Universal Versus Contextual Effects on TQM: A Triangulation Study Using Neural Networks
total quality management, contextual factors, business results, artificial neural networks, structural equation modeling, survey
Digital Object Identifier (DOI)
The objective of this study is to extend previous research on total quality management (TQM)-context-performance relationships and ‘fit’ using multiple methods. We combine artificial neural networks (ANNs) with structural equation modelling (SEM) to analyse several hypotheses and propositions. This is the first study in this area of research that utilises ANNs and a triangulation technique in the presence of several contextual factors. The SEM analyses suggest that company size and industry type may have contingency effects on some of the TQM practices and/or TQM-performance relationships. However, the ANN models have shown that these two contingency factors do not moderate TQM outcomes, implying that all organisations can benefit from TQM regardless of size and type. As well, these models show that formal TQM implementation and/or ISO certifications do not add any predictive power to the ANN models except in one case: TQM implementation and/or ISO certification added to organisational effectiveness and customer results to predict financial and market (F&M) results. The results further indicate that even though implementing TQM alone has a bigger impact on F&M results than obtaining ISO certification alone, combining the two will have an even greater impact on these results. Joint implementation leads to greater improvements in organisational effectiveness, which, in turn, has a positive effect on customer results and consequently F&M results. This is a unique finding within the context of moderator effects on TQM-performance relationships.
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Citation / Publisher Attribution
Production Planning & Control, v. 28, issue 5, p. 367-386
Scholar Commons Citation
Sila, Ismail and Walczak, Steven, "Universal Versus Contextual Effects on TQM: A Triangulation Study Using Neural Networks" (2017). School of Information Faculty Publications. 336.