Literature Review
The rapid growth of cloud computing is threatening to replace traditional methods of computing despite the threats associated with cloud computing. Critical to the discussion is the fact that industry leaders in technology have already embraced the technology and are quickly influencing other industry players into their path. For instance, organizations such as Google, Amazon, and Facebook rely on cloud computing because the technology helps the mentioned organizations in reducing the cost of doing business. Apart from the influence from industry leaders, the adoption of the technology in question is also influenced by employees’ knowledge on cloud computing (In Despotovic?-Zrakic?, In Milutinovic?, and In Belic?, 2014). It is why the researcher conducted a study to examine whether employees’ attitudes towards the adoption of cloud computing differs based on their level of experience with the same technology using the hypotheses below.
H0: There is no statistically significant difference in employees’ attitudes on the adoption of the technology under discussion between experienced and inexperienced employees on cloud computing.
H1: There is a statistically significant difference in employees’ attitudes on the adoption of the technology under discussion between experienced and inexperienced employees on cloud computing.
Cloud computing is growing rapidly and is expected to reach US$ 241 billion in 2020 (Carcary, 2014). Vital to the discussion is the fact that employees’ knowledge on cloud computing defines an organizations propensity towards the adoption of the technology. As evidenced, Abdollahzadehgan and Hussin (n.d) conducted a study with an aim of identifying success factors for the adoption of the technology in question in SMEs. As a result, the authors concluded that SMEs fail to adopt the technology because of limited knowledge on the subject. In a different study, Sadeghzadeh, Haghshenas, Nassiriyar, and Shahbazi (2014) reveal that the technology is not widely used for knowledge management because practitioners in the industry have limited knowledge on the subject. Hassan, Nasir, Khairudin and Adon (2017) also examined factors dictating the adoption of cloud computing services and concluded that the cost of adopting the technology is the leading reason for adopting cloud computing.
The data was collected by sending questionnaires to employees who responded and send back their questionnaires to the researcher. The responses were then extracted from questionnaires and filed into Microsoft Excel. Critical to the discussion is the reality that the same Microsoft Excel was used to tabulate the responses and organize them into data that could be analyzed. It is important to note that the study was interested in identifying the effect of employee’s knowledge on the adoption of the technology in question at the work place. Consequently, the researcher categorized the data into two groups. One group contained data from employees who had prior knowledge in cloud computing while the other group contained data from employees without prior experience in cloud computing.
Research Methodology
It is notable that the researcher conducted an independent samples t test to examine whether employees attitude towards the adoption of cloud computing services differ based on their knowledge of the same technology. As a result, the study checked for the required assumptions before conducting the test. For instance, the researcher checked the data for any significant outliers and removed the outliers from the data set. This was done using box plots on each of the variables used for analysis. The code used to check for outliers is shown in figure 2 below. It is important to highlight that the code helped the researcher in plotting box plots and identifying the outliers, which were present in the inexperienced employees’ column.
Upon removing outliers, the researcher checked to confirm that the variables were approximately normally distributed using qqplots. This owes to the fact the code shown in figure four below indicates how the researcher produced the plots depicted in the same figure. From the plots, it is evident that the two variables are approximately normally distributed. This owes to the fact that the plots begin with few data points below the line and end with few data points above the line.
The homogeneity of variances was also checked using Bartlett’s test for homogeneity of variances. Critical to the discussion is the fact that the data was prepared for the test by combining the inexperienced and experienced employees’ variables into a single column. The difference between the two variables was set using dummy variables with zero representing inexperienced employees and one representing experienced employees. Bartlett’s test was then run, which resulted in p<0.05, implying that each category was unique.
A t test was then conducted after confirming all the assumptions had been met. A sample of employees was asked to indicate how there past experience with cloud computing influenced their attitude of adopting cloud computing at their work place. The mean score for employees with previous experience in cloud computing was higher (M = 33.64, SD = 12.95, n = 22) than the mean score for employees without prior experience in cloud computing (M = 33.22, SD = 13.00, n = 22), t(42) = -0.38, p < .05, 95% CI [-2.6. 1.8].
From the results, the p value is greater than 0.05. It follows that the results fail to reject the null hypothesis implying that there is no statistically significant difference between the two groups. As evidenced, Fischetti (2015) argues that the researcher should always reject the null hypothesis if the power of the test is greater than the desired threshold, which in most cases is 0.05 or 0.001. In short, the mean from experienced employees is higher than the mean from inexperienced employees,.
