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

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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.
Original languageEnglish
Title of host publication2017 TTRA Conference Proceedings / Tourism Travel and Research Association: Advancing Tourism Research Globally
Number of pages10
Place of PublicationAtlanta, GA
PublisherAssociation for Information Systems. AIS Electronic Library (AISeL)
Publication date2017
Article number12
Publication statusPublished - 2017
Event48th Annual International Conference of the Travel and Tourism Research Association. TTRA 2017 - Hilton, Quebec City, Canada
Duration: 20 Jun 201722 Jun 2017
Conference number: 48


Conference48th Annual International Conference of the Travel and Tourism Research Association. TTRA 2017
CityQuebec City
Internet address

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