Navigating work and study: The interplay of time-spatial flexible work arrangements, workload, and work-life integration among undergraduate working students
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DOIhttp://dx.doi.org/10.21511/ppm.23(2).2025.22
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Article InfoVolume 23 2025, Issue #2, pp. 319-330
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Creative Commons Attribution 4.0 International License
The growing number of undergraduate students juggling jobs and studies poses problems for attaining work-life integration. Understanding the factors influencing this integration is critical, especially in the context of flexible work arrangements (FWA) and workload. The purpose of this study is to investigate the impact of FWA and workload on work-life integration among working students in Indonesia, focusing on the contradiction that flexibility promotes balance and increases job demands. Data were obtained from 125 working students in the service industry on Indonesia’s two most populous islands, Java and Sumatra, using structural equation modeling partial least squares (SEM-PLS). The study found that FWA considerably increases workload (β = 0.846, p = 0.000) and improves work-life integration (β = 0.638, p = 0.000). Workload is a mediator between FWA and work-life integration (β = 0.540, p = 0.000), indicating that flexibility increases job demands but improves work-life integration. Workload has an adjusted R² of 0.714, explaining 71.4% of the variance. Work-life integration is explained by workload and FWA combined, accounting for 83.0%. These findings emphasize that, while a higher workload may appear contradictory, it promotes better time management and financial stability, improving well-being and work-life integration. Institutions and employers must ensure that task expectations remain moderate to prevent stress from negating the benefits of flexibility. This study presents empirical evidence to support policies that promote flexibility while avoiding excessive job demands.
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JEL Classification (Paper profile tab)M50, J22, I23, A22
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References57
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Tables4
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Figures2
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- Figure 1. Conceptual framework
- Figure 2. Structural and measurement model
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- Table 1. Respondents’ characteristics
- Table 2. Factor loadings, convergent validity, and reliability
- Table 3. Discriminant validity
- Table 4. Path coefficients
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