Cluster Computing

, Volume 22, Supplement 1, pp 1873–1887 | Cite as

An empirical study on the influential factors affecting continuous usage of mobile cloud service

  • Seong-Taek Park
  • Myeong-Ryoon OhEmail author


Diffusion of smart devices, explosive growth of SNSs, increased speed of mobile network, alongside with the rapid development of ICT, all allow provision and use of many services on smart (mobile) devices, which have been provided in the wired network environment. Particularly, the use of a cloud service that allows users to access it in any place and at any time has increased on mobile devices, as well as on PCs. However, as compared to wired services, wireless services are likely to be exposed to the risk of security breach. This may inhibit the penetration of technologies. The purpose of the present study is to identify the factors that affect the intention of a continuous use (continuous intention) of mobile cloud services. For that purpose, the present study analyzes the effects of security breach risk on trust and the intention of a continuous use. The results of our analysis indicate the risks relevant to service authentication. Specifically, fault recovery and compliance exerted significant effects on trust and the continuous use intention. However, we also found that the service interruption risk and the personal information leakage risk have a significant influence on trust only. On the other hand, our findings demonstrate that trust also significantly affects the intention of a continuous use. Therefore, when a strategic decision making is considered a requisite to induce a continuous use, it is advisable to opt for and control the technologies relevant to service authentication, fault recovery, and compliance risks instead of those related to the disruption of services or leakage of personal information. Therefore, it appears to be imperative to adopt an integrated management and support process for developing a service equipped with security information technologies facilitating the continuity of businesses.


