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A predictive study of antecedent variables of Passion towards Work

María-José Serrano-Fernández et al · Servicio de Publicaciones de la Universidad de Murcia · 2020

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Abstract: Background: The aim of this study was to conduct a correlational-predictive study of the antecedent variables of Passion towards Work. Method: The participants were 513 workers (48.1% male, 51.9% female), obtained through non-probability sampling. We used the FACTOR programs (7.2 version) and SPSS 20.0. Results: We found that variables such as Personality, Engagement, Self-efficacy, obsessive-compulsive component COI, Life satisfaction and Lifestyle were predictive of Passion towards Work. Conclusions: Passion towards Work can be predicted as follows: The variables Dedication, Growth, Physical activity, Satisfaction with life and Excessive responsibility were direct predictors of Harmonious Passion whereas Vigor was an inverse predictor. Similarly, the variables Absorption and Excessive responsibility were predictors of Obsessive Passion, whereas Satisfaction with life, Openness to experience and Kindness were negative predictors.

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APA 7

al, M. J. S. F. E. (2020). A predictive study of antecedent variables of Passion towards Work. https://doi.org/10.6018/analesps.35.3.331091

MLA

al, María-José Serrano-Fernández et. "A predictive study of antecedent variables of Passion towards Work." 2020. https://doi.org/10.6018/analesps.35.3.331091.

Chicago

al, María-José Serrano-Fernández et. 2020. "A predictive study of antecedent variables of Passion towards Work.". https://doi.org/10.6018/analesps.35.3.331091.

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al, M. J. S. F. E. 2020, A predictive study of antecedent variables of Passion towards Work, Servicio de Publicaciones de la Universidad de Murcia, available at: https://doi.org/10.6018/analesps.35.3.331091 [Accessed 8 Aug. 2026].

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Title
A predictive study of antecedent variables of Passion towards Work
Author / contributors
María-José Serrano-Fernández et al
Publisher
Servicio de Publicaciones de la Universidad de Murcia
Publication year
2020
ISSN
0212-9728
ISSN
0212-9728
Language
English

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