An Empirical Study of the Factors Affecting Mobile Social Network Service Use

Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 275)

Abstract

This research analyses the application of Mobile Social Network Services (MSNSs) in regard to their nature of being new ICT (Information and Communication Technology) tools. It is recognised that MSNSs are fast, responsive technologies centred on facilitating mobile commerce. This research aims to identify a number of the factors impacting MSNSs acceptance and usage in the context of the Kingdom of Saudi Arabia. In an attempt to achieve sound insight into the market of Saudi Arabia in relation to mobile communication, which is recognised as being a very valuable sector, a survey was carried out targeting a sample of 363 citizens, with a suggested conceptual model based on the UTAUT framework tested. The findings of this study indicate that performance expectancy is the most important offsetting element, with final cost and effort expense following subsequently. Nevertheless, social influence is not impacted in regard to the intention to utilise MSNSs.

Keywords

Saudi Arabia Mobile UTAUT MSNS 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Kourouthanassis, P.E., Giaglis, G.M.: Introduction to the Special Issue Mobile Commerce: The Past, Present, and Future of Mobile Commerce Research. International Journal of Electronic Commerce 16(4), 5–18 (2012)CrossRefGoogle Scholar
  2. 2.
    Mohan, S., Agarwal, N., Dutta, A.: Social Networks Meet Mobile Networks. IEEE Communications Magazine 50, 72–73 (2012)Google Scholar
  3. 3.
    CITC. The ICT Sector in the Kingdom of Saudi Arabia (2011)Google Scholar
  4. 4.
    De Vere, K.: Google study finds affluent Middle East countries among most enthusiastic smartphone users (2012), http://www.insidemobileapps.com/2012/05/23/google-study-finds-affluent-middle-east-countries-among-most-enthusiastic-smartphone-users/ (accessed November 26, 2012)
  5. 5.
    Crum, C.: Google Analyzes How People Use Smartphones In Different Countries (2012), http://www.webpronews.com/google-analyzes-how-people-use-smartphones-in-different-countries-2012-05 (accessed November 26, 2012)
  6. 6.
    Alkhunaizan, A., Love, S.: Effect of Demography on Mobile Commerce Frequency of Actual Use in Saudi Arabia. In: Rocha, Á., Correia, A.M., Wilson, T., Stroetmann, K.A. (eds.) Advances in Information Systems and Technologies. AISC, vol. 206, pp. 125–131. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  7. 7.
    McCarra, D.: Facebook by the numbers: 845 million users sharing 100 billion friendships (2012)Google Scholar
  8. 8.
    Mike, S.: WhatsApp has over 200 million monthly users (2013), http://www.androidauthority.com/whatsapp-reached-200-million-user-milestone-191910/ (accessed April 18, 2013)
  9. 9.
    BBC. Facebook posts loss despite 32% rise in revenues (2012), http://www.bbc.co.uk/news/business-20051654 (accessed November 30, 2012)
  10. 10.
    Emarketer. Twitter Forecast Up After Strong Mobile Showing (2013), http://www.emarketer.com/Article/Twitter-Forecast-Up-After-Strong-Mobile-Showing/1009763 (accessed April 22, 2013)
  11. 11.
    Min, Q., Ji, S., Qu, G.: Mobile commerce user acceptance study in China: a revised UTAUT model. Tsinghua Science & Technology 13(3), 257–264 (2008)CrossRefGoogle Scholar
  12. 12.
    Venkatesh, V., Morris, M.G., Davis, G.B., Davis, F.D.: User acceptance of information technology: Toward a unified view. MIS Quarterly, 425–478 (2003)Google Scholar
  13. 13.
    Van der Heijden, H.: User acceptance of hedonic information systems. MIS Quarterly, 695–704 (2004)Google Scholar
  14. 14.
    Dulle, F.W., Minishi-Majanja, M.: The suitability of the Unified Theory of Acceptance and Use of Technology [UTAUT] model in open access adoption studies. Information Development 27(1), 32 (2011)CrossRefGoogle Scholar
  15. 15.
    Al-Shafi, S., Weerakkody, V.: Factors affecting e-government adoption in the state of Qatar (2010)Google Scholar
  16. 16.
    Al-Shafi, S., Weerakkody, V.: The use of wireless internet parks to facilitate adoption and diffusion of e-government services: an empirical study in Qatar (2008)Google Scholar
  17. 17.
    Kim, S., Garrison, G.: Investigating mobile wireless technology adoption: An extension of the technology acceptance model. Inf. Syst. Front. 11(3), 323–333 (2009)CrossRefGoogle Scholar
  18. 18.
    Shin, D.H.: Towards an understanding of the consumer acceptance of mobile wallet. Comput. Hum. Behav. 25(6), 1343–1354 (2009)CrossRefGoogle Scholar
  19. 19.
    Kuo, Y.F., Yen, S.N.: Towards an understanding of the behavioral intention to use 3G mobile value-added services. Comput. Hum. Behav. 25(1), 103–110 (2009)CrossRefGoogle Scholar
  20. 20.
