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Editorial verdict
Methodologically transparent but inconclusive — the null SEM findings are largely an artifact of near-unity latent correlations among engagement dimensions (r = 0.97–1.01), making dimension-specific age effects statistically untestable; treat the bivariate correlations as the study's primary substantive contribution, and the measurement model as a cautionary flag for UWES use in Indian samples.
Executive summary
This study investigates the direct association between chronological age and three dimensions of work engagement — vigor, dedication, and absorption — among employees in India's organized sector. Drawing on the Job Demands–Resources model and lifespan-developmental perspectives, including Socioemotional Selectivity Theory and Selection, Optimization, and Compensation theory, the authors hypothesized non-directional associations between age and each engagement dimension. A cross-sectional survey of 2,133 employees (ages 20–64, M = 41.19) across public, private, and self-employed sectors was analyzed using structural equation modeling with the Utrecht Work Engagement Scale (15-item revised version). At the bivariate level, age showed small but statistically significant negative correlations with vigor (r = −0.083), dedication (r = −0.092), and absorption (r = −0.070). However, none of the structural paths from age to the three latent engagement dimensions reached statistical significance. This non-significance is attributed primarily to extremely high latent inter-factor correlations (r = 0.97–1.01) that left minimal unique variance for age to explain within each dimension. The authors conclude that chronological age alone is not a strong dimension-specific direct predictor of work engagement in this Indian multisector convenience sample, and that age-related effects may operate indirectly through subjective age, tenure, career stage, health, and job resources.
Key insights
- 1Chronological age showed small but statistically significant negative bivariate correlations with vigor, dedication, and absorption (r = −0.070 to −0.092), but these associations did not survive as unique direct paths in the structural equation model.
- 2Latent correlations among the three UWES dimensions approached or exceeded unity (r = 0.97–1.01), indicating severe empirical overlap and raising serious concerns about discriminant validity of vigor, dedication, and absorption as separable constructs in this Indian sample.
- 3Internal consistency estimates for dedication (α ≈ 0.44) and absorption (α ≈ 0.46) fell well below conventional psychometric benchmarks, suggesting the UWES subscales may not function reliably as distinct measures in this population and context.
Practical takeaways
- Organizations using the UWES in Indian employee populations may be measuring a single broad engagement construct rather than three distinct dimensions — a consideration relevant to how engagement survey results are interpreted and acted upon.
- Age-based workforce segmentation in engagement programs may lack empirical support as a direct predictor; factors such as job resources, career stage, tenure, and health may be more proximal drivers worth investigating in Indian organizational contexts.
Frameworks mentioned
Job Demands-Resources (JD-R) Model
A theoretical framework proposing that job resources foster engagement through motivational processes, while excessive job demands deplete energy and undermine wellbeing.
Socioemotional Selectivity Theory
A lifespan-developmental theory proposing that as individuals perceive future time as more limited, they increasingly prioritize emotionally meaningful goals and present-focused experiences.
Selection, Optimization, and Compensation (SOC)
A lifespan theory proposing that individuals adapt to age-related changes by selecting valued goals, optimizing available resources, and compensating for limitations through experience or social support.
References
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Source & Provenance
frontiers-orgpsych
Harshdeep Singh, Jangjeet Chahal
September 22, 2026
Research Study
Asia-Pacific
Original source metadata is preserved. AI analysis is generated separately.
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