Predicting technostress: the Big Five model of personality and subjective well-being

The main goal of the current study is to broaden the knowledge on the association between personality, subjective well-being (SWB) and technostress in an academic context. This research specifically examines the prevalence of technostress in a European university sample. It also explores the relatio...

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Detalles Bibliográficos
Autores: Cuadrado González, Dámaris, Otero Moral, Inmaculada, Martínez Gómez, Alexandra, París Rodríguez, Tania, Moscoso Ruibal, Silvia
Tipo de recurso: artículo
Fecha de publicación:2024
País:España
Institución:Universidad de Santiago de Compostela (USC)
Repositorio:Minerva. Repositorio Institucional de la Universidad de Santiago de Compostela
Idioma:inglés
OAI Identifier:oai:minerva.usc.gal:10347/37914
Acceso en línea:https://hdl.handle.net/10347/37914
Access Level:acceso abierto
Palabra clave:Technostress
Big Five model
Subjetive well-being
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spelling Predicting technostress: the Big Five model of personality and subjective well-beingCuadrado González, DámarisOtero Moral, InmaculadaMartínez Gómez, AlexandraParís Rodríguez, TaniaMoscoso Ruibal, SilviaTechnostressBig Five modelSubjetive well-beingThe main goal of the current study is to broaden the knowledge on the association between personality, subjective well-being (SWB) and technostress in an academic context. This research specifically examines the prevalence of technostress in a European university sample. It also explores the relationship between technostress and its dimensions with the Big Five model of personality and with SWB and its affective and cognitive components. Finally, the combined predictive validity of the Big Five and SWB on technostress is tested. The sample was composed of 346 undergraduate students. Correlational and multiple regression analyses were carried out. Results show that fatigue and anxiety are the most frequently experienced dimensions of technostress. Emotional stability, openness to experience, and SWB are negatively and significantly correlated to technostress. Multiple regression analyses show that the Big Five factors and SWB account for technostress variance, the main predictor being the affective component of SWB. These results contribute to a more comprehensive understanding of technostress and suggest that personality traits and SWB are important factors in its prediction. The theoretical and practical implications will be discussed.Public Library of ScienceUniversidade de Santiago de Compostela. Departamento de Ciencia Política e Socioloxía20242024-11-0420242024-11-04journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/10347/37914reponame:Minerva. Repositorio Institucional de la Universidad de Santiago de Compostelainstname:Universidad de Santiago de Compostela (USC)InglésengAgencia Estatal de Investigación http://dx.doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020 PID2020-114984GB-I00 RELACION ENTRE BIENESTAR SUBJETIVO Y PERSONALIDAD OSCURA Y LA PREDICCION DEL LAS DIMENSIONES DEL DESEMPEÑO OCUPACIONAL Y ACADEMICOopen accesshttp://purl.org/coar/access_right/c_abf2© 2024 Cuadrado et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.http://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:minerva.usc.gal:10347/379142026-06-15T12:47:27Z
dc.title.none.fl_str_mv Predicting technostress: the Big Five model of personality and subjective well-being
title Predicting technostress: the Big Five model of personality and subjective well-being
spellingShingle Predicting technostress: the Big Five model of personality and subjective well-being
Cuadrado González, Dámaris
Technostress
Big Five model
Subjetive well-being
title_short Predicting technostress: the Big Five model of personality and subjective well-being
title_full Predicting technostress: the Big Five model of personality and subjective well-being
title_fullStr Predicting technostress: the Big Five model of personality and subjective well-being
title_full_unstemmed Predicting technostress: the Big Five model of personality and subjective well-being
title_sort Predicting technostress: the Big Five model of personality and subjective well-being
dc.creator.none.fl_str_mv Cuadrado González, Dámaris
Otero Moral, Inmaculada
Martínez Gómez, Alexandra
París Rodríguez, Tania
Moscoso Ruibal, Silvia
author Cuadrado González, Dámaris
author_facet Cuadrado González, Dámaris
Otero Moral, Inmaculada
Martínez Gómez, Alexandra
París Rodríguez, Tania
Moscoso Ruibal, Silvia
author_role author
author2 Otero Moral, Inmaculada
Martínez Gómez, Alexandra
París Rodríguez, Tania
Moscoso Ruibal, Silvia
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Universidade de Santiago de Compostela. Departamento de Ciencia Política e Socioloxía
dc.subject.none.fl_str_mv Technostress
Big Five model
Subjetive well-being
topic Technostress
Big Five model
Subjetive well-being
description The main goal of the current study is to broaden the knowledge on the association between personality, subjective well-being (SWB) and technostress in an academic context. This research specifically examines the prevalence of technostress in a European university sample. It also explores the relationship between technostress and its dimensions with the Big Five model of personality and with SWB and its affective and cognitive components. Finally, the combined predictive validity of the Big Five and SWB on technostress is tested. The sample was composed of 346 undergraduate students. Correlational and multiple regression analyses were carried out. Results show that fatigue and anxiety are the most frequently experienced dimensions of technostress. Emotional stability, openness to experience, and SWB are negatively and significantly correlated to technostress. Multiple regression analyses show that the Big Five factors and SWB account for technostress variance, the main predictor being the affective component of SWB. These results contribute to a more comprehensive understanding of technostress and suggest that personality traits and SWB are important factors in its prediction. The theoretical and practical implications will be discussed.
publishDate 2024
dc.date.none.fl_str_mv 2024
2024-11-04
2024
2024-11-04
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/10347/37914
url https://hdl.handle.net/10347/37914
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv Agencia Estatal de Investigación http://dx.doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020 PID2020-114984GB-I00 RELACION ENTRE BIENESTAR SUBJETIVO Y PERSONALIDAD OSCURA Y LA PREDICCION DEL LAS DIMENSIONES DEL DESEMPEÑO OCUPACIONAL Y ACADEMICO
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
http://creativecommons.org/licenses/by/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Public Library of Science
publisher.none.fl_str_mv Public Library of Science
dc.source.none.fl_str_mv reponame:Minerva. Repositorio Institucional de la Universidad de Santiago de Compostela
instname:Universidad de Santiago de Compostela (USC)
instname_str Universidad de Santiago de Compostela (USC)
reponame_str Minerva. Repositorio Institucional de la Universidad de Santiago de Compostela
collection Minerva. Repositorio Institucional de la Universidad de Santiago de Compostela
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,811543

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