Journal of Computer Sciences and Applications. 2018, 6(2), 69-74
DOI: 10.12691/JCSA-6-2-3
Original Research

Trust and Continuous Deployment of Cloud Computing: A Quantitative Analysis

Kunle Elebute1,

1Department of Computer Science, Software Development and Security, University of Maryland University College, Largo, USA

Pub. Date: September 07, 2018

Cite this paper

Kunle Elebute. Trust and Continuous Deployment of Cloud Computing: A Quantitative Analysis. Journal of Computer Sciences and Applications. 2018; 6(2):69-74. doi: 10.12691/JCSA-6-2-3

Abstract

In recent time, many studies have investigated the criteria that should guide a user when selecting a trustworthy cloud service provider. Similarly, factors influencing the user’s decision to adopt cloud computing have been exhaustively discussed. However, it is still unclear if there is a correlation between a user’s trust in the capability of a cloud provider and the user’s decision to continuously deploy cloud computing. Using a multinomial logistic regression, this study analyzed responses from 176 information technology managers who were currently using cloud computing as at the time of the study. Results from the data analysis indicated a negative relationship between a user’s trust in the capability of a cloud provider and the user’s decision to continuously deploy cloud computing. Consequently, a cloud user who does not trust the capability of a cloud provider will be unwilling to continuously deploy cloud computing regardless of the benefits of the cloud platform. This study recommended a synergy between cloud users and cloud providers to bridge trust gaps and develop security plans and policies that will effectively tackle cyber-threats.

Keywords

cloud computing, trust, cloud provider, security, cyber-threats, multinomial logistic regression, cloud deployment

Copyright

Creative CommonsThis work is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

References

[1]  Lian, J. (2015). Critical factors for cloud based e-invoice service adoption in Taiwan: An empirical study. International Journal of Information Management, 35(1), 98-109.
 
[2]  Shiau, W. L., & Chau, P. Y. (2016). Understanding behavioral intention to use a cloud computing classroom: A multiple model comparison approach. Information & Management, 53, 355-365.
 
[3]  Hew, T. S., & Kadir, S. L. S. A. (2016). Predicting the acceptance of cloud-based virtual learning environment: The roles of self-determination and channel expansion theory. Telematics and Informatics, 33(4), 990-1013.
 
[4]  Jabbar, S., Naseer, K., Gohar, M., Rho, S., & Chang, H. (2016). Trust model at service layer of cloud computing for educational institutes. The Journal of Supercomputing, 72(1), 58-83.
 
[5]  Sabi, H. M., Uzoka, F. M. E., Langmia, K., & Njeh, F. N. (2016). Conceptualizing a model for adoption of cloud computing in education. International Journal of Information Management, 36(2), 183-191.
 
[6]  Elebute, K. (2018). Cyber-attack, intellectual property theft, and organizations' continuance intention to use cloud computing: A quantitative correlational study. Dissertation, Capella University.
 
[7]  Siadat, S., Rahmani, A. M., & Navid, H. (2017). Identifying fake feedback in cloud trust management systems using feedback evaluation component and Bayesian game model. The Journal of Supercomputing, 73(6), 2682-2704.
 
[8]  Mell, P., & Grance, T. (2011). The NIST definition of cloud computing. NIST Special Publication, 800(145), 7.
 
[9]  Ramachandran, M., & Chang, V. (2016). Towards performance evaluation of cloud service providers for cloud data security. International Journal of Information Management, 36(4), 618-625.
 
[10]  Oliveira, T., Thomas, M., Baptista, G., & Campos, F. (2016). Mobile payment: Understanding the determinants of customer adoption and intention to recommend the technology. Computers in Human Behavior, 61, 404-414.
 
[11]  Arpaci, I. (2016). Understanding and predicting students' intention to use mobile cloud storage services. Computers in Human Behavior, 58, 150-157.
 
[12]  Ghorbel, A., Ghorbel, M., & Jmaiel, M. (2017). Privacy in cloud computing environments: a survey and research challenges. The Journal of Supercomputing, 73(6), 2763-28.
 
[13]  Astri, L. Y. (2015). A study literature of critical success factors of cloud computing in organizations. Procedia Computer Science, 59, 188-194.
 
[14]  Singh, A., & Chatterjee, K. (2017). Cloud security issues and challenges: A survey. Journal of Network and Computer Applications, 79, 88-115.
 
