Document Type
Article
Publication Date
2012
Digital Object Identifier (DOI)
https://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.
Was this content written or created while at USF?
No
Citation / Publisher Attribution
Advances in Artificial Neural Systems, v. 2012, art. 517234, 12 p.
This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Scholar Commons Citation
Walczak, Steven, "Methodological Triangulation Using Neural Networks for Business Research" (2012). School of Information Faculty Publications. 169.
https://digitalcommons.usf.edu/si_facpub/169
