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vol.39 número1Psychological career resources and coping resources of the young unemployed African graduate: an exploratory studyPsychological emPowerment, job insecurity and employee engagement índice de autoresíndice de assuntospesquisa de artigos
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SA Journal of Industrial Psychology

versão On-line ISSN 2071-0763
versão impressa ISSN 0258-5200

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DU PLOOY, Janine  e  ROODT, Gert. Biographical and demographical variables as moderators in the prediction of turnover intentions. SA j. ind. Psychol. [online]. 2013, vol.39, n.1, pp.01-12. ISSN 2071-0763.

ORIENTATION: The aim of the study was to explore the possible moderation effects of biographical and demographical variables on a prediction model of turnover intention (TI). RESEARCH PURPOSE: The main purpose of the study was to determine how biographical and demographical variables have an impact on predictors of turnover intentions. MOTIVATION FOR THE STUDY: Twenty-first century organisations face significant challenges in the management of talent and human capital. One in particular is voluntary employee turnover and the lack of appropriate business models to track this process. RESEARCH DESIGN, APPROACH, AND METHOD: A secondary data analysis (SDA) was performed in a quantitative research tradition on the cross-sectional survey sample (n = 2429). Data were collected from a large South African Information and Communication Technologies (ICT) sector company (N = 23 134). MAIN FINDINGS: The results of the study confirmed significant moderation effects regarding race, age, and marital status in the prediction equations of TIs. PRACTICAL AND MANAGERIAL IMPLICATIONS: Practical implications of the study suggested increased understanding of workforce diversity effects within the human resource (HR) value chain, with resultant evidence-based, employee retention strategies and interventions. Issues concerning talent management could also be addressed. CONTRIBUTION AND VALUE-ADD: The study described in this article took Industrial/Organisational (I/O) psychological concepts and linked them in unique combinations to establish better predictive validity of a more comprehensive turnover intentions model.

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