Chronological age and work engagement among Indian employees: a structural equation modeling study

Introduction

Work engagement has become a central construct in organizational and occupational health psychology because it captures a positive, fulfilling work-related state marked by Vigor, Dedication, and Absorption (Schaufeli and Bakker, 2003; Schaufeli et al., 2006). It is generally understood as a motivational and affective state that reflects high levels of energy, enthusiasm, and immersion in work, and it has been widely studied in relation to performance, wellbeing, and organizational effectiveness. Although the three dimensions are conceptually related, they are not fully interchangeable: vigor and dedication tend to show stronger associations with performance and proactive work behavior, whereas absorption can vary more across settings and outcomes (Dong and Zhong, 2022; Sonnentag, 2003). This distinction suggests that demographic variables such as age may not relate uniformly to all dimensions of engagement, making dimension-level analysis more informative than the use of a single global engagement score.

A useful framework for understanding work engagement is the Job Demands–Resources (JD–R) model (Bakker and Demerouti, 2007), which proposes that job resources stimulate motivational processes that foster engagement, whereas excessive job demands can undermine energy and wellbeing (Bakker and Demerouti, 2007). Subsequent extensions of the JD–R model have also emphasized the role of personal resources, including self-efficacy, resilience, optimism, and adaptive capacities that help employees manage work demands and sustain motivation over time. However, the JD–R model alone does not specify why chronological age should influence engagement. To address this issue, the present study also draws on lifespan-developmental perspectives that view age as associated with changes in goal priorities, emotional regulation, occupational experience, resource management, and career-related concerns (Carstensen et al., 1999; Zacher and Frese, 2009).

Socioemotional selectivity theory proposes that as individuals perceive future time as more limited, they increasingly prioritize emotionally meaningful goals, positive relationships, and present-focused experiences (Carstensen et al., 1999). In work settings, these motivational shifts may contribute to stronger identification with meaningful work, greater emotional regulation, and a more stable sense of occupational purpose. Such processes may be particularly relevant to dedication, which reflects enthusiasm, pride, inspiration, and perceived significance in one's work. At the same time, older employees may draw on accumulated experience, job knowledge, and coping strategies to preserve engagement despite changing work demands (Mori et al., 2024; Zacher and Frese, 2009).

Selection, optimization, and compensation perspectives further suggest that employees may adapt to age-related changes by selecting important goals, investing resources in valued tasks, and compensating for declines in particular capacities through experience, strategies, or social support (Baltes and Baltes, 1990). These processes may help sustain dedication and concentration at work; however, they may have less consistent implications for vigor because vigor includes high energy, persistence, and willingness to invest effort. Vigor may be influenced not only by accumulated experience and motivation but also by health, physical stamina, workload, recovery opportunities, and job demands. Absorption may similarly depend on task characteristics, autonomy, cognitive demands, and opportunities to become immersed in work. Therefore, chronological age may not have identical associations with vigor, dedication, and absorption (Abraham and Hansson, 1995; Baltes and Baltes, 1990).

Empirical evidence on age and work engagement remains mixed. Some studies suggest that older workers may report higher engagement because of improved emotional regulation, experience, and more favorable responses to job resources (Mori et al., 2024). Other studies suggest that associations differ across engagement dimensions or may be weak once work conditions and career-related variables are considered (Douglas and Roberts, 2020; Zhang and Farndale, 2022). Thus, chronological age should be regarded as a broad indicator that may be associated with, but does not directly represent, the underlying mechanisms of aging at work (Zacher and Frese, 2009).

Within the Indian context, engagement has been linked to organizational performance, employee wellbeing, and lower turnover. Yet the literature has concentrated primarily on managerial, organizational, and contextual predictors rather than on demographic variables (Kumar Lal et al., 2025). Existing Indian studies have sometimes reported higher engagement among older or senior employees, but such work has often relied on broad generational categories or composite engagement scores instead of treating age as a continuous predictor and analyzing vigor, dedication, and absorption separately (Chaudhary and Rangnekar, 2017; Chawla et al., 2017). As a result, several gaps remain unresolved. First, age has rarely been examined as a central explanatory variable in Indian engagement research. Second, the use of composite engagement measures can obscure potentially different age effects across the three dimensions. Third, categorical age-group comparisons may miss incremental variation that can be captured more precisely by continuous modeling approaches. Finally, much of the Indian evidence comes from single organizations or sectors, limiting generalizability across the broader organized workforce (Venkatesan et al., 2018).

