Work-life balance and organisational commitment are widely discussed constructs in the academic community and amongst human ressource managers. This publications examines the relationships of work-life balance measures, work-life balance and organisational commitment through structural equation modelling.
Abstract
Purpose
Our research aims to determine the impact of work-life balance on the components of organisational commitment in comparison to other key determinants and the influence of work-life balance measures on work-life balance.
Methodology
We conducted two studies with 760 and 389 participants each. Our model, consisting of work-life balance measures, work-life balance, other working conditions, and organisational commitment is tested by structural equation modelling.
Findings
Work-life balance positively influences the affective and normative component of organisational commitment. This influence, however, is weaker than the impact of other working conditions, like perceived leadership behaviour and the working climate. The only measure that has a significant impact on work-life balance is flexible working time.
Practical Implications
Our results imply that it is beneficial for organisations to improve their employees’ work-life balance. Employers should, however, focus primarily on other working conditions when aiming to improve the organisational commitment of their employees. When launching work-life balance initiatives, organisations should focus on flexible working time arrangements.
Originality
So far, it has not been sufficiently examined how a strong work-life balance impacts organisational commitment in comparison to other key determinants. Furthermore, most studies investigate the correlation between work-life balance measures and organisational commitment. We, on the other hand, propose a model in which work life-balance plays a mediating role between organisational measures and commitment and is analysed in comparison to other “regular” working conditions.
Publication and Download
Weiterführende Informationen
Nähere Informationen zum eingesetzten Auswertungsverfahren finden Sie hier: Partial Least Squares Path Modelling (in R mit plspm).
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