[15]  Chiregi, M., & Navimipour, N. J. (2016). Trusted services identification in the cloud environment using the topological metrics. Karbala International Journal of Modern Science, 2(3), 203-210.
 
[16]  Crossman, A., & Lee‐Kelley, L. (2004). Trust, commitment and team working: the paradox of virtual organizations. Global networks, 4(4), 375-390.
 
[17]  Ryzhova, S. V. (2017). Trust and ethnic tolerance in the face of social change. Sociological Research, 56(3), 197-217.
 
[18]  Shaikh, R., & Sasikumar, M. (2015). Trust model for measuring security strength of cloud computing service. Procedia Computer Science, 45, 380-389.
 
[19]  Halabi, T., & Bellaiche, M. (2017). Towards quantification and evaluation of security of Cloud Service Providers. Journal of Information Security and Applications, 33, 55-65.
 
[20]  Yang, H., & Lin, S. (2015). User continuance intention to use cloud storage service. Computers in Human Behavior, 52, 219-232.
 
[21]  Noor, T. H., Zeadally, S., Alfazi, A., & Sheng, Q. Z. (2018). Mobile cloud computing: Challenges and future research directions. Journal of Network and Computer Applications, 115, 70-85.
 
[22]  Abbadi, I. M., & Martin, A. (2011). Trust in the Cloud. Information Security Technical Report, 16(3-4), 108-114.
 
[23]  Rizvi, S., Karpinski, K., Kelly, B., & Walker, T. (2015). Utilizing Third Party Auditing to Manage Trust in the Cloud. Procedia Computer Science, 61, 191-197.
 
[24]  Prasad, V. K., Shah, M., Patel, N., & Bhavsar, M. (2018). Inspection of Trust Based Cloud Using Security and Capacity Management at an IaaS Level. Procedia Computer Science, 132, 1280-1289.
 
[25]  Moyano, F., Fernandez-Gago, C., & Lopez, J. (2013). A framework for enabling trust requirements in social cloud applications. Requirements Engineering, 18(4), 321-341.
 
[26]  Tang, C., & Liu, J. (2015). Selecting a trusted cloud service provider for your SaaS program. Computers & Security, 50, 60-73.
 
[27]  Chong, S. K., Abawajy, J., Ahmad, M., & Hamid, I. R. A. (2014). Enhancing trust management in cloud environment. Procedia-Social and Behavioral Sciences, 129, 314-321.
 
[28]  Agag, G., & El-Masry, A. A. (2016). Understanding consumer intention to participate in online travel community and effects on consumer intention to purchase travel online and WOM: An integration of innovation diffusion theory and TAM with trust. Computers in Human Behavior, 60, 97-111.
 
[29]  Davis, F. D. (1993). User acceptance of information technology: System characteristics, user perceptions and behavioral impacts. International Journal of Man-Machine Studies, 38(3), 475-487.
 
[30]  Wallace, L. G., & Sheetz, S. D. (2014). The adoption of software measures: A technology acceptance model (TAM) perspective. Information & Management, 51(2), 249-259.
 
[31]  Dibra, M. (2015). Rogers theory on diffusion of innovation: The most appropriate theoretical model in the study of factors influencing the integration of sustainability in tourism businesses. Procedia Social and Behavioral Sciences, 195, 1453-1462.
 
[32]  Creswell, J. W. (2014). Research design: Qualitative, quantitative and mixed methods approaches (4th ed.). Thousand Oaks, CA: Sage Publications.
 
[33]  Cooper, H. (2016). Research synthesis and meta-analysis: A step-by-step approach (5th ed.). Washington, DC: Sage Publications.
 
[34]  Field, A. (2013). Discovering statistics using IBM SPSS Statistics (4th ed.). Thousand Oaks, CA: Sage.
 
[35]  El-Habil, A. M. (2012). An application on multinomial logistic regression model. Pakistan journal of statistics and operation research, 8(2), 271-291.
 
[36]  Vogt, W. P. (2007). Quantitative research methods for professionals in education and other fields. Boston, MA: Pearson Custom Publishing.
 
[37]  Badri, M., Toure, F., & Lamontagne, L. (2015). Predicting unit testing effort levels of classes: An exploratory study based on multinomial logistic regression modeling. Procedia Computer Science, 62, 529-538.
 
[38]  Goeman, J. J., & le Cessie, S. (2006). A goodness-of-fit test for multinomial logistic regression. Biometrics, 62(4), 980-985.