Categorization of Destinations and Formation of Mental Destination Representations: A Parallel Biclustering Analysis

Segmentation analysis in tourism research is challenged by an excessive number of variables used. The biclustering approach to segmentation is considered a promising approach to address the problem of an inappropriately large number of variables involved. This paper introduces, to tourism research, a disruptive biclustering approach advanced by recent developments of Bayesian relational modeling. This new approach, for the first time in tourism research, allows to design and conduct a segmentation analysis by simultaneously biclustering multiple datasets consisting of cases and variables in a parallel format. We demonstrate how the new analytical framework can be applied to analyze and compare patterns of associations which individuals have of multiple destinations. Subsequently, this paper elaborates potential contributions the Bayesian relational modeling framework makes to the tourism research discipline by outlining a conceptual idea of the segmentation analysis that enables the simultaneous biclustering of individuals and their associations for multiple destinations in a parallel format.