Presenter Bios

Fumiko Kano Glückstad is an Associate Professor at Copenhagen Business School. She holds PhD specialized in cross-cultural communication and cognition. Her work has appears in Journal of Cross-Cultural Psychology, Artificial Intelligence and Law among others. She is co-founder of a governmentally funded project, UMAMI: Understanding Mindsets Across Markets, Internationally (http://sf.cbs.dk/umami).

Alexander Josiassen is an Associate Professor at the Copenhagen Business School. His work has appeared in several marketing and tourism journals, such as Journal of Marketing, Tourism Management, Journal of Travel Research, Annals of Tourism Research, and Journal of Retailing.

Florian Kock is a doctoral candidate at the Copenhagen Business School and Royal Melbourne Institute of Technology. His work has appeared in tourism journals such as Journal of Travel Research and Annals of Tourism Research.

A. George Assaf is an Associate Professor at the Isenberg School of Management, University of Massachusetts- Amherst. His work has appeared in several tourism, marketing and economic journals, such as Tourism Management, Journal of Travel Research, Annals of Tourism Research, and Journal of Retailing.

Abstract

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.

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Short Abstract

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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.