Methodological Triangulation Using Neural Networks for Business Research

Author

Walczak, Steven

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