Critical Factors for the Adoption of Virtualization Software: An Empirical Study

  • Said Ally The Open University of Tanzania
Keywords: virtual computing, hypervisor, container, server virtualization

Abstract

The growth of virtualization technologies has allowed cloud adopters to make better use of computing resources. Despite its rapid expansion, adopters are having difficulty locating an appropriate virtualization solution and realizing the desired benefits. Six adoption factors were found to be the essential aspects of virtualization software for a seamless transition from physical to virtual computing after 43 tools and seven constructs were extracted using technology diffusion techniques. Based on the effect level, they are the software features, innovation risk, environmental factor, relative advantage, perceived usefulness, and social system. For public entities, the transition is specifically driven by ICT policies, regulations, and costs, whereas private sectors rely on market trends, the business status quo, and expert ranking. Adopters can utilize the study's findings as a strategic signal for a risk-free switch from physical to virtual computing, while vendors can leverage the design attributes to enhance software functionality.    

References

Addo, P. C., Akabua, C., & Akpatsa, S. K. (2019). Leveraging Network Virtualization for Safer and Greener Communication in Africa. In 2019 IEEE AFRICON (pp. 1-9). IEEE.

Ahmed, M., & Litchfield, A. T. (2018). Taxonomy for identification of security issues in cloud computing environments. Journal of Computer Information Systems, 58(1), 79-88.

Ajzen I., (1991). The theory of planned behavior, Organizational Behavior and Human Decision Processes, 50 (2), 179-211

Ajzen, I. (1985). From intentions to actions: A theory of planned behavior, In J. Kuhl, & J. Beckmann (Eds.), Action control: From cognition to behavior. New York: Springer-Verlag

Ally, S., & Jiwaji, N. (2022). Common inhibiting factors for technology shifting from physical to virtual computing. Ethiopian Journal of Science and Technology, 15(2), 125-139.

Battaglia, Michael P., (2011). Nonprobability sampling, Encyclopedia of Survey Research Methods, 2008, SAGE Publications, 8 Nov 2011, pp. 523 – 526

Chuttur M.Y., (2009). Overview of the technology acceptance model: Origins, Developments and Future Directions, Indiana University, USA, Sprouts: Working Papers on Information Systems, 9(37). http://sprouts.aisnet.org/9-37

Cisco, (2016). Cisco global cloud index: Forecast and methodology, 2015–2020., White Paper Cisco Public”, C11-738085-00 11/16, http://www.cisco.com/c/dam/en/us/solutions/collateral/service-provider/global-cloud-index-gci/white-paper-c11-738085.pdf. Date Accessed: July 11, 2017

Crockett E. (2022). The Network Virtualization Market in 2022. Enterprise storage forum. Website: https://www.enterprisestorageforum.com/networking/network-virtualization-market/. Date accessed: 05-07-2022

Davis, F., (1989). Perceived usefulness, perceived ease of use and user acceptance of information technology, MIS Quarterly: Management Information Systems, Volume 13, pages 319-339. URL: http://business.clemson.edu/ise/html/perceived_usefulness__perceive.html, Date Accessed: November 10, 2016.

Fishbein, M. & Ajzen, I., (1975). Relief, attitude, intention and behaviour: An introduction to theory and research. Addison – Wesley Pub. Co: Reading Mass; Don Mills, Ontario

Hair, J., Black, W., Babin, B., & Anderson R., (2010). “Multivariate data analysis, 7th edition, Upper Saddle River, NJ., USA: Prentice – Hall, Inc

Hu, P. J., Chau, P. Y. K., & Sheng, O. R. L. (1999). Examining the technology acceptance model using physician acceptance of telemedicine technology, Journal of Management Information Systems, 16 (2): 91–112

Infographic, (2014). Top 5 reasons why you can’t afford not to virtualize. Infographic Whitepaper on VMware https://www.vmware.com/content/dam/digitalmarketing/vmware/en/pdf/infographic/vmw-top5-reasons-infographic.pdf. Date Accessed: July 11, 2017

Kizza, J. M. (2008). Africa Can Greatly Benefit from Virtualization Technology–Part, International Journal of Computing and ICT Research, Vol. 6, Issue 1, June 2012

Kumar R., (2011). Research methodology. A step-by-step guide for beginners. 3rd edition, SAGE Publications, ISBN: 978-1-84920-300-New Delhi, India

Legris, P., Ingham, J., & Collerette, P., (2003). Why do people use information technology? A critical review of the technology acceptance model. Information & Management, 40 (3): 191–204.

