Developing a Theoretical Model to Examine Consumer Acceptance Behavior of Mobile Shopping

  • Hannah R. MarriottEmail author
  • Michael D. Williams
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9844)


Mobile activity is increasing in popularity with Smartphones and Tablets being used for a variety of daily online activities. However, the number of mobile users utilizing mobile devices for the purpose of shopping is relatively low and there has been limited theoretical research examining the acceptance behavior of consumers in the UK. This research aims to develop a theoretically grounded adoption model to examine UK consumers’ mobile shopping acceptance behavior. Through consideration into findings from existing research, a theoretically grounded model is developed by extending UTAUT2 with perceived risk, trust, mobile affinity and innovativeness. This theoretical model can subsequently be empirically tested with data gathered from the UK.


Acceptance Consumer behavior Mobile shopping (m-shopping) Perceived risk UK UTAUT2 


  1. 1.
    Büllinger, F., Stamm, P.: Mobile Commerce via Smartphone & Co: Analyse und Prognose des zukünftigen Marktes aus Nutzerperspektive. Hg. v. Verbraucherzentrale Bundesverband eV. Wissenschaftliches Institut für Infrastruktur und Kommunikationsdienste GmbH. Büyüközkan, G. (2009) Determining the mobile commerce user requirements using an analytic approach. Computer Standards and Interfaces 31(1), 144–152 (2012)Google Scholar
  2. 2.
    Holmes, A., Byrne, A., Rowley, J.: Mobile shopping behaviour: insights into attitudes, shopping process involvement and location. Int. J. Retail Distrib. Manag. 42(1), 25–39 (2014)CrossRefGoogle Scholar
  3. 3.
    Chong, A.Y.L.: Predicting m-commerce adoption determinants: a neural network approach. Expert Syst. Appl. 40(2), 523–530 (2013)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Groß, M.: Exploring the acceptance of technology for mobile shopping: an empirical investigation among Smartphone users. Int. Rev. Retail, Distrib. Consum. Res. 25(3), 215–235 (2014)CrossRefGoogle Scholar
  5. 5.
    Yang, K., Kim, H.Y.: Mobile shopping motivation: an application of multiple discriminant analysis. Int. J. Retail Distrib. Manag. 40(10), 778–789 (2012)CrossRefGoogle Scholar
  6. 6.
    Mulpuru, S., Johnsob, C., Wu, S., Roberge, D., Naparstek, L.: US Mobile Retail Forecast, 2012 to 2017. Forrester Research (2014).
  7. 7.
    Hung, M.C., Yang, S.T., Hsieh, T.C.: An examination of the determinants of mobile shopping continuance. Int. J. Electron. Bus. Manag. 10(1), 29 (2012)Google Scholar
  8. 8.
    Fishbein, M., Ajzen, I.: Belief, Attitude, Intention and Behavior: An Introduction to Theory and Research. Addison-Wesley, Reading (1975)Google Scholar
  9. 9.
    Ajzen, I.: The theory of planned behavior. Organ. Behav. Hum. Decis. Process. 50(2), 179–211 (1991)CrossRefGoogle Scholar
  10. 10.
    Davis, F.D.: Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 13(3), 319–340 (1989)CrossRefGoogle Scholar
  11. 11.
    Venkatesh, V., Morris, M.G., Davis, G.B., Davis, F.D.: User acceptance of information technology: toward a unified view. MIS Q. 27(3), 425–478 (2003)Google Scholar
  12. 12.
    Venkatesh, V., Thong, J.Y., Xu, X.: Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS Q. 36(1), 157–178 (2012)Google Scholar
  13. 13.
    Rogers, E.: Diffusion of Innovations, 4th edn. Free Press, New York (1995)Google Scholar
  14. 14.
    Aldás-Manzano, J., Ruiz-Mafe, C., Sanz-Blas, S.: Exploring individual personality factors as drivers of M-shopping acceptance. Ind. Manag. Data Syst. 109(6), 739–757 (2009)CrossRefGoogle Scholar
  15. 15.
    Bigné, E., Ruiz-Mafé, C., Sanz-Balz, S.: Key drivers of mobile commerce adoption. An exploratory study of Spanish mobile users. JTAER 2(2), 48–60 (2007)Google Scholar
  16. 16.
    Zhang, L., Zhu, J., Liu, Q.: A meta-analysis of mobile commerce adoption and the moderating effect of culture. Comput. Hum. Behav. 28(5), 1902–1911 (2012)CrossRefGoogle Scholar
  17. 17.
    Nassuora, A.B.: Understanding factors affecting the adoption of m-commerce by consumers. J. Appl. Sci. 13(6), 913 (2013)CrossRefGoogle Scholar
  18. 18.
    Foon, Y.S., Fah, B.C.Y.: Internet banking adoption in Kuala Lumpur: an application of UTAUT model. Int. J. Bus. Manag. 6(4), 161 (2011)Google Scholar
  19. 19.
    Wong, C.H., Lee, H.S., Lim, Y.H., Chua, B.H., Tan, G.W.H.: Predicting the consumers’ intention to adopt mobile shopping: an emerging market perspective. Int. J. Netw. Mob. Technol. 3(3), 24–39 (2012)Google Scholar
  20. 20.
    Grandón, E.E., Nasco, S.A., Mykytyn, P.P.: Comparing theories to explain e-commerce adoption. J. Bus. Res. 64(3), 292–298 (2011)CrossRefGoogle Scholar
  21. 21.
    Mathieson, K.: Predicting user intentions: comparing the technology acceptance model with the theory of planned behavior. Inf. Syst. Res. 2(3), 173–191 (1991)CrossRefGoogle Scholar
  22. 22.
    Williams, M.D., Rana, N.P., Dwivedi, Y.K., Lal, B.: Is UTAUT really used or just cited for the sake of it? A systematic review of citations of UTAUT’s originating article. In: ECIS, June 2011Google Scholar
  23. 23.
    Matthews, T., Pierce, J., Tang, J.: No smart phone is an island: the impact of places, situations, and other devices on smart phone use. IBM RJ10452 (2009)Google Scholar
  24. 24.
    Jacoby, J., Kaplan, L.B.: The components of perceived risk. Adv. Consum. Res. 3(3), 382–383 (1972)Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2016

Authors and Affiliations

  1. 1.School of ManagementSwansea UniversitySwanseaUK

Personalised recommendations