    Wei, T.T., Marthandan, G., Chong, A.Y.L., Ooi, K.B., Arumugam, S.: What drives Malaysian m-commerce adoption? An empirical analysis. Industrial Management & Data Systems 109(3), 370–388 (2009)CrossRefGoogle Scholar
  21. 21.
    Bhatti, T.: Exploring factors influencing the adoption of mobile commerce. J. Internet Banking Commerce 12(3), 1–13 (2007)Google Scholar
  22. 22.
    Herrero Crespo, Á., Rodríguez del Bosque, I.: The effect of innovativeness on the adoption of B2C e-commerce: A model based on the Theory of Planned Behaviour. Comput. Hum. Behav. 24(6), 2830–2847 (2008)CrossRefGoogle Scholar
  23. 23.
    Lu, J., Liu, C., Yu, C.S., Wang, K.: Determinants of accepting wireless mobile data services in China. Information & Management 45(1), 52–64 (2008)CrossRefGoogle Scholar
  24. 24.
    Chong, A.Y.L.: Predicting m-commerce adoption determinants: A neural network approach. Expert Syst. Appl. (2012)Google Scholar
  25. 25.
    Chong, A.Y.L., Chan, F.T.S., Ooi, K.: Predicting consumer decisions to adopt mobile commerce: Cross country empirical examination between China and Malaysia. Decis. Support Syst (2011)Google Scholar
  26. 26.
    Wu, J.H., Wang, S.C.: What drives mobile commerce?: An empirical evaluation of the revised technology acceptance model. Information & Management 42(5), 719–729 (2005)CrossRefGoogle Scholar
  27. 27.
    Ajzen, I.: The theory of planned behavior. Organ. Behav. Hum. Decis. Process. 50(2), 179–211 (1991)CrossRefGoogle Scholar
  28. 28.
    Venkatesh, V., Brown, S.A.: A longitudinal investigation of personal computers in homes: Adoption determinants and emerging challenges. MIS Quarterly, 71–102 (2001)Google Scholar
  29. 29.
    Irani, Z., Dwivedi, Y., Williams, M.D.: Understanding consumer adoption of broadband: an extension of the technology acceptance model. J. Oper. Res. Soc. 60(10), 1322–1334 (2008)CrossRefGoogle Scholar
  30. 30.
    Kwon, O., Wen, Y.: An empirical study of the factors affecting social network service use. Comput. Hum. Behav. 26(2), 254–263 (2010)CrossRefGoogle Scholar
  31. 31.
    Suki, N.M., Ramayah, T., Ly, K.K.: Empirical investigation on factors influencing the behavioral intention to use Facebook. Universal Access in the Information Society 11(2), 223–231 (2012)CrossRefGoogle Scholar
  32. 32.
    Wang, H.Y., Wang, S.H.: Predicting mobile hotel reservation adoption: insight from a perceived value standpoint. International Journal of Hospitality Management 29(4), 598–608 (2010)CrossRefGoogle Scholar
  33. 33.
    Lewis, P., Saunders, M.N.K., Thornhill, A.: Research methods for business students. Pearson (2009)Google Scholar
  34. 34.
    Calculating, interpreting, and reporting Cronbach’s alpha reliability coefficient for Likert-type scales. In: Midwest Research to Practice Conference in Adult, Continuing, and Community Education (2003)Google Scholar
  35. 35.
    Robinson, J.P., Wrightsman, L.S., Andrews, F.: Measures of personality and social psychological attitudes. Academic Pr. (1991)Google Scholar
  36. 36.
    Sekaran, U. (ed.): Research Methods For Business: A Skill Building Approach, 3rd edn. John Wiley and Sons Inc. (2000)Google Scholar
  37. 37.
    Hinton, P.R., Brownlow, C.: SPSS explained. Theatre Arts Books (2004)Google Scholar
  38. 38.
    Straub, D., Boudreau, M., Gefen, D.: Validation guidelines for IS positivist research. Communications of the Association for Information Systems 13(24), 380–427 (2004)Google Scholar
  39. 39.
    Dwivedi, Y.K., Lal, B.: Socio-economic determinants of broadband adoption. Industrial Management & Data Systems 107(5), 654–671 (2007)CrossRefGoogle Scholar
  40. 40.
    Field, A.: Discovering statistics using SPSS. Sage Publications Limited (2009)Google Scholar
  41. 41.
    Wu, J.H., Wang, S.C.: What drives mobile commerce?: An empirical evaluation of the revised technology acceptance model. Information & Management 42(5), 719–729 (2005)CrossRefGoogle Scholar
  42. 42.
    Luarn, P., Lin, H.H.: Toward an understanding of the behavioral intention to use mobile banking. Comput. Hum. Behav. 21(6), 873–891 (2005)Google Scholar
  43. 43.
    Al-Ghaith, W.A., Sanzogni, L., Sandhu, K.: Factors influencing the adoption and usage of online services in Saudi Arabia. The Electronic Journal of Information Systems in Developing Countries 40 (2010)Google Scholar
  44. 44.
    Al-Sobhi, F.: The roles of intermediaries in the adoption of e-government services in Saudi Arabia. School of Information Systems, Computing and Mathematics (2011)Google Scholar
  45. 45.
    Kurnia, S., Smith, S., Lee, H.: Consumers’ perception of mobile internet in Australia. e-Business Review 5(1), 19–32 (2006)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.School of Information Systems, Computing & MathematicsBrunel UniversityMiddlesexUK

Personalised recommendations