Letlonkane, L., Bukohwo, M. E., & Molehabangwe, G. (2016, September). Cloud Storage Usage by Students of the Northwest University South Africa. In 10th European Conference on Information Systems Management: ECISM 2016 (p. 124). Academic Conferences and publishing limited.

Li, Y., & He, Y. (2021, August). Research on Computer Application Technology Based on Big Data Environment. In Journal of Physics: Conference Series (Vol. 1992, No. 2, p. 022127). IOP Publishing.

Market Data Forecast, (2022). Network function virtualization. Website: https://www.marketdataforecast.com/market-reports/network-function-virtualization. Date accessed: 05-07-2022.

Mathieson K., (1991). Predicting user intention: Comparing the technology acceptance model with the theory of planned behavior, Information Systems Research, 2, 3, 173 – 191

Meyer, G. (2004). Diffusion methodology: Time to innovate? Journal of Health Communication: International Perspectives. 9(S1): 61.

Njue, M. K. (2015). The Effect of ICT Industry Standards on the Success of Server Virtualization-A Case Study of Africa Nazarene University (Doctoral dissertation, Africa Nazarene University).

Obasuyi, G.C. & Sari, A., (2015). Security challenges of virtualization hypervisors in virtualized hardware environment. Int. J. Communications, Network and System Sciences, 8, 260-273. http://dx.doi.org/10.4236/ijcns.2015.87026

Pearce, M., Zeadally, S., & Hunt, R. (2013). Virtualization: issues, security threats, and solutions. ACM Computing Surveys (CSUR), 45(2), 17. 39 pages. DOI = 10.1145/2431211.2431216 http://doi.acm.org/10.1145/2431211.2431216

Plouffe, C. R., Hulland, J. S., & Vandenbosch, M. (2001). Richness versus parsimony in modeling technology adoption decisions—understanding merchant adoption of a smart card-based payment system, Information Systems Research, 12(2), 208-222

Press, L., Foster, W., Wolcott, P., & McHenry, W. (2002). The internet in India and China. First Monday, 7(10).

Rodriguez, L. F., (2012). The technology acceptance model (TAM) as a viable model to determine the acceptance of e-learning technologies in higher education institutions (HEI’s)”, http://works.bepress.com/luis_rodriguez/8

Rogers, E., (2003). Diffusion of innovations. 5th Edition, Simon and Schuster. ISBN 978-0-7432-5823-4.

Sabi H. M., Uzoka F. E., Langmia K., Njeh F. N., (2016). Conceptualizing a model for adoption of cloud computing in education. International Journal of Information Management, 36 (2016), 183-191

Segars, A. H. & Grover, V. (1998). Strategic information systems planning success: An investigation of the construct and its measurement. MIS Quarterly, 22(2), 139-163.

Spiceworks, (2016). Server virtualization and OS trends. Spiceworks State of IT Report – Networking Article. https://community.spiceworks.com/networking/articles/2462-server-virtualization-and-os-trends, Date Accessed: July 11, 2017

Thomas, M., & Van Belle, J. P. (2017). Virtualization Footprint: Why Re-Invest? Journal of Information Technology Education: Discussion Cases, 6(1).

Uhlig, R., Neiger, G., Rodgers, D., Santoni, A.L., Martins, F.C.M., Anderson, A.V., (2005). Intel virtualization technology. Computer, 38, 48-56. http://dx.doi.org/10.1109/mc.2005.163

Venkatesh, V. & Davis, F.D., (1996). A model of the antecedents of perceived ease of use: development and test., Decision Sciences. 27 (3): 451–481.

Published
2023-09-30