Impact of Consumer Gender on Expenditure Done in Mobile Shopping Using Test of Independence

Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 9)


In India, a number of mobile users have grown exponentially in the last decade. Now people spend more time with smartphones rather than personal meeting. Mobile commerce is the growing area of research nowadays. Most of the companies provide better pricing in mobile applications so they involve customer in mobile shopping. In this paper, the authors conduct an online survey on smartphone users in India. They try to find out that the gender of customer is dependent on the amount spent in mobile shopping. The authors analyze 258 data sets and perform test of intendance both parametric and nonparametric. The result of survey gives p value = 0.373 for parametric test and p value = 0.386 for nonparametric test. These results show that customer gender and expenditure done in mobile shopping are related to each other.


Mobile shopping Mobile commerce Chi-square test Fisher’s exact test 


  1. 1.
    Jacinth Evangeline S, Subramanian KM, Venkatachalam K (2013) Survey on personal mobile commerce pattern mining and prediction. Int J Adv Res Comput Eng Technol (IJARCET) 2(12):3163–3167Google Scholar
  2. 2.
    Moghadam AD, Jandaghi A, Safavi SO (2015) The probability of predicting e-customer’s buying pattern based on personality type 4(1):18781–18785Google Scholar
  3. 3.
    Dhanalakshmi D, KomalaLakshmi J (2014) A survey on data mining research trends. Int J Eng Comput Sci 3(10):8911–8919Google Scholar
  4. 4.
    Ghode MPP (2014) Survey on personal mobile commerce pattern mining and prediction. Int J Technol Res Eng 1(8):538–540Google Scholar
  5. 5.
    Wei GT, Kho S, Husain W, Zainol Z (2015) A study of customer behaviour through web mining. J Inform Sci Comput Technol 2(1):103–107Google Scholar
  6. 6.
    Mahdavian Maryam, Mostajeran Fahimeh (2013) Studying key users’ skills of ERP system through a comprehensive skill measurement model. Int J Adv Manuf Technol 69(9-12):1981–1999CrossRefGoogle Scholar
  7. 7.
    Badri M et al (2014) Students’ intention to take online courses in high school: a structural equation model of causality and determinants. Educ Inf Technol 1–27Google Scholar
  8. 8.
    Yong L (2009) Applications of Chi-square test and contingency table analysis in customer satisfaction and empirical analyses. In: IEEE computer society international conference on innovation management, pp 105–107Google Scholar
  9. 9.
    Duffett RG (2015) The influence of Facebook advertising on cognitive attitudes amid Generation Y. Electron Commer Res 15(2):243–267Google Scholar
  10. 10.
    Giannakos MN et al (2015) Investigating teachers’ confidence on technological pedagogical and content knowledge: an initial validation of TPACK scales in K-12 computing education context. J Comput Educ 2(1):43–59Google Scholar
  11. 11.
    Gohary A, Hanzaee KH (2014) Personality traits as predictors of shopping motivations and behaviors: a canonical correlation analysis. Arab Econ Bus J 9(2):166–174CrossRefGoogle Scholar
  12. 12.
    Turkyilmaz CA, Erdem S, Uslu A (2015) The effects of personality traits and website quality on online impulse buying. Procedia-Soc Behav Sci 175:98–105Google Scholar
  13. 13.
    Muthukumar S, Muthu N The Indian kaleidoscope: emerging trends in M-commerceGoogle Scholar
  14. 14.
    Boutsis I, Karanikolaou S, Kalogeraki V (2015) Personalized event recommendations using social networks. In: 2015 16th IEEE international conference on mobile data management (MDM), vol. 1. IEEEGoogle Scholar
  15. 15.
    Nithya J, Geetha R (2012) Agent-based data mining in mobile commerce: an overview. IJCSET |2(4):1065–1068Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.L J Institute of Computer ApplicationsAhmedabadIndia
  2. 2.K S School of Business StudiesAhmedabadIndia

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