%0 Journal Article
%T Methodological Triangulation Using Neural Networks for Business Research
%A Steven Walczak
%J Advances in Artificial Neural Systems
%D 2012
%I Hindawi Publishing Corporation
%R 10.1155/2012/517234
%X 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. 1. Introduction Artificial Neural networks (ANNs) have been used as a popular research and implementation paradigm in multiple domains for several decades now [1每9]. Recent literature is advocating the further usage of ANNs as a research methodology, especially in previously untried or underutilized domains [10, 11]. However, due to the early premise that ANNs are black boxes (i.e., it is difficult to evaluate the contribution of the independent variables) the demonstration of rigor and generalization of results from neural network research has been problematic. Similarities between ANNs and various statistical methods (which have been shown to be both rigorous and generalizable) have been described for potential adopters [10, 12]. A common research paradigm for ANN researchers is to compare results obtained using an ANN to other more traditional statistical methods, including regression [13每16], discriminant analysis [17每21], other statistical methods [22每24], and multiple statistical methods [25每28]. Of the 16 articles just referenced, the majority of these results show ANNs being either similar to (with 2 being similar) or better than (with 12 outperforming) the compared statistical methods within the specific application domain. While ANNs have a history, though short, their black box nature has led to adoption resistance by numerous-business related disciplines [29]. Methodological triangulation may help to overcome these adoption and usage reservations as well as providing a means for improving the overall efficacy of ANN applications. Methodological triangulation is the utilization of multiple methods on
%U http://www.hindawi.com/journals/aans/2012/517234/