Cloud computing factors and innovation characteristics influencing its adoption in small and medium enterprises: a case of Kampala capital city, Uganda.

Abstract

This project was to investigate how cloud computing factors and innovation characteristics affect its adoption in Small and Medium Enterprises in Uganda, as well as to design and generate a practical model that can be used for the adoption of cloud computing. The specific objectives included: to assess the effect of human factors in influencing the decision to adopt cloud computing by Small and Medium Enterprises in Uganda; to evaluate environmental factors that influence the decision to adopt cloud computing by Small and Medium Enterprises in Uganda; to examine organizational factors that influence the decision to adopt cloud computing by Small and Medium Enterprises in Uganda; and to establish the extent to which innovation characteristics influence the decision to adopt cloud computing by Small and Medium Enterprises in Uganda. This study was partially guided by the innovation theory by Schumpeter (Schumpeter, 1947), but mainly by the Diffusion of Innovation Theory of Rogers (1983). The study also relied on the positivist approach paradigm with a descriptive and explanatory-quantitative research design undertaken from a deductive approach that allows for moderating and control variables to be tested using a cross-sectional survey. The targeted study population was 379, 257 employees that work with the registered SMEs according to a report by the Uganda Bureau of Statistics Census of Business Establishments 2010/201. 1000 tax compliant SMEs in Kampala as listed by the Uganda Revenue Authority were considered for this study. A sample of 416 respondents was selected from 16 industries. Only questionnaires were used for data collection. The study found out that the odds ratio for human factors indicates that the odds of being at a higher level of cloud computing adoption increase by a factor of 0.687 for every unit increase in human factors. Given the odds ratio is < 1, this indicates a decreasing probability of being at a higher level of cloud computing adoption as values increase on human factors. The odds ratio for environmental factors indicates that the odds of being at a higher level of cloud computing adoption increase by a factor of 0.435 for every unit increase in the environmental factors. Given the odds ratio is < 1, this indicates a decreasing probability of being at a higher level of cloud computing adoption as values increase in the environmental factors. The odds ratio for xiv organisational factors indicates that the odds of being at a higher level of cloud computing adoption increase by a factor of 1.506 for every unit increase on organizational factors. When a Spearman's rank correlation was run to determine the relationship between human factors and cloud computing adoption, there was a very weak, negative monotonic correlation between human factors and cloud computing adoption decision (rs = -.059, n = 307, p>.001). The study also showed that there was a very weak, negative monotonic correlation between environmental factors and cloud computing adoption decision (rs = -.071, n = 307, p>.001) and that there was a very weak, negative monotonic correlation between organizational factors and cloud computing adoption decision (rs = -.151, n = 307, p>.001). For these variables, as a result, the null hypotheses are not rejected as was stated although not with absolute certainty. Lastly, the study revealed a strong, positive monotonic correlation between innovative characteristics and cloud computing adoption decision (rs = .615, n = 307, p< .001). For this variable, the study thus rejects the null hypothesis since the finding was statistically significant. Based on the various gaps that have been shown, this research initiated a new cloud adoption model as a contribution to new knowledge called the Sustainability of Adopting Innovative Cloud Computing (SAICC) Model. The key concepts of the model include innovation characteristics; environmental factors; organizational factors; innovative adoption; sustainable adoption; human factors; and decision to adopt. The study, therefore, recommends educating both top management and SME managers about emerging information technologies such as CC and its issues. The study further recommends that when it comes to innovation characteristics of CC, Policymakers and IT technicians should consider the fact that calculations are somewhat different for small and medium-sized businesses which do not have the means to set up the initial infrastructure required to understand the cost structures of big data centers. Lastly, the study recommends that in shifting their IS infrastructure to a cloud, SMEs as led by their managers and business owners in Uganda and other developing countries need to change their attitudes and have a positive approach towards CC.