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FRONTIERS PSYCHOLOGY ORG · Employee Engagement, L&D

Work–family conflict and motivation: the mediating role of perceived visionary leadership and insights from explainable machine learning

Not yet classifiedby Edip Doğan, Erman Kılınç, Kadir AktaşNovember 10, 2026 58 min read
work-family conflict employee motivation visionary leadership conservation of resources theory self-determination theory age and motivation explainable ai machine learning in hr research pls-sem organisational behaviour

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Licence. The original is licensed under CC BY 4.0. This page adapts it: the summary and analysis are ours, not the authors’.

Editorial verdict

Methodologically ambitious but limited in generalisability — the PLS-SEM findings are modest and the machine learning layer adds interpretive texture rather than predictive power; treat the mediation finding on visionary leadership as hypothesis-generating, not confirmatory.

Executive summary

This study investigates the relationships among work-family conflict (WFC), perceived visionary leadership (VL), employee age, and motivation, drawing on Conservation of Resources (COR) theory and Self-Determination Theory (SDT). The authors find that WFC is not directly associated with employee motivation; instead, a small indirect association runs through lower perceived visionary leadership, which goes with lower motivation. Data were collected via structured online surveys from 232 employees across approximately 35 organisations, predominantly in Türkiye, and analysed using a hybrid approach combining PLS-SEM with six machine learning algorithms and explainable AI techniques including SHAP values, partial dependence plots, and accumulated local effects. Key findings confirm perceived VL as a significant mediator of the WFC-motivation relationship and identify age as both a direct predictor of motivation and a moderator of the WFC-motivation link, with older employees experiencing stronger motivational disruption from WFC. Machine learning explainability analyses corroborate VL's primacy as a predictor. The authors acknowledge that cross-sectional design, self-report bias, a modest and geographically concentrated sample, and weak overall model predictive power limit causal inference, positioning findings as associational and warranting longitudinal replication.

researchRelevance: 5/10Middle East

Key insights

  • 1The direct association between work-family conflict and employee motivation was not significant (β = −0.109, p = 0.074), while a small indirect association through perceived visionary leadership was (−0.057, 95% CI −0.112 to −0.002); with cross-sectional data, the authors read this as an associational pattern, not proof of mediation.
  • 2WFC's correlations differ by type of motivation rather than running one way for motivation overall: negative or non-significant with autonomous forms (intrinsic, identified), significantly positive with controlled forms (particularly introjected), a distinction with implications for understanding motivational quality in high-conflict work environments.
  • 3Employee age moderates the WFC-motivation relationship, with older employees experiencing greater motivational disruption from WFC — interpreted through Socioemotional Selectivity Theory as reflecting heightened valuation of role stability — while younger employees demonstrate greater motivational resilience.
  • 4Machine learning explainability analyses identified nonlinear predictive patterns for WFC, including an unexpected positive association with predicted motivation at high WFC levels, a pattern absent in the structural model; the authors ask readers to interpret it cautiously because few observations fall in that range.
  • 5Integrating PLS-SEM and machine learning as complementary rather than mutually validating frameworks revealed that the two approaches can produce divergent patterns, underscoring the importance of treating ML importance metrics as predictive contribution measures rather than causal estimates.

Practical takeaways

  • The finding that visionary leadership mediates the WFC-motivation link points to leadership perception as a lever in work-family conflict contexts, suggesting that how leadership is experienced by employees may matter more than conflict intensity alone.
  • The age-moderation finding indicates that uniform motivational strategies are unlikely to be equally effective across employee age groups, particularly in contexts where work-family demands are elevated.

Frameworks mentioned

Conservation of Resources (COR) Theory

A theoretical framework proposing that individuals strive to acquire and protect valued resources, and that resource depletion — such as that caused by work-family conflict — leads to negative psychological and behavioural outcomes.

Self-Determination Theory (SDT)

A motivational theory distinguishing between autonomous (intrinsic) and controlled (extrinsic) forms of motivation, used here to frame how WFC shifts employees away from autonomous toward controlled motivation.

Socioemotional Selectivity Theory

A lifespan developmental theory proposing that as people age they increasingly prioritise emotionally meaningful goals and stability, used to explain why older employees may be more vulnerable to WFC's motivational impact.

PLS-SEM

Partial Least Squares Structural Equation Modelling — a variance-based analytical method used to test hypothesised causal relationships among latent constructs, implemented here via SmartPLS 4.

References

  1. Matthews et al. (2010).Work-family conflict scale.
  2. Gagné et al. (2010).Motivation scale (Self-Determination Theory).
  3. Çınar and Kaban (2012).Visionary Leadership scale.
  4. Netemeyer et al. (1996).Work-family conflict measurement.
  5. Michel et al. (2011).Meta-analysis of work-family conflict.
  6. Ryan & Deci (2000).Self-Determination Theory.
  7. Judge & Piccolo (2004).Transformational and transactional leadership meta-analysis.
  8. Wang et al. (2024).Visionary and transformational leadership.
  9. Hobfoll (1989).Conservation of Resources theory.
  10. Lundberg & Lee (2017).A unified approach to interpreting model predictions (SHAP).
  11. Greenwell (2017).Partial dependence plots.
  12. Hair et al. (2022).PLS-SEM procedures.
  13. Podsakoff et al. (2003).Common method bias.

Source & Provenance

Numbers checked
Publisher / Source

Not specified

Author

Edip Doğan, Erman Kılınç, Kadir Aktaş

Publication Date

November 10, 2026

Article Type

Research Study

Geography

Middle East

Content Type
Unknown Source Type
Original Source

Original source metadata is preserved. AI analysis is generated separately.

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