We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 December 2024
Reseach Article

Challenges in Transition to m Commerce in Rural India

by Nishi Malhotra, Pankaj Shah, Saravanan
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 174 - Number 4
Year of Publication: 2017
Authors: Nishi Malhotra, Pankaj Shah, Saravanan
10.5120/ijca2017915387

Nishi Malhotra, Pankaj Shah, Saravanan . Challenges in Transition to m Commerce in Rural India. International Journal of Computer Applications. 174, 4 ( Sep 2017), 39-47. DOI=10.5120/ijca2017915387

@article{ 10.5120/ijca2017915387,
author = { Nishi Malhotra, Pankaj Shah, Saravanan },
title = { Challenges in Transition to m Commerce in Rural India },
journal = { International Journal of Computer Applications },
issue_date = { Sep 2017 },
volume = { 174 },
number = { 4 },
month = { Sep },
year = { 2017 },
issn = { 0975-8887 },
pages = { 39-47 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume174/number4/28399-2017915387/ },
doi = { 10.5120/ijca2017915387 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:21:18.362265+05:30
%A Nishi Malhotra
%A Pankaj Shah
%A Saravanan
%T Challenges in Transition to m Commerce in Rural India
%J International Journal of Computer Applications
%@ 0975-8887
%V 174
%N 4
%P 39-47
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

With advent of m commerce the marketing and channel environment in rural India has changed drastically. India being a predominantly agricultural economy has lot of potential for m commerce marketing. With advent of legislative policy changes such as Digital India programme, m commerce is no option but is a necessity. With introduction of Payment banks, mobile applications and mobile commerce platforms rural India cannot remain in isolation. This descriptive research paper is aimed at studying the reasons for decreased mobile usage in rural India. This paper aims at providing a comprehensive literature review of relevant research work done in this field. Hidden Markov Model is an approach to study the temporal sequence of behavior in channel migration and channel choice. Various empirical models have been derived for different kinds of data distributions including univariate, bivariate and multivariate data distributions. An descriptive study to evaluate various kinds of models for different kinds of data distribution is aimed at identifying the best kind of Hidden Markov Model for studying the issue of channel migration in Rural India. The study concludes that Hidden Markov Model based on Multinomial Logit Regression approach is the best model to study the given problem.

References
  1. Ansari, A., Montoya, R., & Netzer, O. (2012). Dynamic learning in behavioral games: A hidden Markov mixture of experts approach. Quantitative Marketing and Economics, 10(4), 475–503. https://doi.org/10.1007/s11129-012-9125-8
  2. Ascarza, E., & Hardie, B. G. S. (2013). A Joint Model of Usage and Churn in Contractual Settings. Marketing Science, 32(February), 570–590. https://doi.org/10.1287/mksc.2013.0786
  3. Bansal, H. S., & Taylor, S. F. (1999). A Model of Consumer Switching Behavior in the Services Industry. Journal of Service Retail, 2(2), 200–218.
  4. Care, E. C., Education, E., Education, N. F., Blackboard, O., Education, S., Vidyalaya, N., … Education, M. (1992). National Policy on Education 1986, 2–189. Retrieved from http://mhrd.gov.in/sites/upload_files/ mhrd/files/document-reports/POA_1992.pdf
  5. Chang, C. (2012). Multichannel Marketing and Hidden Markov Models. Retrieved from http://hdl.handle.net/1773/20625
  6. Deshmukh, S. P. ., Deshmukh, P., & Thampi, G. T. . (2013). Transformation from E-commerce to M-commerce in Indian Context. International Journal of Computer Science Issues (IJCSI), 10(4), 55–60. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&db=aci&AN=89863759&site=ehost-live
  7. Green, P. E., & Frank, R. E. (1966). Bayesian Statistics and Marketing Research. Journal of the Royal Statistical Society: Series C (Applied Statistics) (Vol. 15). Retrieved from http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=6025342&site=ehost-live
  8. Hogarth, J. M. (2006). Financial education and economic development. Improving Financial Literacy: International Conference Hosted by the Russian G8 Presidency in Cooperation with the OECD, 1–34.
  9. M-commerce – The Next Generation Commerce. (2016), (December), 1–28.
  10. Moon, S., Kamakura, W. A., & Ledolter, J. (2007). Estimating Promotion Response When Competitive Promotions Are Unobservable. Journal of Marketing Research, 44(3), 503–515. https://doi.org/10.1509/jmkr.44.3.503
  11. Muthukumar, S., & Muthu, D. N. (2015). The Indian kaleidoscope: emerging trends in M-Commerce. Ijarcce, 4(1), 50–56. https://doi.org/10.17148/IJARCCE.2015.4110
  12. Netzer, O., Lattin, J. M., & Srinivasan, V. (2008). A Hidden Markov Model of Customer Relationship Dynamics. Marketing Science, 27(2), 185–204. https://doi.org/10.1287/mksc.1070.0294
  13. Ngai, E. W. T., & Gunasekaran, A. (2007). A review for mobile commerce research and applications. Decision Support Systems, 43(1), 3–15. https://doi.org/10.1016/j.dss.2005.05.003
  14. Prasad, M. R., Gyani, J., & P.R.K.Murti. (2012). Mobile Cloud Computing : Implications and Challenges. Journal of Information Engineering and Applications, 2(7), 7–16.
  15. Rabiner, L., & Juang, B. (1986). Introduction to hidden Markov models. Ieee Assp Mag., 1–15. https://doi.org/10.1002/0471250953.bia03as18
  16. Suryawanshi, J. R. (2014). E-Commerce through Smart Devices in Emerging India. International Journal of Research in Computer and Communication Technology, 3(7), 712–714. Retrieved from http://www.ijrcct.org/index.php/ojs/article/view/779/pdf
  17. Tiwari, R., Buse, S., & Herstatt, C. (2006). Special Feature : Converging Technologies Opportunities through technology convergence for business services. Tech Monitor, 38–45.
  18. Zhang, A., Jun, Z., & Zhang, B. (2014). Max-Margin Infinite Hidden Markov Models. Proceedings of the 31st International Conference on Machine Learning, 32, 315–323.
  19. Zucchini, W., & Macdonald, I. L. (2009). Model structure, properties and Preliminaries : mixtures and Markov.
Index Terms

Computer Science
Information Sciences

Keywords

m Commerce Hidden Markov Model Literature Review