Customer Attitude Towards E-Commerce using Naive Bayes Algorithm : Machine Learning Approach

B.Naveena Devi; Dr.Y.Rama Devi; Dr.R.Rajeswara Rao

doi:https://doi.org/10.14445/22315373/IJMTT-V55P538

International Journal of Mathematics Trends and Technology

Research Article | Open Access | Download PDF

Volume 55 | Number 4 | Year 2018 | Article Id. IJMTT-V55P538 | DOI : https://doi.org/10.14445/22315373/IJMTT-V55P538

Customer Attitude Towards E-Commerce using Naive Bayes Algorithm : Machine Learning Approach

B.Naveena Devi, Dr.Y.Rama Devi, Dr.R.Rajeswara Rao

Citation :

B.Naveena Devi, Dr.Y.Rama Devi, Dr.R.Rajeswara Rao, "Customer Attitude Towards E-Commerce using Naive Bayes Algorithm : Machine Learning Approach," International Journal of Mathematics Trends and Technology (IJMTT), vol. 55, no. 4, pp. 294-304, 2018. Crossref, https://doi.org/10.14445/22315373/IJMTT-V55P538

Abstract

In the era of the e-commerce transmission and its services provided to the customer over the Internet, the Internet is commonly used by both customers and business to buy and sell their goods and services across the world. This study focuses on the factors influencing customer decision and attitudes towards adopting online shopping in India. This study is mainly emphasizes on how demographic variable (age, income and occupation) & Psychological factors (Motivation, Perception, Learning, Belief and attitudes) affect customer buying behavior towards E-commerce. The research questionnaire and hypotheses were developed on review of the literature. Based on the research objectives, a structured questionnaire with 30variables, mainly with a 5-point Likert scale was used. The survey was carried out on selected 1500 customers using E-Commerce applications in India. For data collection, random sampling was adopted. Data mining Tools and Techniques i.e. WEKA, R programming, R Rattle and R Cmdr. have been used to analyze the data. Multinomial Logistics model is applied for predicting customer buying behaviours. Data mining techniques like Principle Component Analysis and factor analysis is applied for Data Dimension Reduction. Originally there were 30 factors influencing attributes/variables which were reduced to 7 after the PCA technique is applied. This research result represent that customer region is the variable that is highly influencing the customer buying behavior and the E- Commerce. The second reason is the customer income which is also influencing the customer buying behavior and E- Commerce next to the customer Region to some extent. The rest of the demographic variables are not showing significant influence.

Keywords

Ecommerce, Customer Perception, Customer attitude, Decision Tree, Ordinal /Multinomial Logistic, Naive Bayes Algorithm, Confusion Matrices, sensitivity, specificity.

References

[1] Akinci, S., Aksoy, S., &Atilgan, E. (2004). Adoption of internet banking among sophisticated consumer segments in an advanced developing country. International Journal of Bank Marketing, 22 (3), 212-232.
[2] Bakos, J.Y. (1997), “Reducing buyer search costs: implications for electronic marketplaces”, Management Science, 43(12), pp. 1676- 1692.
[3] Chellappa, K.R and Pavlou A.P. (2002), “Perceived information security, financial liability and consumer trust in electronic commerce transactions”, Logistics Information Management, 15(5), pp. 358-368.
[4] Chiles, T.H. and McMackin, J.F. (1996), “Integrating variable risk preferences, trust, and transaction cost economics”. Academy of Management Review, 21, 73-99.
[5] Chung-Hoon P., Y.-G. Kim, (2003), “Identifying key factors affecting consumer purchase behavior in an online shopping context”, International Journal of Retail and Distribution Management, 31(1), pp. 16-29.
[6] Clarke, K. (2001), "What price on loyalty when a brand switch is just a click away?” Qualitative Market Research, 4(3), pp. 160-168.
[7] Conner M. and Armitage J.C. (1998), “Extending the theory of planned behavior: A review and avenues for further research”, Journal of Applied Social Psychology, Vol. 28, pp. 1430.
[8] Constantinides, E. (2004)., “Influencing the online consumer’s behavior: the Web experience”. Internet Research. Vol. 14, No. 2, pp. 111-126.
[9] Currall, S. and Judge, T. (1995), “Measuring Trust Between Organizational Boundary Role Persons,” Organizational Behavior and Human Decision Processes (64), p. 151-170.
[10] Cuneyt, K. Gautam, B. (2004). The impacts of quickness, price, payment risk, and delivery issues on on-line shopping, Journal of Socio-Economics, Vol.33, PP.241–251.
[11] Dawson, J. (2000). Retailing at Century End: Some Challenges for Management and Research. International Review of Retail Distribution and Consumer Research vol (2): 119-124.
[12] Dekimpe, M.G., Steenkamp, J.-B.E.M. and Mellens, M. (1997), “Decline and variability in brand loyalty’’, International Journal of Research in Marketing, Vol. 14, pp. 405-420.
[13] Dillon, T. D., and Reif, H. L. (2004). Factors influencing consumer’s e-commerce commodity purchases. Information Technology, Journal of Learning and Performance, 22(2), 1-12. Hasan, H., and Rahim, S. A. (2004). Factors affecting online purchasing behavior. Journal of Communication, 24, 1-19. Jarvenpaa, S. L., & Todd, P. A. (1996-97). Consumer reactions to electronic shopping on the World Wide Web. International Journal of Electronic Commerce, 1(2), 59-88.
[14] Kesh, S., Ramanujan, S. and Nerur, S. (2002) “A framework for analyzing ecommerce security”, Information Management & Computer Security, 10 (4)pp. 149-458.
[15] Liao, Z. and Cheung, M.T. (2002), “Internet-based E-Banking and Consumer Attitudes: An Empirical Study”. Information and Management, Vol. 39, pp. 283–295.
[16] Rotchanakitumnuai, S., &Speece, M. (2003). Barriers to internet banking adoption: a qualitative study among corporate consumers in Thailand. International Journal of Bank Marketing, 21 (6/ 7), 312-323.
[17] Roehl, W.S. and Fesenmaier, D.R. 1992. Risk Perceptions and pleasure travel: An exploratory analysis. Journal of Travel Research, 30(4): 17–26.
[18] Schlosser, A.E. (2003a). Experiencing products in the virtual world: the role of goal and imagery in influencing attitudes. Journal of Consumer Research, Vol. 30, pp. 184-198.
[19] Tan, M. &Teo, T. S. H. 2000. Factors influencing the adoption of Internet banking. Journal of the Association for Information Systems. Vol. 1 (5), pp. 1-42.
[20] Wang, Y., Wang, Y., Lin, H., & Tang, T. (2003). Determinants of user acceptance of internet banking: an empirical study. International Journal of Bank Marketing, 14 (5), 501-519.