The Impact of Perceived Risks on Consumer Online Shopping in the Apparel Industry in Kegalle District in Sri Lanka

With the recent growth of the internet and its use in various businesses, as well as the advantages of saving time and reducing geographic limitations through the Internet, most consumers are focusing on using e-commerce to acquire goods and services. When consumers shop online, they face a variety of issues, including payment security, privacy concerns, e-contact accuracy, inaccurate information disclosure, and many others. As a result, the researcher intends to investigate the impact of perceived risk on consumers' online shopping, with a focus on the online apparel retail market in Sri Lanka's Kegalle district. The study looked at six perceived risk factors that influence consumers' online shopping for apparel products. The study specifically looked at the impact of product risk, financial risk, time risk, information security risk, delivery risk, and social risk on consumers' apparel product online shopping in Sri Lanka's Kegalle district.
To achieve the research objective, the researcher distributed a questionnaire among 200 consumers in the Kegalle district using a convenience sampling method. The researcher analyzed data using the quantitative method via IBM SPSS software.
Results for the univariate analysis indicated that there is a lower level of perceived risk in the apparel industry in the Kegalle district in Sri Lanka and a high level of online shopping in the apparel industry in the Kegalle district in Sri Lanka. Pearson Coefficient correlation indicates that there is a strong negative relationship between perceived risks and online shopping. The r-squared value indicated 0.521, i.e., 52.1% of the variation in online shopping is explained by product risk, financial risk, time risk information security risk, delivery risk and social risk. Finally, concluded that perceived risks has a negative impact on consumer online shopping in the apparel industry in Kegalle district in Sri Lanka.

Perceived Risks, Online Shopping, Product Risk, Financial Risk, Time Risk, Information Security Risk, Delivery Risk, Social Risk

1. Aboobucker, I. (2019). Consumers’ Perceived Security Risks in Onlin Shopping: A Survey Study in Sri Lanka. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3344634 [Google Scholar] [Crossref]

2. Antonijevic, T., Lancaster, J. L., & Starobin, J. M. (2014). Modeling order-disorder transition in Low-Density Lipoprotein. 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014, 8(1), 5220– 5223. https://doi.org/10.1109/EMBC.2014.6944802 [Google Scholar] [Crossref]

3. Ariff, M. S. M., Sylvester, M., Zakuan, N., Ismail, K., & Ali, K. M. (2014). Consumer perceived risk, attitude and online shopping behaviour; Empirical evidence from Malaysia. IOP Conference Series: Materials Science and Engineering, 58(1). https://doi.org/10.1088/1757-899X/58/1/012007 [Google Scholar] [Crossref]

4. Arshad, A., Zafar, M., Fatima, I., & Khan, S. K. (2015). The Impact of Perceived Risk on Online Buying Behavior. International Journal of New Technology and Research, 1(8), 13–18. [Google Scholar] [Crossref]

5. Featherman, S. M. (2003). Predicting e-services adoption: A perceived risk facets perspective. International Journal of Human-Computer Studies. [Google Scholar] [Crossref]

6. Jayasinghe C., Fernando S. & siriwardhana, Y. (2019). Sri Lanka's Apparel Industry in a Changing Global Economy: Challenges and Strategies. Springer. [Google Scholar] [Crossref]

7. Keen, &. G. (2001). Recreational shopping and the identity motivation of the shopper. International Journal of Retail & Distribution Management. [Google Scholar] [Crossref]

8. Kim. (2007). A trust-based consumer decision-making model in electronic commerce: . [Google Scholar] [Crossref]

9. Kim, J. a. (2008). Adoption of virtual try-on technology for online apparel shopping. Journal of Interactive Marketing. [Google Scholar] [Crossref]

10. Liang, T.-P. a.-S. (1998). An Empirical Study on Consumer Acceptance of Products in Electronic Markets: A Transaction Cost Model. Decision Support Systems. [Google Scholar] [Crossref]

11. Mathew, B. (2015). A Study on Changing Trends in Online Shopping of Indian Consumers in Apparel Segment. International Journal of Applied Research, 1(9), 207–214. [Google Scholar] [Crossref]

12. Zheng, L., Favier, M., Huang, P., & Coat, F. (2012). Chinese consumer perceived risk and risk relievers in e-shopping. 17th Symposium of the Association Information and Management 2012, AIM 2012, 13(3), 255–274. [Google Scholar] [Crossref]

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