Against this background, the present study examines the direct association of chronological age with vigor, dedication, and absorption among employees in India's organized sector. The study does not test the full set of proposed lifespan mechanisms, including subjective age, future time perspective, organizational tenure, occupational tenure, career stage, health, job resources, and job demand. Accordingly, its purpose is to provide an initial, theoretically informed test of whether chronological age demonstrates a direct association with work engagement dimensions in a multisector Indian sample, rather than to claim that age alone explains engagement. By applying structural equation modeling to a multisector Indian sample, the study provides context-specific evidence on whether age meaningfully predicts engagement at the dimension level rather than only at the aggregate level.

Study objectives

The study was guided by three objectives:

• To assess the relationship between age and vigor,
• To assess the relationship between age and dedication, and
• To assess the relationship between age and absorption among employees in India's organized sector.

Hypotheses

On the basis of the JD–R model and lifespan-developmental perspectives, chronological age may be associated with work engagement through age-related differences in emotional regulation, occupational experience, goal prioritization, health, and resource management. However, these mechanisms may operate in competing directions and may not affect vigor, dedication, and absorption uniformly. Given mixed prior evidence and the absence of direct measures of these mechanisms, the hypotheses are framed as exploratory, non-directional direct-association hypotheses.

• H1: Chronological age is significantly associated with vigor among employees in India's organized sector.
• H2: Chronological age is significantly associated with dedication among employees in India's organized sector.
• H3: Chronological age is significantly associated with absorption among employees in India's organized sector.

Materials and methods

Research design

The study used a cross-sectional quantitative, associational design to examine the relationship between chronological age and the three work-engagement dimensions of Vigor, dedication, and absorption among employees in India's organized sector. Structural equation modeling (SEM) was used to estimate the specified direct paths from age to each engagement dimension while accounting for covariance among the engagement dimensions. The analysis was designed to assess whether chronological age demonstrated direct associations with the engagement dimensions in the present dataset; it was not intended to establish causal or developmental effects of age on work engagement (Spector, 2019).

Chronological age was included as the sole exogenous variable in the primary model because the study aimed to examine age as a focal demographic correlate rather than merely include it as a routine control variable. This intentionally parsimonious specification estimates unadjusted direct associations and does not isolate the association of age independently of other potentially relevant variables, including organizational tenure, occupational tenure, job type, educational level, organizational level, career stage, health, job demands, job resources, and supervisory support.

Sample and participants

Data were collected from employees working in India's organized sector using a cross-sectional survey approach. A non-probability convenience-sampling strategy was used to recruit individuals employed in public-sector organizations, private companies, and self-employed roles. Potential participants were approached through professional and organizational networks and were invited to complete an anonymous, self-administered questionnaire either in paper format or through an online survey link (Etikan, 2016). Eligibility criteria required respondents to be at least 18 years old and currently employed in the organized sector.

After screening for incomplete or invalid responses, 2,133 usable questionnaires were retained for analysis. Participants ranged in age from 20 to 64 years (M = 41.19, SD = 12.38), providing coverage of early, middle, and later working life. Although the sample was large and included employees from multiple employment sectors, the convenience-sampling strategy did not ensure proportional representation across sectors, occupations, organizations, job levels, educational backgrounds, or geographical regions. The sample should therefore be understood as a large multisector convenience sample rather than as nationally representative of all employees in India's organized sector.

Procedure and ethics

Data collection was conducted over a defined period using a structured self-administered questionnaire distributed both in paper form and through an online survey link circulated via organizational and professional contacts. Managers and officials in participating organizations were requested to share the questionnaire or survey link with eligible employees, who completed it voluntarily. Paper questionnaires were returned in sealed envelopes, whereas online responses were submitted directly through online surveys.

The questionnaire was made available in parallel Hindi and English versions, allowing respondents to select the language in which they were most comfortable, in line with established recommendations for multilingual survey research (Brislin, 1980). Before participating, all respondents were informed of the academic purpose of the study, the voluntary nature of participation, and the assurance of anonymity and confidentiality. Completion and return of the paper questionnaire, or submission of the online form, were treated as indications of informed consent. At the time of data collection, the authors' institution did not require formal ethics committee review for anonymous, non-interventional survey research involving adult participants. Nevertheless, the study procedures were designed and implemented in accordance with accepted ethical principles for research involving human participants, including respect for autonomy, confidentiality, and minimal risk. Ethical review and approval were waived for this study due to its anonymous, non-interventional survey design involving adult participants only.

