Predict online customer satisfaction level on the basis of e-commerce services and age group

  • Abhishek Kanani
  • Sanket Chodavadiya
  • Maharashi . Prajapati
  • Shanti Verma


The purpose of study is to develop an
understanding the how many customer satisfied with the
E-commerce services. So there are lots of scopes to an
analyze user's data to find unknown facts of E-commerce.
To achieve objective of this paper authors conduct a
survey named Customer Satisfaction level in India. They
collected a sample of 520 users in one month time duration
via online medium (Google Forms). The major difference
between online and traditional shopping is that in online
shopping there is no touch, feel and trust. So the consumer
gets afraid to pay first before receiving the product. In this
paper authors try to find out relationship between type of
services and satisfaction level in Indian consumer in Ecommerce
services. The result of experiment shows that P
value of customers Ages and Satisfaction level is 0.5817
which is significant at 95% confidence and P value of
customers Services and Satisfaction level is 0.5988. It tells
that consumer satisfaction level according to the type of

Keywords: Survey, E-shopping, E-commerce, Frequency distribution, Test of independence, Data mining


[1] GanTeck Wei, ShirlyKho, Wahidah Husain, ZurinahniZainol, " A
Study of Customer Behaviour Through Web Mining" Journal of
Information Sciences and Computing Technologies Volume 2, Issue 1
2015 page 103-107
[2] Franke, Todd Michael, Timothy Ho, and Christina A. Christie.
"The Chi-Square Test Often Used and More Often Misinterpreted."
American Journal of Evaluation 33.3 (2012): 448458.
[3] McHugh, Mary L. "The chi-square test of independence."
BiochemiaMedica 23.2 (2013): 143-149.
[4] Tanzila Saba, "Implications of e-learning systems and self
efficiency on student outcomes: a model approach," Human centric
computing and Information Sciences: a springeropen journal, 2012,
pp. 02-06.
[5]Michail N. Giannakos, Spyros Doukakis, Ilias 0. Pappas, Nikos
Adamopoulos ,"Investigating teachers' confidence on t echnological
pedagogical and content knowledge: an initial validation of TPACK
scales in K-12 computing education context" J. Comput. Educ. (2015)
2(1), pp. 43-59.
[6] Li, Xining, Jiazao Lin, and Lian Li. "On the design of a mobile
agent environment for context-aware M-commerce." Computer
Science and Information Technology (ICCSIT), 2010 3rd IEEE
International Conference on.Vol. 3.IEEE, 2010.
[7] Manochehri, Naser-nick, and MrYousufAlhinai. "Mobile phone
users attitude towards Mobile Commerce (m-commerce) and Mobile
Services in Oman." 2006 2nd IEEE/IFIP International Conference in
Central Asia on Internet.IEEE, 2006.
[8] Jiao, Ming-hai, et al. "Research on personalized recommendation
optimization of E -commerce system based on customer trade
behaviour data." Control and Decision Conference (CCDC), 2016
Chinese.IEEE, 2016.
[9] Wang, Yuqi, Wenqian Shang, and Zhenzhong Li. "The application
of factorization machines in user behavior prediction." Computer and
Information Science (ICIS), 2016 IEEE/ACIS 15th International
Conference on.IEEE, 2016.
[10] Verma, Shanti. "Predicting Effect of Past Qualification in S
uccessful Honoring Master of Computer Applications Degree —A
Neural Network Approach." International Journal of Computing
Academic Research" (IJCAR) 5.1 (2016): 56-62.
[11] jigneshdoshi, Shanti verma. "Correlation between Text book
usage and Academic performance of student in Higher Education
using 'It '." International Conference On Communication And
Networks (COMNET-2015-16). Vol. 508. Springer singapore, 2016
0 Views | 0 Downloads
How to Cite
Kanani, A., Chodavadiya, S., Prajapati, M., & Verma, S. (2019). Predict online customer satisfaction level on the basis of e-commerce services and age group. Asian Journal For Convergence In Technology (AJCT). Retrieved from

Most read articles by the same author(s)