Advances in spatial dependence modeling of consumer attitudes with Bayesian factor models

Abstract

The primary goal of this thesis was to understand how the spatial dependence of consumer attitudes can be modeled, what additional benefits the recovering of spatial dependence can bring and how these benefits can be utilized by researchers and practitioners. So far, most of the approaches to study attitudes assumed spatial independence, i.e. that location does not matter. We argue, that in many cases space matters and attitudes observed in one location are correlated with attitudes observed in other, usually neighboring, locations. Three studies contributed to achieve the aforementioned goal. The first study addressed the specification issues of spatial models and the consequences of their misspecification. Recommendations for researchers regarding model selection and specification of weights matrices were formulated. In the second study a new Bayesian Spatial Factor Analytic (BSFA) model was developed. It was applied on Schwartz value priority data obtained from five European countries and this application revealed interesting spatial patterns of some value domains which were not possible to detect otherwise. Finally, the third study was devoted to extending the proposed BSFA model by introducing exogenous covariates which can influence the latent constructs and adapting the model with cutoff point formulation to accommodate the discrete measurement scale. The applicability of the model was demonstrated in empirical study on consumer attitudes in the financial domain. Further, a new procedure for spatial scheduling, based on the model results, was introduced which has substantial managerial relevance.