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...
| Autores: | , , , , |
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| 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 |
| Sumario: | 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. |
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