FINDINGS
• Most students shop once a month or more than once in six months.
• The majority of students spend up to ₹500 per purchase.
• Most students prefer shopping during heavy discount periods or when needed.
• Mobile phones are the most preferred medium for online shopping.
• Cash on Delivery (COD) and UPI payments are the most common payment
methods.
• Amazon, Flipkart, and Meesho are the most preferred platforms.
• Most students rate their online shopping experience as "satisfied" or "very much
satisfied".
• A few students rated their experience as "average".
• Saving money and saving time are the top reasons students shop online.
• Clothing is the most commonly purchased category.
• Other purchases include healthcare products.
• The most common issues are cheap quality products, incorrect products, and
damaged items.
• While some students find it easy to return or exchange products from the hostel,
others are neutral.
• A majority have never returned a product, though a few have.
TABLE OF CONTENTS
CHAPTER NO. TITLE PAGE NO.
ABSTRACT I
LIST OF TABLES Ii
LIST OF CHARTS Iii
INTRODUCTION 1
1 1.1 Introduction About the Study 1
1.2 Industry Profile 1
1.3 Need for study 3
1.4 Scope and Significance of study 3
1.5 Objective OF Study 4
1.6 Limitations of The Study 4
2 REVIEW OF LITERATURE 5
2.1 Review of Literature 5
3 RESEARCH METHODOLOGY 12
3.1 Research Design 12
3.2 Sources of data 12
3.3 Structure of Questionnaire 12
3.4 Sampling Technique 12
3.5 Period of Study 12
3.6 Analytical Tools 12
4 DATA ANALYSIS AND INTERPRETATION 13
4.1 Tables and Chart 13
5 5.1 Findings 31
5.2 Suggestions and Recommendations 32
5.3 Conclusions 32
REFERENCES 33
APPENDIX – 1 (Questionnaire) 35
Chapter 3
Research Methodology
3.1 RESEARCH DESIGN
3.1.1 Descriptive Research Design
Descriptive research is a study designed to depict the participants in an accurate way.
More simply put, descriptive research is all about describing people who take part in the
study.
3.2 SOURCES OF DATA
Data collection is the term used to describe a process of preparing and collecting data.
3.2.1 Primary Data – Questionnaire given to 150 respondents
3.2.2 Secondary Data - Websites and online journals, Published reports & Review of
literature from published articles
3.3 STRUCTURE OF QUESTIONNAIRE
Questionnaire was divided into two sections. First part was designed to know the general
information about customers and the second part contained the respondent ‘s opinions
about customer ‘s experience
3.4 SAMPLING TECHNIQUE
Convenience sampling method
A convenience sample is one of the main types of non-probability sampling methods. A
convenience sample is made up of people who are easy to reach.
3.5 PERIOD OF STUDY
The duration of study is from DECEMBER 2020 to March 2021 which is a three month
of study.
3.6 Analytical Tools
Percentage analyses, Bar Chart.
12
5.2 SUGGESTION
The overall experience index from the study reveals that the company is performing very
well and customers buying are much experience with the service given to them. Some of
the customers have complained about the slack in the delivery process and timings.
Therefore, this is the area which I recommend to the showroom to focus a little bit more.
It needs to improve its delivery process and time. Need to become little quick and fast.
The websites should also work considerably in building trust and friendly relationship with
customers by coming with better privacy policies as this would encourage shoppers to
use others mode of payments like debit cards, credit cards etc. as well.
5.3 Conclusion
A successful webstore is not the just a good-looking website with the dynamic technical
features but is also emphasis on building the relationship with customers with making
money. Firstly, understanding the customers need and wants is very essential for building
a relation with the customers keeping companies’ promises gives a customer a reason to
come back and meeting the expectations gives them a reason to stay. Price factor and
after sale factor play an important role in e commerce business so online marketers
should give due importance to it as well as work on satisfying the existing customer each
time and offer new schemes day by day to attract new ones.
32
References
❖ ACNielsen. (2007). Seek and You Shall Buy. Entertainment and Travel.
viewed 18 January 2007 <http://www2.acnielsen.com/news/20051019.shtml>.
❖ Ajzen, I., & Fishbein, M. (1980). Understanding Attitude and Predicting Social
Behavior. New Jersey: Prentice-Ha
❖ Babin, B. J., Darden, W. R., & Griffin, M. (1994). Work and/or fun: measuring
hedonic and utilitarian shopping value. Journal of Consumer Research, 20,
644-456.
❖ Bruner, G. C., & Hensel, P. J. (1996). Marketing Scales Handbook: a
Compilation of Multi-Item Measures. American Marketing Association,
Chicago, IL 2.
❖ Childers, T. L., Cristopher L. Carr, Joan Peck, & Carson, S. (2001). Hedonic
and Utilitarian Motivations for Online Retail Shopping Behavior. Journal of
Retailing, 77(4), 511-535.
❖ Gardyn, R. (2002). Educated consumers. American Demographics, 24(10), 18-
19.
❖ Ghani, E. K., Said, J., & Nasir, N. (2001). Cross Sectional Studies on on-line
Shopping Among Malaysian Employees. Unpublished Dissertation, University
Technology MARA.
❖ Goldsmith, R. E., & Flynn, L. R. (2004). Psychological and behavioural drivers
of online clothing purchase. Journal of Fashion Marketing and Management,
8(1), 84-95.
❖ Haque, A., Sadeghzadeh, J., & Khatibi, A. (2006). Identifing Potentiality Online
Sales In Malaysia: A Study On Customer Relationships Online Shopping.
Journal of Applied Business Research, 22(4), 119-130
❖ Khatibi, A., Haque, A., & Karim, K. (2006). E-Commerce: A study on Internet
Shopping in Malaysia. Journal of Applied Science 3(6), 696-705.
❖ Kim, E. Y., & Kim, Y. K. (2004). Predicting online purchases intentions for
clothing products. European journal of Marketing, 38, 883-897.
33
❖ Kim, Y. M., & Shim, K. Y. (2002). The influence of Internet shopping mall
characteristics and user traits on purchase intent. Irish Marketing Review,
15(2), 25-34.
❖ Li, H., Daugherty, T., & Biocca, F. (2002). Impact of 3-D advertising on product
knowledge, brand attitude and purchase intention: the mediating role of
presence. Journal of Advertising Research, 31(3), 43-57.
❖ Mathieson, K. (1991). Predicting user intentions: comparing the technology
acceptance model with the theory of planned behavior. Information Systems
Research, 2, 173-191.
❖ Vijayasarathy, L. R. (2002). Product characteristics and Internet shopping
intention. Internet Research: Electronic Networking. Applications and Policy,
12(5), 411-426.
❖ Wolfinbarger, M., & Gilly, M. C. (2001). Shopping Online for Freedom, Control,
and Fun. California Management Review, 43(2), 34-55.
❖ Sorce, P., Perotti, V., & Widrick, S. (2005). Attitude and age differences in
online buying. International Journal of Retail & Distribution Management, 33(2),
122-132.
❖ Turban, E., & Gehrke, D. (2000). Determinants of e-commerce website.
Human Management, 19, 111-120.
❖ Verhoef, P. C., & Langerak, F. (2001). Possible determinants of consumers'
adoption of electronic grocery shopping in The Netherlands. Journal of
Retailing and Consumer Services, 8, 275-285.
❖ Forrester. (2006). ‘Online Retail: Strong, Broad Growth’, viewed 18 January
2007.
<http://www.forrester.com/Research/Document/Excerpt/0,7211,39915,00.html
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