Internet penetration in Africa has been accelerated, with Kenya at the fore front at over 89.5% penetration, this penetration has led to an increase in knowledge repository for Kenyans to choose from and as a result E-learning has steadily increased not only in tertiary institutions but in informal learning as well. Facebook and YouTube are some of the web based tools, – that are listed as having potential applications for teaching and learning. As a result, it is essential to understand the challenges faced in the adoption of e-learning technology. The overall purpose of this study was to investigate the factors affecting adoption of e-learning technology in Kenya with a focus on three key factors, self-efficacy, objective usability and system accessibility. This study used explanatory/hypothesis research design to investigate the factors which affect adoption of e-learning technology in Kenya. An explanatory research design was fit to the study because it will helped to ascertain not only the relationship between the different variables, but measure the effect and strength of each independent variable on the dependent variable which was the technology adoption. The target population was social media (Facebook) users in Kenya. According to Internet World Statistics there are over 6.2 million Facebook subscribers in Kenya as of June 2017. This study focused on 200 social media users in Nairobi who were interested in e-learning or have ever learned online. They were sampled by geographic cluster, simple random sampling technique with a focus on Nairobi. Online questionnaire was used by Google form and 93% responded. In the first objective of self-efficacy, the correlation result revealed positive correlation between perceived ease of use with self-efficacy (r=0.441, p<0.05) and perceived usefulness with self-efficacy(r=0.337, p<0.05). On the CFA, there was a strong model equation but on the SEM it was not. The path coefficient for the relationship between SE and adoption of e-learning was weak. In the second objective, the correlation result revealed positive correlation between perceived ease of use with objective usability (r=0.598, p<0.05) and perceived usefulness with SE (r=0.456, p<0.05). Based on SEM, the path coefficient for the relationship between objective usability and adoption of e-learning was significant. Without any latent/intervening variable, OU and PEOU was positive and significant at the 0.05 level (βeta=0.938, T-value =3.658, p<0.05). The positive relationship indicates that one unit increase in OU will result in 0.938 increases in PEOU. In the last objective of system accessibility, the correlation result revealed positive correlation between perceived ease of use with system accessibility (r=0.441, p<0.503) and perceived usefulness with system accessibility (r=0.514, p<0.05). The path coefficient for the relationship between system accessibility and adoption of e-learning was weak and not significant hence dropped from the model.The research established that Self-efficacy had little to no effect on adoption of e-learning technology in Kenya. Objective usability was the strongest factor affecting the adoption of e-learning technology while system accessibility had correlation but was not strong enough to impact the model and as such impact the adoption of e-learning technology in Kenya was weak. These findings provided some useful insights for governments, potential instructors or educators, e-learning platforms and policy makers. This recommends and highlights the need to shift from the metropolitan areas to the rural areas in order to get a holistic view of the e-learning environment in Kenya. This research shed light on objective usability as a strong factor on perceived ease of use and perceived usefulness and as such on the adoption of e-learning technology in Kenya. Based the responses received and the SEM analysis further research should focus on the two variables of system accessibility and self-efficacy involving a larger number of variables to examine, as well as larger numbers of respondents to support the factor analysis. Research should also focus on attitude and behavioral intention and their interaction with perceived eases of use and perceived usefulness. The research needs to now shift from the metropolitan areas to the rural areas in order to get a holistic view of the e-learning environment in Kenya.

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