The role of social networks for decision-making about tourism destinations
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/20.500.11960/3355 |
Resumo: | The influence of social networks (SN) on destination selection was studied as part of tourism management/marketing strategy. It also presented the new extension of technology acceptance model (TAM), considering the constructs of perceived usefulness (PU), perceived ease of use (PEOU) attitude towards the use (ATU), perceived enjoyment (PE), e-word-of-mouth (eWOM) and previous influence factors (PIF) to assess tourists' behavioural intention (BI) towards the use of SN for choosing the tourism destination. For such, we performed confirmatory factor analysis (CFA) and the hypotheses were tested by structural equation modelling (SEM). Logistic regression was conducted to explain the influence of SN on choosing and finding destination information. Of all the respondents, 66.5% had used SN to get information or decide on their tourist destination. Facebook and Instagram exhibited greater impact on tourist destination selection than LinkedIn. The results provided insights for marketers, governments and tourism related organisations. |
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The role of social networks for decision-making about tourism destinationsSocial networksTourism destinationInfluenceTourist behaviourAnalysed theoriesConstructsTechnology acceptance modelTAMPerceived ease of usePEOUAttitude towards the useATUPrevious influence factorsPIFThe influence of social networks (SN) on destination selection was studied as part of tourism management/marketing strategy. It also presented the new extension of technology acceptance model (TAM), considering the constructs of perceived usefulness (PU), perceived ease of use (PEOU) attitude towards the use (ATU), perceived enjoyment (PE), e-word-of-mouth (eWOM) and previous influence factors (PIF) to assess tourists' behavioural intention (BI) towards the use of SN for choosing the tourism destination. For such, we performed confirmatory factor analysis (CFA) and the hypotheses were tested by structural equation modelling (SEM). Logistic regression was conducted to explain the influence of SN on choosing and finding destination information. Of all the respondents, 66.5% had used SN to get information or decide on their tourist destination. Facebook and Instagram exhibited greater impact on tourist destination selection than LinkedIn. The results provided insights for marketers, governments and tourism related organisations.2023-06-02T11:57:11Z2023-01-01T00:00:00Z20232022-11-23T13:29:48Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/20.500.11960/3355eng1741-810010.1504/IJIMA.2023.128148metadata only accessinfo:eu-repo/semantics/openAccessVieira, Bruno MiguelBorges, Ana PintoVieira, Elvira Pachecoreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-06-08T06:45:26Zoai:repositorio.ipvc.pt:20.500.11960/3355Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T17:59:58.100270Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
The role of social networks for decision-making about tourism destinations |
title |
The role of social networks for decision-making about tourism destinations |
spellingShingle |
The role of social networks for decision-making about tourism destinations Vieira, Bruno Miguel Social networks Tourism destination Influence Tourist behaviour Analysed theories Constructs Technology acceptance model TAM Perceived ease of use PEOU Attitude towards the use ATU Previous influence factors PIF |
title_short |
The role of social networks for decision-making about tourism destinations |
title_full |
The role of social networks for decision-making about tourism destinations |
title_fullStr |
The role of social networks for decision-making about tourism destinations |
title_full_unstemmed |
The role of social networks for decision-making about tourism destinations |
title_sort |
The role of social networks for decision-making about tourism destinations |
author |
Vieira, Bruno Miguel |
author_facet |
Vieira, Bruno Miguel Borges, Ana Pinto Vieira, Elvira Pacheco |
author_role |
author |
author2 |
Borges, Ana Pinto Vieira, Elvira Pacheco |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Vieira, Bruno Miguel Borges, Ana Pinto Vieira, Elvira Pacheco |
dc.subject.por.fl_str_mv |
Social networks Tourism destination Influence Tourist behaviour Analysed theories Constructs Technology acceptance model TAM Perceived ease of use PEOU Attitude towards the use ATU Previous influence factors PIF |
topic |
Social networks Tourism destination Influence Tourist behaviour Analysed theories Constructs Technology acceptance model TAM Perceived ease of use PEOU Attitude towards the use ATU Previous influence factors PIF |
description |
The influence of social networks (SN) on destination selection was studied as part of tourism management/marketing strategy. It also presented the new extension of technology acceptance model (TAM), considering the constructs of perceived usefulness (PU), perceived ease of use (PEOU) attitude towards the use (ATU), perceived enjoyment (PE), e-word-of-mouth (eWOM) and previous influence factors (PIF) to assess tourists' behavioural intention (BI) towards the use of SN for choosing the tourism destination. For such, we performed confirmatory factor analysis (CFA) and the hypotheses were tested by structural equation modelling (SEM). Logistic regression was conducted to explain the influence of SN on choosing and finding destination information. Of all the respondents, 66.5% had used SN to get information or decide on their tourist destination. Facebook and Instagram exhibited greater impact on tourist destination selection than LinkedIn. The results provided insights for marketers, governments and tourism related organisations. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-11-23T13:29:48Z 2023-06-02T11:57:11Z 2023-01-01T00:00:00Z 2023 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/20.500.11960/3355 |
url |
http://hdl.handle.net/20.500.11960/3355 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
1741-8100 10.1504/IJIMA.2023.128148 |
dc.rights.driver.fl_str_mv |
metadata only access info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
metadata only access |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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