Profiling tourists based on their perceptions of the trustworthiness of different types of peer-to-peer applications

Giacomo Del Chiappa; Carlota Lorenzo-Romero; María del Carmen Alarcón-del-Amo

doi:10.1080/13683500.2015.1077208

Profiling tourists based on their perceptions of the trustworthiness of different types of peer-to-peer applications

Giacomo Del Chiappa, Carlota Lorenzo-Romero, María del Carmen Alarcón-del-Amo

Research output: Contribution to journal › Article › Research › peer-review

17 Citations (Scopus)

Abstract

© 2015 Taylor & Francis. This research profiles tourists based on the extent to which they trust user-generated content (UGC) uploaded in different types of peer-to-peer applications, also considering their socio-demographic characteristics, frequency of travel and motivations for using the Internet and UGC when making their travel choices. For this purpose, latent class segmentation was applied on a sample of 609 Italian tourists and three different clusters were identified, namely: “distrustful tourists”, “untrusted tourists” and “social web tourists”. The findings suggest that hospitality marketers should improve their social media strategy by focusing their attention on Travel 2.0 applications according to the socio-demographic and behavioural characteristics of their target market.

Original language	English
Pages (from-to)	259-276
Journal	Current Issues in Tourism
Volume	21
Issue number	3
DOIs	https://doi.org/10.1080/13683500.2015.1077208
Publication status	Published - 11 Feb 2018

Keywords

Travel 2.0 applications
latent segmentation analysis
motivations
socio-demographic characteristics
trustworthiness
user-generated content

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title = "Profiling tourists based on their perceptions of the trustworthiness of different types of peer-to-peer applications",

abstract = "{\textcopyright} 2015 Taylor & Francis. This research profiles tourists based on the extent to which they trust user-generated content (UGC) uploaded in different types of peer-to-peer applications, also considering their socio-demographic characteristics, frequency of travel and motivations for using the Internet and UGC when making their travel choices. For this purpose, latent class segmentation was applied on a sample of 609 Italian tourists and three different clusters were identified, namely: “distrustful tourists”, “untrusted tourists” and “social web tourists”. The findings suggest that hospitality marketers should improve their social media strategy by focusing their attention on Travel 2.0 applications according to the socio-demographic and behavioural characteristics of their target market.",

keywords = "Travel 2.0 applications, latent segmentation analysis, motivations, socio-demographic characteristics, trustworthiness, user-generated content",

author = "{Del Chiappa}, Giacomo and Carlota Lorenzo-Romero and Alarc{\'o}n-del-Amo, {Mar{\'i}a del Carmen}",

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AU - Del Chiappa, Giacomo

AU - Lorenzo-Romero, Carlota

AU - Alarcón-del-Amo, María del Carmen

PY - 2018/2/11

Y1 - 2018/2/11

N2 - © 2015 Taylor & Francis. This research profiles tourists based on the extent to which they trust user-generated content (UGC) uploaded in different types of peer-to-peer applications, also considering their socio-demographic characteristics, frequency of travel and motivations for using the Internet and UGC when making their travel choices. For this purpose, latent class segmentation was applied on a sample of 609 Italian tourists and three different clusters were identified, namely: “distrustful tourists”, “untrusted tourists” and “social web tourists”. The findings suggest that hospitality marketers should improve their social media strategy by focusing their attention on Travel 2.0 applications according to the socio-demographic and behavioural characteristics of their target market.

AB - © 2015 Taylor & Francis. This research profiles tourists based on the extent to which they trust user-generated content (UGC) uploaded in different types of peer-to-peer applications, also considering their socio-demographic characteristics, frequency of travel and motivations for using the Internet and UGC when making their travel choices. For this purpose, latent class segmentation was applied on a sample of 609 Italian tourists and three different clusters were identified, namely: “distrustful tourists”, “untrusted tourists” and “social web tourists”. The findings suggest that hospitality marketers should improve their social media strategy by focusing their attention on Travel 2.0 applications according to the socio-demographic and behavioural characteristics of their target market.

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KW - latent segmentation analysis

KW - motivations

KW - socio-demographic characteristics

KW - trustworthiness

KW - user-generated content

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