The importance of understanding what motivates individuals toward acceptance of technology, and what compels them to use it is becoming more essential due to the fact that organizations rely heavily on information technology. There is no lack of research in the area of information technology, but studies examining the effects of certain individual traits and training on computer self-efficacy, and the resulting increase in usage behavior are limited. Unless there is a clearer understanding of what motivates individuals to use technology, there is a likelihood that the technology will be under-used.

There is indication throughout research that the Big-Five personality traits (Extraversion, Conscientiousness, Neuroticism, Agreeableness and Openness to Experience) may have an effect on an individual's level of computer self-efficacy. Those who have a higher level of computer self-efficacy may be more accepting of technology and may be more likely to use all its available features. This study examined the relationship between these personality traits and levels of computer self-efficacy. There were pre-tests to determine the beginning level of computer self-efficacy and likelihood to use. This was followed by two segments of training, with a time lapse of a week between, to see whether the training raises the level of computer self-efficacy. During each training segment, half the participants received training using behavior modeling, and the other half had behavior modeling with a retention enhancement activity.

Partial least squares was used to test the models. We found that Openness to Experience was related to computer self-efficacy, and that computer self-efficacy did not increase following training sessions. These findings and the fact that intention to use decreased following the second training were important in understanding more about training and in learning about those factors that influence utilization of available technologies. We must understand how to effectively train workers on different levels of software complexity.