Discussion
Clearly, the results indicate the absence of a statistically significant difference on the intention to adopt cloud computing between inexperienced and experienced employees on cloud computing. On the contrary, In Despotovic?-Zrakic?, In Milutinovic?, and In Belic? (2014) conducted a study and concluded that employees’ knowledge on cloud computing and an organization’s adoption of the same technology have a strong positive relationship. In simpler terms, the authors demonstrated that an increase in the level of employees knowledge of cloud computing is likely to result in the adoption of cloud computing technology at the work place of such employees. Vital to the debate is the fact that further evidence from Sallehudin, Razak, and Ismail (2015) highlights training employees on cloud computing prior to introducing the technology at the work place is likely to result in a smooth transition when introducing the technology at the workplace.
Evidence from previous studies indicates that employees’ knowledge on cloud computing has a strong positive relationship with the introduction of the technology at the workplace. Regardless, this study indicates the absence of a statistically significant difference on the intention to adopt cloud computing between inexperienced and experienced employees on cloud computing. The study is limited by the questionnaire used for collecting responses from the study participants. For instance, the questionnaire did not categorize employees based on their knowledge on cloud computing. Instead, it categorized the employees based on their experience with cloud computing. It owes to the fact that some employees may lack experience, but they could possess considerable knowledge on cloud computing. As a result, such employees would have similar attitudes towards the adoption of cloud computing at the work place with those of employees with prior experience in cloud computing. It follows that future studies should eliminate the mentioned limitation when designing the questionnaire.
Conclusion
Previous publications establish that employees’ knowledge influences the adoption of the technology under discussion within an organization. Regardless, this study did not establish a significant difference of employees’ attitude towards the adoption of cloud computing between experienced and inexperienced employees on cloud computing. This owes to the fact that the researcher used an independent samples t test to examine for the difference in question because every response was recorded from a different study participant. Critical to the discussion is the reality that the researcher checked the data to ensure that all assumptions required for running an independent samples t test were met. However, the study is limited by the questionnaire used for collecting data from the study participants. It is because the questionnaire allowed the researcher to categorize responses based on experience instead of categorizing them based on their level of knowledge on cloud computing.
References
Abdollahzadehgan, A., and Hussin, C. A. (n.d). The Organizational Critical Success Factors for
Adopting Cloud Computing in SMEs. Journal of Information Systems Research and Innovation. ISSN: 2289-1358. Available at: https://seminar.utmspace.edu.my/jisri/ Accessed on [26 May 2017]
Buyya, R., Vecchiola, C., and Selvi, S. T. (2013). Mastering Cloud Computing. New Delhi,
McGraw Hill. Cotton, R. (2013). Learning R. Beijing: O’Reilly
Carcary, M., Doherty, E. and Conway, G. (2014). The Adoption of Cloud Computing by Irish SMEs – an Exploratory Study. The Electronic Journal Information Systems Evaluation, 17(1): 3-14. Available at: www.ejise.com Accessed on [26 May 2017]
Fischetti, T. (2015). Data analysis with R: load, wrangle, and analyze your data using the world’s most powerful statistical programming language. Birmingham, UK; Mumbai: Packt Publishing.
Hassan, H., Nasir, M. H. M., Khairudin N., and Adon, I. (2017). Factors Influencing Cloud Computing Adoption in Small and Medium Enterprises. Journal of Information and Computer Technology, 16(1): 21- 41. Available at: https://www.jict.uum.edu. Accessed on [26 May, 2017]
In Despotovic?-Zrakic?, M., In Milutinovic?, V., and In Belic?, A. (2014). Handbook of Research on High Performance and Cloud Computing in Scientific Research and Education. Hershey, PA: Information Science Reference
Sadeghzadeh, A., Haghshenas, M., Mojtaba, N., and Roghayeh, S. (2014). Adoption of Cloud Based Knowledge Management. International Journal of Engineering and Innovative Technology, 3(11): 324-328. Available at: https://www.ijeit.com/Vol%203/Issue%2011/IJEIT1412201405_57.pdf. Accessed on [26 May, 2017]
Sallehudin, J., Razak, K., and Ismail, H. (2015). Factors Influencing Cloud Computing Adoption in the Public Sector: An Empirical Analysis. Journal of Entrepreneurship and Business, 3(1): 20-5. Available at: https://fkp.umk.edu.my/JEB/download/3-v3.pdf. Accessed on [26 May, 2017]
Unwin, A. (2015). Graphical data analysis with R. Boca Raton, FL: CRC Press Xie, S. C., and Wickham, H., and Grolemund, G. (2016). R for data science: import, tidy, transform, visualize, and model data. Sebastopol, CA: O’Reilly Media.
Xin, W. F. (2016). Benefits and Challenges of the Adoption of Cloud Computing in Business. International Journal on Cloud Computing: Services and Architecture (IJCCSA), 6(6): 1-15. Available at: https://aircconline.com/ijccsa/V6N6/6616ijccsa01.pdf. Accessed on [26 May, 2017]
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