Show simple item record

dc.contributor.authorSarra, Annalina
dc.contributor.authorEvangelista, Adelia
dc.contributor.authordi battista, tonio
dc.date.accessioned2022-06-02T04:22:35Z
dc.date.available2022-06-02T04:22:35Z
dc.date.issued2021
dc.date.submitted2022-06-01T12:20:17Z
dc.identifierONIX_20220601_9788855184618_535
dc.identifier2704-5846
dc.identifierhttps://library.oapen.org/handle/20.500.12657/56350
dc.identifier.urihttps://directory.doabooks.org/handle/20.500.12854/83032
dc.description.abstractProtected areas are well-defined geographical spaces that, in view of their recognized, natural, ecological or cultural values, receive protection. They have the twofold mandate of protection of natural resources and providing a space for nature-based tourism activities. In the last years, the nature-based tourism is experiencing positive and sustainable growth worldwide. Understanding the value attached by visitors to their destination and know their assessment on various activities in which they are engaged during their stay is a key element in shaping tourist’s satisfaction. Objective of this research was to identify the profiles of visitors to tourist destinations within Natural Park of Majella (Abruzzo region, Italy) and to assess the link with their satisfaction. The data for this study were collected by means of a structured questionnaire administrated to tourists who visited the sites of the protected area during the last three summer months. A total of 150 valid questionnaires were obtained and form the base of the data analysis. Through a Bayesian model-based clustering, better known as Bayesian Profile Regression, we partition visitors into clusters, characterized by similar profiles in terms of their demographic characteristics (age, gender, education attainment), as well as, in terms of the features of their travel behaviour (accommodation, length of stay, past visitation experience). A further benefit of the followed approach lies in the ability of that Bayesian technique of simultaneously estimating the contribute of all covariates to the outcome of interest. In our context, we explore the association of detected groups with the tourists’ satisfaction. In the survey, the global quality of tourism service is segmented into single features and respondents were asked to give their level of appreciation on a five-point Likert satisfaction scale. To estimate the latent trait measured by the items and related to the overall satisfaction we followed an IRT modelling.
dc.languageEnglish
dc.relation.ispartofseriesProceedings e report
dc.rightsopen access
dc.subject.otherBayesian Profile Regression
dc.subject.otherTourists' satisfaction
dc.subject.otherProtected areas
dc.subject.otherIRT modelling
dc.titleChapter Assessment of visitors’ perceptions in protected areas through a model-based clustering
dc.typechapter
oapen.identifier.doi10.36253/978-88-5518-461-8.46
oapen.relation.isPublishedBy2ec4474d-93b1-4cfa-b313-9c6019b51b1a
oapen.relation.isbn9788855184618
oapen.pages6
oapen.place.publicationFlorence
dc.seriesnumber132


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record

open access
Except where otherwise noted, this item's license is described as open access