Cloud Cloud computing Mobile cloud service Security risk Continuous usage PLS 


  1. 1.
    Kim, Y.K., Kim, T.U., Park, S.T., Jung, J.R.: Establishing the importance weight of appropriability mechanism by using AHP: the case of the China’s electronic industry. Clust. Comput. 19(3), 1635–1646 (2016)CrossRefGoogle Scholar
  2. 2.
    Kwon, H.C., Jung, D.Y., Jung, B.H., Kim, J.N.: Cloud security overview. J. Korean Inst. Commun. Sci. 32(10), 71–76 (2015)Google Scholar
  3. 3.
    Bartels, A., Bartoletti, D., Rymer, J.R.: The Public Cloud Services Market Will Grow Rapidly To $236 Billion In 2020. Forrester, Cambridge (2016)Google Scholar
  4. 4.
    IDC.: Worldwide Semiannual Public Cloud Services Spending Guide. (2017).
  5. 5.
    Gillett, F.E., Pelino, M., Maxim, M., Dai, C., Ask, J.A., Kindness, A.: Predictions 2017: Security And Skills Will Temper Growth Of IoT. Forrester, Cambridge (2016)Google Scholar
  6. 6.
    Bloter.: Cloud Security Business authentic boots. (2017).
  7. 7.
    Coles, C.: Gartner’s Latest CASB Report: How to Evaluate Vendors. Gartner, Stamford (2015)Google Scholar
  8. 8.
    Lee, Y.H., Park, H.S.: Big data analysis strategy in mobile cloud. J. Korean Inst. Commun. Sci. 32(7), 57–62 (2015)Google Scholar
  9. 9.
    NIPA.: (2017)
  10. 10.
    Wang, H., Zheng, Z., Wu, L., Li, P.: New directly revocable attribute-based encryption scheme and its application in cloud storage environment. Clust. Comput. 20(3), 2385–2392 (2016)CrossRefGoogle Scholar
  11. 11.
    Wang, Y., Chandrasekhar, S., Singhal, M.: A limited-trust capacity model for mitigating threats of internal malicious services in cloud computing. Clust. Comput. 19(2), 647–662 (2016)CrossRefGoogle Scholar
  12. 12.
    Dubois, D.J., Casale, G.: OptiSpot: minimizing application deployment cost using spot cloud resources. Clust. Comput. 19(2), 893–909 (2016)CrossRefGoogle Scholar
  13. 13.
    Choi, H.J., Son, D.O., Kim, J.M., Kim, J.S., Kim, C.H.: A novel memory management technique for cloud client devices. Clust. Comput. 18(3), 1111–1116 (2015)CrossRefGoogle Scholar
  14. 14.
    Bae, J.K.: An empirical study on the effect of perceived privacy, perceived security, perceived enjoyment on continuance usage intention in mobile cloud computing. e-Business Stud. 15(3), 3–27 (2014)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Han, J.S.: Security threats in the mobile cloud service environment. J. Digit. Converg. 12(5), 263–269 (2014)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Dinh, H.T., Lee, C.H., Niyato, D., Wang, P.: A survey of mobile cloud computing: architecture, applications, and approaches. Wirel. Commun. Mob. Comput. 13(18), 1587–1611 (2013)CrossRefGoogle Scholar
  17. 17.
    Liu, S.G.: An architecture of mobile internet base on cloud computing. Adv. Mater. Res. 457–458(1), 38–41 (2012)CrossRefGoogle Scholar
  18. 18.
    Park, S.T., Park, E.M., Seo, J.H., Li, G.: Factors affecting the continuous use of cloud service: focused on security risks. Clust. Comput. 19(1), 485–495 (2016)CrossRefGoogle Scholar
  19. 19.
    Kim, D.Y., Li, G., Park, S.T., Ko, M.H.: A study on effects of security risks on acceptance of enterprise cloud service: moderating of employment and non-employment using PLS multiple group analysis. J. Comput. Virol. Hacking Tech. 12(3), 151–161 (2016)CrossRefGoogle Scholar
  20. 20.
    Ross, R., Oren, J.C., McEvilley, M.: Systems Security Engineering: An Integrated Approach to Building Trustworthy Resilient Systems, Gaithersburg, MD. National Institute of Standard and Technology. Special Publication. NIST SP 800-160 (2014)Google Scholar
  21. 21.
    Vikas, S.S., Pawan, K., Gurudatt, A.K., Shyam, G.: Mobile cloud computing: security threats. In: 2014 International Conference (IEEE), Electronics and Communication Systems (ICECS), pp. 1–4 (2014)Google Scholar
  22. 22.
    Kazim, M., Zhu, S.Y.: A survey on top security threats in cloud computing. Int. J. Adv. Comput. Sci. Appl. 6(3), 109–113 (2015)Google Scholar
  23. 23.
    ITU-T X.1601.: Security framework for cloud computing—ITU. (2015)
  24. 24.
    Cloud Security Alliance.: The Treacherous 12—Cloud Computing Top Threats in 2016. CSA (2016)Google Scholar
  25. 25.
    Getova, M.: Top 12 Security Issues Facing Cloud, Zetta. (2017)
  26. 26.
    Shahzad, A., Hussain, M.: Security issues and challenges of mobile cloud computing. Int. J. Grid Distrib. Comput. 6(6), 37–50 (2013)CrossRefGoogle Scholar
  27. 27.
    Usha, M., Malathi, P., PushpaRani, M.: Security threats in mobile cloud computing. Int. J. Res. Sci. Eng. Technol. 2(9), 42–45 (2015)Google Scholar
