Methodological Triangulation Using Neural Networks for Business Research
Author
Source
Advances in Artificial Neural Systems
Issue
Vol. 2012, Issue 2012 (31 Dec. 2012), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2012-03-08
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Information Technology and Computer Science
Abstract EN
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.
American Psychological Association (APA)
Walczak, Steven. 2012. Methodological Triangulation Using Neural Networks for Business Research. Advances in Artificial Neural Systems،Vol. 2012, no. 2012, pp.1-12.
https://search.emarefa.net/detail/BIM-477961
Modern Language Association (MLA)
Walczak, Steven. Methodological Triangulation Using Neural Networks for Business Research. Advances in Artificial Neural Systems No. 2012 (2012), pp.1-12.
https://search.emarefa.net/detail/BIM-477961
American Medical Association (AMA)
Walczak, Steven. Methodological Triangulation Using Neural Networks for Business Research. Advances in Artificial Neural Systems. 2012. Vol. 2012, no. 2012, pp.1-12.
https://search.emarefa.net/detail/BIM-477961
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-477961