Assessing IT usage: the role of prior experience
MIS Quarterly
Audience engagement in multimedia presentations
ACM SIGMIS Database
Information and Management
Information Systems Research
Information Systems Research
Combining IS Research Methods: Towards a Pluralist Methodology
Information Systems Research
Engagement in Multimedia Training Systems
HICSS '99 Proceedings of the Thirty-Second Annual Hawaii International Conference on System Sciences-Volume 1 - Volume 1
International Journal of Human-Computer Studies - Special issue on HCI and MIS
Information and Management
Journal of Management Information Systems
International Journal of Business Information Systems
Exploring information technology adoption in the classroom: case of online learning technology
International Journal of Business Information Systems
Predicting m-commerce adoption determinants: A neural network approach
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Computer Self-Efficacy: A Meta-Analysis
Journal of Organizational and End User Computing
Factors affecting Chinese Ubiquitous Game Service usage intention
International Journal of Mobile Communications
Predicting open IOS adoption in SMEs: An integrated SEM-neural network approach
Expert Systems with Applications: An International Journal
Proceedings of International Conference on Information Integration and Web-based Applications & Services
Technical proficiency for IS Success
Computers in Human Behavior
International Journal of Productivity Management and Assessment Technologies
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Computer self-efficacy (CSE) is a person's judgment of his or her ability to use a computer system. We investigated cognitive engagement, prior experience, computer anxiety, and organizational support as determinants of CSE in the use of a multimedia ERP system's training tool. We also examined the impact of CSE on its acceptance. We determined the benefits of a sequential multi-method approach using structural equation modeling and neural network analysis. High reliability predictions of individual CSE were achieved with a sequential multi-method approach. Specifically, we obtained almost 68% perfect CSE group prediction overall, with almost 85% perfect CSE group prediction using fuzzy sets and over 94% accuracy within one group classification. The resulting CSE assessment and classification enables management interventions, such as allocating users to appropriate instruction for more effective training.