This paper aims at developing a sound methodology for both extending Importance-Performance Analysis (IPA) to the consideration of many tourism destinations simultaneously, and defining the prioritization of core attributes in each. Multivariate Competition-based IPA (MCIPA) allows to provide detailed information of particular utility for destination management, through a synthetic representation, accounting also for the geographical and administrative context. The conceptual framework of strategic groups, applied for the first time to tourist destinations, is the interpretative key of MCIPA. It makes the comparison of importance and performance, of various attributes in many areas, feasible and synthetic. It leads to interpret combinations of importance values in terms of target segment's preferences, defining destinations with similar performances on the same target segment as direct rivals. Based on a set of very broadly applicable statistical techniques, MCIPA helps addressing some methodological and interpretative drawbacks of IPA. An application to Italian provinces is shown.
Andrea, G., Annalisa, S. (2017). Destinations strategic groups via Multivariate Competition-based IPA. TOURISM MANAGEMENT, 58, 40-50 [10.1016/j.tourman.2016.10.004].
Destinations strategic groups via Multivariate Competition-based IPA
GUIZZARDI, ANDREA;STACCHINI, ANNALISA
2017
Abstract
This paper aims at developing a sound methodology for both extending Importance-Performance Analysis (IPA) to the consideration of many tourism destinations simultaneously, and defining the prioritization of core attributes in each. Multivariate Competition-based IPA (MCIPA) allows to provide detailed information of particular utility for destination management, through a synthetic representation, accounting also for the geographical and administrative context. The conceptual framework of strategic groups, applied for the first time to tourist destinations, is the interpretative key of MCIPA. It makes the comparison of importance and performance, of various attributes in many areas, feasible and synthetic. It leads to interpret combinations of importance values in terms of target segment's preferences, defining destinations with similar performances on the same target segment as direct rivals. Based on a set of very broadly applicable statistical techniques, MCIPA helps addressing some methodological and interpretative drawbacks of IPA. An application to Italian provinces is shown.File | Dimensione | Formato | |
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