Advertisement

Impact of Perceived Connectivity on Intention to Use Social Media: Modelling the Moderation Effects of Perceived Risk and Security

  • Samuel Fosso WambaEmail author
  • Shahriar Akter
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9844)

Abstract

The main objective of this study is to assess the impact of perceived connectivity (PC) on the intention to use (IU) social media in organizations, as well as the moderating effects of perceived risk (PR) and perceived security (PS) on this relationship. Data were collected from 2,556 social media users across Australia, Canada, India, the UK, and the US to test our proposed research model. Our results found that PC has a significant positive effect on the IU social media in organizations, and non-significant moderating effects of PR and PS. The study concludes with the implications for practice and research.

Keywords

Social media Adoption and use Intention Perceived connectivity Perceived risk Perceived security Moderation 

References

  1. 1.
    Liang, T.-P., Turban, E.: Introduction to the special issue social commerce: a research framework for social commerce. Int. J. Electron. Commer. 16(2), 5–14 (2011)CrossRefGoogle Scholar
  2. 2.
    IBM: Social Commerce Defined. IBM (2009)Google Scholar
  3. 3.
    Munnukka, J., Järvi, P.: Perceived risks and risk management of social media in an organizational context. Electron. Markets 24(3), 219–229 (2014)CrossRefGoogle Scholar
  4. 4.
    Culnan, M.J., McHugh, P.J., Zubillaga, J.I.: How large US companies can use Twitter and other social media to gain business value. MIS Q. Executive 9(4), 243–259 (2010)Google Scholar
  5. 5.
    Burke, W.Q., Fields, D.A., Kafai, Y.B.: Entering the clubhouse: case studies of young programmers joining the online scratch communities. J. Organ. End User Comput. 22, 21–35 (2010)CrossRefGoogle Scholar
  6. 6.
    Stephen, A.T., Toubia, O.: Deriving value from social commerce networks. J. Market. Res. 47(2), 215–228 (2010)CrossRefGoogle Scholar
  7. 7.
    Benbasat, I.: A program of studies to improve the communication between customers and online stores. In: Galletta, D.F., Zhang, P. (eds.) Human-Computer Interaction and Management Information Systems: Application. Advances in Management Information Systems (2006)Google Scholar
  8. 8.
    Trinchera, L.: Unobserved heterogeneity in structural equation models: a new approach to latent class detection in PLS path modeling, p. 338. Universita degli Studi di Napoli Federico II, Napoli(2007)Google Scholar
  9. 9.
    Strader, T.J., Ramaswami, S.N., Houle, P.A.: Perceived network externalities and communication technology acceptance. Eur. J. Inf. Syst. 16(1), 54–65 (2007)CrossRefGoogle Scholar
  10. 10.
    Fosso Wamba, S., Chatfield, A.T.: A contingency model for creating value from RFID supply chain network projects in logistics and manufacturing environments. Eur. J. Inf. Syst. 18(6), 615–636 (2009)CrossRefGoogle Scholar
  11. 11.
    Gritzalis, D., et al.: History of information: the case of privacy and security in social media. In: Proceedings of the History of Information Conference (2014)Google Scholar
  12. 12.
    Akter, S., Wamba, S.F.: Big data analytics in e-commerce: a systematic review and agenda for future research. Electron. Markets 26, 1–22 (2016)CrossRefGoogle Scholar
  13. 13.
    Martin, K.E.: Ethical issues in the big data industry. MIS Q. Executive (2015, Forthcoming)Google Scholar
  14. 14.
    Pantelis, K., Aija, L.: Understanding the value of (big) data. In: 2013 IEEE International Conference on Big Data (2013)Google Scholar
  15. 15.
    George, G., Haas, M.R., Pentland, A.: Big data and management. Acad. Manag. J. 57(2), 321–326 (2014)CrossRefGoogle Scholar
  16. 16.
    Davis, F.D.: Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 13(3), 319–340 (1989)CrossRefGoogle Scholar
  17. 17.
    Leung, J., Cheung, W., Chu, S.-C.: Aligning RFID applications with supply chain strategies. Inf. Manag. 51, 260–269 (2014)CrossRefGoogle Scholar
  18. 18.
    Luo, X., Gurung, A., Shim, J.P.: Understanding the determinants of user acceptance of enterprise instant messaging: an empirical study. J. Organ. Comput. Electron. Commer. 20(2), 155–181 (2010)Google Scholar
  19. 19.
    Guadagnoli, E., Velicer, W.: Relation of sample size to the stability of component patterns. Psychol. Bull. 103, 265–275 (1988)CrossRefGoogle Scholar
  20. 20.
    Chin, W.W.: The partial least squares approach for structural equation modeling (1998)Google Scholar
  21. 21.
    Chin, W.W.: How to write up and report PLS analyses. In: Handbook of Partial Least Squares, pp. 655–690 (2010)Google Scholar
  22. 22.
    Fornell, C., Larcker, D.F.: Evaluating structural equation models with unobservable variables and measurement error. J. Market. Res. 18, 39–50 (1981)CrossRefGoogle Scholar
  23. 23.
    Rahimnia, F., Hassanzadeh, J.F.: The impact of website content dimension and e-trust on e-marketing effectiveness: the case of Iranian commercial Saffron corporations. Inf. Manag. 50(5), 240–247 (2013)CrossRefGoogle Scholar
  24. 24.
    Fosso Wamba, S., Carter, L.: Twitter adoption and use by SMEs: an empirical study. In: The 46 Hawaii International Conferences on System Sciences (HICSS), Maui, Hawaii, US (2013)Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2016

Authors and Affiliations

  1. 1.Toulouse Business SchoolToulouseFrance
  2. 2.Faculty of BusinessUniversity of WollongongWollongongAustralia

Personalised recommendations