Computer self-efficacy is frequently used as an explanatory variable in software training and technology acceptance investigations and it has been frequently used to predict training and learning outcomes. While self-efficacy models identify prior experience with computers as an important determinant of generalized self-efficacy judgments, relatively few studies have systematically examined the types of experience that drive such judgments. Gender and frequency of computer use have also been identified as other predictors of generalized computer self-efficacy. In this investigation, self-reported knowledge/skill attainments levels with each of nineteen computer use/knowledge dimensions are used to measure prior experience/knowledge of computers. These were collected from 340 university students at the same time that they completed a generalized computer self-efficacy scale. This data is used to test two predictions: 1) that greater prior computer knowledge/experience is directly related to higher computer self-efficacy scores and 2) for comparable levels of prior experience/knowledge, males will have higher self-efficacy scores than females. Our results provide support for the first prediction but not the second. Our findings suggest that experience/knowledge of less common computer applications may be more important in shaping self-efficacy judgments than are greater levels of experience/knowledge with common computer applications.
Case, Tom; Han, Hyo-Joo; and Bullington, Joseph, "Antecedents of Generalized Computer Self-Efficacy Judgements" (2005). AMCIS 2005 Proceedings. 253.