The Factors Influencing the Adoption of Software as a Service (Saas) by Small and Medium Size Enterprises (Smes): a Case Study of Nairobi County in Kenya

Abstract

The output of this research was a framework that informs the adoption of Software as a Service (SaaS) for Small and middle-sized businesses (SMEs). Recent research has shown that there are significant advantages of SaaS when adopted by SMEs. However, there is a knowledge gap as pertains to the understanding of the factors that influence the effective adoption of SaaS in SMEs. More so, specific to Kenya, where firstly, there is a big disconnect between the popular and scholarly discussion about SaaS for SMEs leading to limited and inadequate access to relatable information in this regard. Secondly, existing adoption models are not contextualized for developing countries. The research then proposed a conceptual framework of how SMEs' could adoption SaaS. To better facilitate a relatable and contextualized SaaS adoption model, To develop this framework the researcher employed three approaches; the first approach was to conduct a preliminary empirical study involving 6 Cloud providers and 25 SMEs in Nairobi County. The second approach involved a critical review of literature pertaining to studies on SaaS adoption of SMEs. The final approach was a critical review of an existing framework, Technology Organization and Environmental framework (TOE). These three approaches informed the design of the framework developed by this research. The conceptual framework developed was an extension of TOE, which has fourteen exogenous variables and four moderating variables. The exogenous variables were Awareness, Trust, Cost, Top management support, Trialability, Complexity, Compatibility, Uncertainty, Relative advantage, ICT Services level, Dealer labors and outdoor calculating provision, Competitive pressure, Prior IT experience, Innovativeness and moderating factors which include SME Firm Size I.e. Small Medium, Age of the SME, SME Sector and SME Market Scope ie Local Regional. The exogenous variables were complex constructs that needed to be operationalized using multiple measures. The researcher then developed a criterion that led to SaaS adoption for SMEs for investigation. The research approach used was a cross-sectional approach because there was more emphasis on the impact of each specific construct and variable. Data provided by the Nairobi County Government SME licensing section, enhanced a comprehensive cluster sampling technique. A cluster sampling method was used to inaugurate the identified defendants. The author investigated SMEs in deferent sectors on a weighting proposal. This target was 87% in excess of the required minimum of 200 respondents as required by Structural Equation Modeling (SEM). However 293 respondents were obtained, which was significantly above the minimum requirement. SEM was used to test multifaceted associations amongst unrushed and latent variables. In addition, it tests relationships between two or more latent variables. Data analysis began with data management using SPSS where, the author cleaned the data and guaranteed the quality of data. Hypotheses testing was also done using path coefficients indicated by the P-values and how best the prototypical fits the data. The final stage of investigation was prepared using SPSS Analysis of Moment Structures (AMOS), which focused on modeling, trimming and best fit. The research findings indicated that SaaS adoption status showed that SMEs already using SaaS constitute 30%, those intending to adopt SaaS make up of 56%, while the remaining 14% do not intend to adopt. The findings also indicated that the factors that significantly influence the adoption of SaaS by SMEs include: awareness, trust, prior IT experience, relative advantage, triability, ICT knowledge and skills, top level management support and complexity. It was determined that the relevant moderating variables in this regard were market scope, the specific SME sector, and what the size of the SME was. This study extended the existing body of knowledge by providing better context for SaaS adoption by SMEs in Kenya. On this basis the researcher was able to recommend firstly, that evaluation of the market is critical for SaaS deployment and finally, that application developers, technological consultants, software vendors, and policy makers that intend to adopt SaaS should consider the Extended TOE model developed from this research. The investigation model in this study can advance their indulgent of why some SMEs take to adopt cloud computing facilities, while apparently comparable ones facing analogous market circumstances do not. Keywords: ICT Adoption, Small and Medium Enterprises, Software as a Service, Nairobi County