The complex influences of quality management leadership and workforce involvement on manufacturing firm success

Purpose - Quality management constructs related to management leadership and workforce involvement have consistently shown strong correlation with firm success for years. However, there is an increasing body of research based on complexity theory (CT) suggesting that constructs such as these should be viewed as variables in a complex system with inter-dependencies, interactions, and potentially nonlinear relationships. Despite the significant body of conceptual research related to CT, there is a lack of methodological research into these potentially nonlinear effects. The purpose of this paper is to demonstrate the theoretical and practical importance of non-linear terms in a multivariate polynomial model as they become more significant predictors of firm success in collaborative environments and less significant in more rigidly controlled work environments. Design/methodology/approach - Multivariate polynomial regression methods are used to examine the significance and effect sizes of interaction and quadratic terms in operations scenarios expected to have varying degrees of complex and complex adaptive behaviors. Findings - The results find that in highly collaborative work environments, non-linear and interaction effects become more significant predictors of success than the linear terms in the model. In more rigid, less collaborative work environments, these effects are not present or significantly reduced in effect size. Research limitations/implications - This study shows that analytical methods sensitive to detecting and measuring nonlinearities in relationships such as multivariate polynomial regression models enhance our theoretical understanding of the relationships between constructs when the theory predicts that complex and complex adaptive behaviors are present and important. Originality/value - This study demonstrates that complex adaptive behaviors between management and the workforce exist in certain environments and provide greater understanding of factor relationships relating to firm success than more traditional linear analytical methods.