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College Students’ Online Purchase Intention in Big Data Era

  • Ou LiuEmail author
  • Zhonghui Shi
  • Woonkian Chong
  • Ka-Lok Man
  • Chi-On Chan
Conference paper

Abstract

There are huge amounts of data produced and accumulated in the business world every day. Business firms and other organizations are interested in discovering new business insight from the big data through Big Data Analytics (BDA) to increase business performance. This chapter discusses the application of BDA in e-commerce, and its impact on customers’ online purchase intention. We focus on the sample of college students because of the younger generation’s significant online purchasing power. To verify the hypothesized model, a survey method is adopted to collect data, and the Generalized Linear Model is used to analyse the data. The empirical study validates the hypothesized model and reveals the factors that affect customers’ online purchase intention.

Keywords

Big data Big data analytics College student E-commerce Online retailing Purchase intention 

References

  1. 1.
    S.S. Alam, N.M. Yasin, An investigation into the antecedents of customer satisfaction of online shopping. J. Market. Dev. Competitive. 5(1), 71–78 (2010)Google Scholar
  2. 2.
    H. Chen, R.H.L. Chiang, V.C. Storey, Business intelligence and analytics: From Big data to big impact. MIS Q. 36, 1165–1188 (2012)Google Scholar
  3. 3.
    S. Choon-Yin, C. Sharma, An exploration into the factors driving consumers in Singapore towards or away from the adoption of online shopping. Global Bus. Manag. Res. 7(1), 60–73 (2015)Google Scholar
  4. 4.
    E.K. Clemons, How information changes consumer behavior and how consumer behavior determines corporate strategy. J. Manag. Inf. Syst. 25(2), 13–40 (2008)CrossRefGoogle Scholar
  5. 5.
    A.H. Crespo, I.R. Del Bosque, M.M.G. De Los Salmones Sanchez, The influence of perceived risk on Internet shopping behavior: A multidimensional perspective. J. Risk Res. 12(2), 259–277 (2009)CrossRefGoogle Scholar
  6. 6.
    F.D. Davis, User acceptance of information technology: System characteristics, user perceptions and behavioral impacts. Int. J. Man-Machine Stud. 38(3), 475–487 (1993)CrossRefGoogle Scholar
  7. 7.
    S. Fan, R.Y.K. Lau, J.L. Zhao, Demystifying Big data analytics for business intelligence through the lens of marketing mix. Big. Data. Res. 2, 28–32 (2015)CrossRefGoogle Scholar
  8. 8.
    J.K. Gerrikagoitia, I. Castander, F. Rebón, A. Alzua-Sorzabal, New trends of intelligent E-marketing based on web mining for E-shops. Procedia. Soc. Behav. Sci. 175, 75–83 (2015)CrossRefGoogle Scholar
  9. 9.
    P.B. Goes, Big data and IS research. MIS Q. 38(3), iii–viii (2014)Google Scholar
  10. 10.
    C. Hsinchun, R.H.L. Chiang, V.C. Storey, Business intelligence and analytics: From Big data to big impact. MIS Q. 36(4), 1165–1188 (2012)Google Scholar
  11. 11.
    M.E. Johnson, W. Seungjin, E-business and supply chain management: An overview and framework. Product. Operat. Manag. 11(4), 413–423 (2002)CrossRefGoogle Scholar
  12. 12.
    K. Jongeun, Analyzing college students’ online shopping behavior through attitude and intention. Inter. J. Interdiscipl. Soc. Sci. 5(3), 365–376 (2010)Google Scholar
  13. 13.
    K. Jongeun, Developing an empirical model of college students’ online shopping behavior. Inter. J. Interdiscipl. Soc. Sci. 6(10), 81–109 (2012)Google Scholar
  14. 14.
