are moderately to highly concern about product quality, privacy, and delivery risks. Financial risk is not a major concern due to improved secure payment systems. Gender, age, occupation, and income do not have significant influence on risk perception. Reading reviews, secure payment methods, trusted brands, and clear return policies are the most commonly adopted strategies. Perceived risks significantly influence purchase decisions and online shopping frequency. Consumers who are highly concerned about risks tend to shop less frequently. Originality Value: This study provides localized insights into consumer risk perceptions within a Tier-2 city and highlights the particular concerns and behaviour of online apparel shoppers in Bhopal. It provides practical recommendations for e-commerce businesses to address these barriers by enhancing trust, transparency, and service quality.
Authors
- Mrs. Jaya Saini
- Dr.Rasmeet Kaur Malhi
Publication Details
- Published
- 2025-02-15
- DOI
- https://doi.org/10.51767/ic250104
- Primary Topic
- Smart Systems and Machine Learning
- Type
- article
- Field-Weighted Citation Impact
- 0.00