Picture of virus under microscope

Research under the microscope...

The Strathprints institutional repository is a digital archive of University of Strathclyde research outputs.

Strathprints serves world leading Open Access research by the University of Strathclyde, including research by the Strathclyde Institute of Pharmacy and Biomedical Sciences (SIPBS), where research centres such as the Industrial Biotechnology Innovation Centre (IBioIC), the Cancer Research UK Formulation Unit, SeaBioTech and the Centre for Biophotonics are based.

Explore SIPBS research

Modeling complex interactions of switching barriers - a latent profile approach

Eiting, Alexander and Blut, Marcus and Evanschitzky, Heiner and Woisetschläger, David M. (2009) Modeling complex interactions of switching barriers - a latent profile approach. In: AMA Summer Educators' Conference 2008 Enhancing Knowledge Development in Marketing. AMA Summer Educators Conference, 19 . American Marketing Association, Chigaco, USA, pp. 121-122. ISBN 0877573336

Full text not available in this repository. (Request a copy from the Strathclyde author)

Abstract

Existing research on drivers of customer loyalty such as satisfaction and switching barriers often does not account for possible nonlinear interaction effects (e.g., Jones et al. 2000). General and generalized linear models (e.g., linear regression, logistic regression, or structural equation modeling), however, typically do not fully account for the possible nonlinear interactive relationships that emerge when an entire profile of variables is considered in its holism. In consequence, the understanding of interaction effects is somewhat limited and subject to misinterpretations. This paper examines moderating effects of switching barriers on the link of satisfaction on loyalty intention. The methodical challenge is set by the need to capture possible nonlinear interactions among many moderating factors. Therefore, this article aims (a) to provide a demonstration with a particular focus on the use of finite mixture models to capture these interactive effects and (b) to compare these results to those of a traditional binomial logit regression.