    N. Kumar, I. Benbasat, The influence of recommendations and consumer reviews on evaluations of websites. Inf. Syst. Res. 17(4), 425–439 (2006)CrossRefGoogle Scholar
  15. 15.
    O. Kwon, N. Lee, B. Shin, Data quality management, data usage experience and acquisition intention of big data analytics. Int. J. Inf. Manag. 34, 387–394 (2014)CrossRefGoogle Scholar
  16. 16.
    O. Liu, W.K. Chong, K.L. Man, C.O. Chan, Lecture Notes in Engineering and Computer Science: Proceedings of The International Multi-Conference of Engineers and Computer Scientists 2016. 16–18 March, 2016. The application of big data analytics in business world, (Hong Kong, 2016), pp. 665–667Google Scholar
  17. 17.
    O. Liu, Z. Shi, W.K. Chong, K.L. Man, C.O. Chan, College students’ online purchase intention in big data era, in Transactions on Engineering Technologies, ed. by S-I Ao, H.K. Kim, X. Huang, O. Castillo (Springer, Singapore, 2017), pp. 61–72Google Scholar
  18. 18.
    S. Mithas, M.R. Lee, S. Earley, S. Murugesan, R. Djavanshir, Leveraging big data and business analytics [Guest editors’ introduction]. IT Prof. 15(6), 18–20 (2013)CrossRefGoogle Scholar
  19. 19.
    R. Panda, B. Narayan Swar, Online shopping: An exploratory study to identify the determinants of shopper buying behaviour. Inter. J. Bus. Insight. Trans. 7(1), 52–59 (2013)Google Scholar
  20. 20.
    P. Setia, V. Venkatesh, S. Joglekar, Leveraging digital technologies: How information quality leads to localized capabilities and customer service performance. MIS Q. 37(2), 565–5A4 (2013)Google Scholar
  21. 21.
    P. Sullivan, J. Heitmeyer, Looking at Gen Y shopping preferences and intentions: Exploring the role of experience and apparel involvement. Int. J. Consum. Stud. 32(3), 285–295 (2008)CrossRefGoogle Scholar
  22. 22.
    C.K. Velu, S.E. Madnick, M.W. Van Alstyne, Centralizing data management with considerations of uncertainty and information-based flexibility. J. Manag. Inf. Syst. 30(3), 179–212 (2013)CrossRefGoogle Scholar
  23. 23.
    V. Viswanathan, V. Jain, A dual-system approach to understanding “generation Y” decision making. J. Consum. Mark. 30(6), 484–492 (2013)CrossRefGoogle Scholar
  24. 24.
    W.U. Wann-Yih, K.E. Ching-Ching, An online shopping behavior model integrating personality traits, perceived risk, and technology acceptance. Soc. Behav. Persona. Int. J. 43(1), 85–97 (2015)Google Scholar
  25. 25.
    L. Xia, H. Mengqiao, G. Fang, X. Peihong, An empirical study of online shopping customer satisfaction in China: A holistic perspective. Int. J. Retail Distrib. Manag. 36(11), 919–940 (2008)CrossRefGoogle Scholar
  26. 26.
    T. Zhang, R. Agarwal, J.H.C. Lucas, The value of It-enabled retailer learning: Personalized product recommendations and customer store loyalty in electronic markets. MIS Q. 35(4), 859–8A7 (2011)Google Scholar
  27. 27.
    Y. Zhao, D. Li, L. Pan, Cooperation or competition: An evolutionary game study between commercial banks and big data-based e-commerce financial institutions in China. Discret. Dyn. Nat. Soc. 890972, 1–8 (2015)MathSciNetGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2017

Authors and Affiliations

  • Ou Liu
    • 1
    Email author
  • Zhonghui Shi
    • 1
  • Woonkian Chong
    • 1
  • Ka-Lok Man
    • 2
  • Chi-On Chan
    • 3
  1. 1.International Business School SuzhouXi’an Jiaotong-Liverpool UniversitySuzhouChina
  2. 2.Department of Computer Science & Software EngineeringXi’an Jiaotong-Liverpool UniversitySuzhouChina
  3. 3.Hong Kong Shue Yan UniversityHong KongChina

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