Purpose: E-learning is an organizationally risky investment given the cost and poor levels of adoption by users. In order to gain a better understanding of this problem, the aim of this paper is to conduct a study into the use of e-learning in a rail organization. Design/methodology/approach: Using an online survey, employees of a rail-sector organization were questioned about their use and likelihood of adoption of e-learning. This study explores the factors that affect the way in which learners experience and perceive such systems. Using statistical analysis, 12 hypotheses are tested and explored. Relationships between learning satisfaction, intention to adopt, and the characteristics of e-learning systems were established. Findings: The study found that e-learning characteristics can buffer the relationship between learner characteristics and intention to adopt further e-learning in the future. Further, the paper found that high levels of support can compensate individuals who are low in technological efficacy to adopt e-learning. Research limitations/implications: The cross-sectional design of the study and its focus on measuring intention to adopt as opposed to actual adoption are both limitations. Future research using longitudinal design and research employing a time lag design measuring actual adoption as well as intention are recommended. Practical implications: From a practical perspective, organizations can focus on the actual content and authenticity of the learning experience delivered by the e-learning system to significantly impact how employees will perceive and use e-learning in the future. Low technological efficacy individuals tend not to adopt new technology. Instead of changing individuals' personalities, organizations can implement supportive policies and practices which would lead to higher e-learning adoption rate among low efficacy individuals. Originality/value: The study integrates technology adoption and learning literatures in developing enablers for e-learning in organizations. Further, this study collects data from rail employees, and therefore the findings are practical to an industry. (Contains 2 tables and 6 figures.)