  28. 28.
    Goel, L., Jain, V.: A Review on Security Issues and Challenges of Mobile Cloud Computing and Preventive Measures. In: IJCA Proceedings on International Conference on Advances in Computer Engineering and Applications, vol. 5, pp. 22–27 (2014)Google Scholar
  29. 29.
    Olafare, O., Parhizkar, H., Vem, S.: A new secure mobile cloud architecture. Int. J. Comput. Sci. Issues 12(2), 161–175 (2015)Google Scholar
  30. 30.
    Cloud Security Alliance’s Security.: Guidance for Critical Areas of Focus in Cloud Computing v4.0. CSA (2017)Google Scholar
  31. 31.
    Rousseau, D., Tijoriwala, S.: What’s a good reason to change? Motivated reasoning and social accounts in promoting organizational change. J. Appl. Psychol. 84(4), 514–528 (1999)CrossRefGoogle Scholar
  32. 32.
    Gefen, D., Karahanna, E., Straub, D.W.: Trust and TAM in online shopping: an integrated model. MIS Q. 27(1), 51–90 (2003)CrossRefGoogle Scholar
  33. 33.
    CVE Details.: Apple iCloud: CVE security vulnerabilities, versions and detailed reports. (2017)
  34. 34.
    Adaptive Mobile.: iCloud Attacks Expand. (2016)
  35. 35.
    Mayer, R.C., Davis, J.H., Schoorman, F.D.: An integrative model of organizational trust. Acad. Manag. Rev. 20(3), 709–734 (1995)CrossRefGoogle Scholar
  36. 36.
    Eastlick, M.A., Lotz, S.L., Warrington, P.: Understanding online B-to-C relationships: an integrated model of privacy concerns, trust, and commitment. J. Bus. Res. 59(8), 877–886 (2006)CrossRefGoogle Scholar
  37. 37.
    Park, E.S., Woo, H.J.: A study on factors affecting the intention to use personal cloud service: focused on the convergence model of TAM and PMT. J. Cyber Commun. Acad. Soc. 30(2), 111–150 (2013)MathSciNetGoogle Scholar
  38. 38.
    Kim, S.H., Kim, G.A.: An empirical study on the factors affecting the adoption of mobile cloud and the moderating effect of mobile trust. e-Business Stud. 12(1), 281–310 (2011)CrossRefGoogle Scholar
  39. 39.
    Yu, H.X., Sura, S., Ahn, J.: An empirical analysis on the persistent usage intention of chinese personal cloud service. J. Internet Comput. Ser. 16(3), 79–93 (2015)CrossRefGoogle Scholar
  40. 40.
    Jun, C.J., Lee, J.H., Jeon, I.S.: Research about factor affecting the continuous use of cloud storage service: user factor, system factor, psychological switching cost factor. J. Soc. e-Business Stud. 19(1), 15–42 (2014)CrossRefGoogle Scholar
  41. 41.
    Jouinia, M., Rabaia, L.B.A., Aissa, A.B.: Classification of security threats in information systems. Procedia Comput. Sci. 32, 489–496 (2014)CrossRefGoogle Scholar
  42. 42.
    Ahn, J.H., Choi, K.C., Sung, K.M., Lee, J.H.: A study on the impact of security risk on the usage of knowledge management system: focus on parameter of trust. J. Soc. e-Business Stud. 15(4), 143–163 (2010)Google Scholar
  43. 43.
    Chandra, S., Srivastava, S.C., Theng, Y.L.: Evaluating the role of trust in consumer adoption of mobile payment systems: an empirical analysis. Commun. Assoc. Inf. Syst. 27, 561–588 (2010)Google Scholar
  44. 44.
    Abroud, A., Choong, Y.V., Muthaiyah, S.: A conceptual framework for online stock trading service adoption. Int. J. E-Adopt. 5(1), 52–67 (2013)CrossRefGoogle Scholar
  45. 45.
    Chin, W.W., Gopal, A.: Adoption intention in GSS: relative importance of beliefs. ACM SigMIS Database 26(2–3), 42–64 (1995)CrossRefGoogle Scholar
  46. 46.
    Fornell, C., Larcker, D.F.: Evaluating structural equation models with unobservable variables and measurement error. J. Market. Res. 18(1), 39–50 (1981)CrossRefGoogle Scholar
  47. 47.
    Nunnally, J.: Psychometric Theory. McGraw-Hill, New York, NY (1978)Google Scholar
  48. 48.
    Thompson, R., Barclay, D.W., Higgins, C.A.: The partial least squares approach to causal modeling: personal computer adoption and use as an illustration. Technol. Stud 2(2), 284–324 (1995)Google Scholar
  49. 49.
    Falk, R.F., Miller, N.B.: A Primer for Soft Modeling. University of Akron Press, Ohio (1992)Google Scholar
  50. 50.
    Wetzels, M., Odekerken-Schroder, G., Oppen, C.V.: Using PLS path modeling for assessing hierarchical construct models: guidelines and empirical illustration. Manag. Inf. Syst. Q. 33(1), 177–195 (2009)CrossRefGoogle Scholar
  51. 51.
    Tenenhaus, M., Vinzi, V.E., Chatelin, Y.M., Lauro, C.: PLS Path Modeling. Comput. Stat. Data Anal. 48(1), 159–205 (2005)MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2017

Authors and Affiliations

  1. 1.Department of Management Information SystemChungbuk National UniversityCheongjuSouth Korea

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