Picture of athlete cycling

Open Access research with a real impact on health...

The Strathprints institutional repository is a digital archive of University of Strathclyde's Open Access research outputs. Strathprints provides access to thousands of Open Access research papers by Strathclyde researchers, including by researchers from the Physical Activity for Health Group based within the School of Psychological Sciences & Health. Research here seeks to better understand how and why physical activity improves health, gain a better understanding of the amount, intensity, and type of physical activity needed for health benefits, and evaluate the effect of interventions to promote physical activity.

Explore open research content by Physical Activity for Health...

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.