Document Type

Article

Publication Date

1-1-2012

Digital Object Identifier (DOI)

http://dx.doi.org/10.1155/2012/517234

Abstract

Artificial neural network (ANN) modeling methods are becoming more widely used as both a research and application paradigm across a much wider variety of business, medical, engineering, and social science disciplines. The combination or triangulation of ANN methods with more traditional methods can facilitate the development of high-quality research models and also improve output performance for real world applications. Prior methodological triangulation that utilizes ANNs is reviewed and a new triangulation of ANNs with structural equation modeling and cluster analysis for predicting an individual's computer self-efficacy (CSE) is shown to empirically analyze the effect of methodological triangulation, at least for this specific information systems research case. A new construct, engagement, is identified as a necessary component of CSE models and the subsequent triangulated ANN models are able to achieve an 84% CSE group prediction accuracy.

Comments

This article was written before Steven Walczak was affiliated with the University of South Florida.

Rights Information

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.

Was this content written or created while at USF?

false

Citation / Publisher Attribution

Advances in Artificial Neural Systems, v. 2012, article ID 517234, p. 1-12.

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