The same questionnaire content and participation instructions were used for paper-based and online administration. Nevertheless, respondents may have differed across these modes in terms of accessibility, work context, digital access, or response conditions. Because measurement equivalence across paper and online administration modes was not formally tested, potential mode-related differences cannot be ruled out and are considered when interpreting the findings (Vandenberg and Lance, 2000).

Measures

Age

Age was measured as a continuous variable and recorded in full years at the time of data collection. Respondents self-reported their current age, which was specified as the sole exogenous variable in the primary structural model. Chronological age was treated as a broad demographic indicator and not as a direct measure of subjective age, career stage, occupational tenure, health, future time perspective, or other proposed age-related mechanisms.

Work engagement

Work engagement was assessed using the Utrecht Work Engagement Scale (UWES), a 17-item instrument developed by Schaufeli and Bakker (2003) to assess the three dimensions of vigor, dedication, and absorption. The original scale comprises six items measuring vigor, five items measuring dedication, and six items measuring absorption. Across occupational settings, the UWES has generally demonstrated satisfactory reliability and construct validity, and it has been widely used in engagement research.

For the present study, the overall internal consistency of the UWES was α = 0.78. Internal consistency was also evaluated for each subscale using Cronbach's alpha. These values were considered alongside prior psychometric work, including evidence from Indian samples (Lathabhavan et al., 2017). An initial confirmatory factor analysis (CFA) conducted on the full item pool indicated that two absorption items— “I get carried away when I'm working” and “It is difficult to detach myself from my job”—showed unstable or near-zero loadings and adversely affected internal consistency. This pattern is broadly consistent with concerns reported in some prior adaptations of the absorption dimension (Lathabhavan et al., 2017). Accordingly, these two items were removed, resulting in a refined 15-item, three-factor solution.

A CFA was then conducted on the revised work engagement measure specifying three correlated latent factors: vigor, dedication, and absorption. All retained items loaded significantly and in the expected direction on their intended factors. Standardized loadings ranged from 0.58 to 0.72 for vigor, 0.57 to 0.91 for dedication, and 0.37 to 0.75 for absorption, indicating acceptable convergent validity for the retained indicators. Global fit indices suggested excellent model fit: χ²(87) = 127.00, p = 0.004, CFI = 0.990, TLI = 0.988, and RMSEA = 0.015, 90% CI (0.009, 0.020). In interpreting these indices, particular attention was paid to approximate fit measures, as recommended in SEM research, where CFI values near or above 0.95 and RMSEA values below 0.06 are typically taken as evidence of good fit (Hu and Bentler, 1999).

At the latent level, however, the three factors were extremely highly correlated, suggesting substantial overlap among vigor, dedication, and absorption in the present sample. Because the latent variances were fixed to 1.00, the estimated covariances corresponded to correlations approaching unity, indicating limited empirical separation among the three dimensions. Internal consistency analyses complemented these findings: the vigor subscale showed marginal reliability (α ≈ 0.62), whereas dedication and absorption showed relatively low reliabilities (α ≈ 0.44 and α ≈ 0.46, respectively). Although these coefficients fall below conventional benchmarks often cited in psychometric literature (Nunnally and Bernstein, 1994), all retained items demonstrated significant and theoretically meaningful factor loadings in the CFA. The three subscales were therefore retained as separate observed subscale scores for descriptive and structural analyses, but findings involving dedication and absorption were interpreted with appropriate caution given their lower reliability and high overlap with the broader engagement construct.

Although the revised three-factor measurement model demonstrated excellent global fit, global fit indices alone do not establish adequate discriminant validity. In the present sample, the reliability estimates for dedication and absorption were below conventional benchmarks, and the latent correlations among vigor, dedication, and absorption were extremely high. These findings suggest substantial empirical overlap among the three dimensions and indicate that they may not be fully distinguishable as separate constructs in this dataset. Accordingly, the three-factor structure was retained for consistency with the theoretical UWES framework, but dimension-specific findings were interpreted cautiously.

Future research should compare alternative representations of work engagement, including one-factor, correlated three-factor, higher-order, and bifactor models. Such comparisons would help establish whether a global engagement factor provides a more parsimonious representation of work engagement in Indian employee samples.

Statistical analysis

Data were analyzed using JASP (Version 0.19.3.0) and Jamovi (Version 2.7.27.2), both of which are open-source platforms suitable for psychometric analysis and structural equation modeling. Preliminary analyses included screening for missing values, outliers, and basic distributional properties. Descriptive statistics—including mean, standard deviation, minimum, maximum, skewness, and kurtosis—were computed for age and each engagement dimension. Skewness and kurtosis values were examined to assess whether the data showed serious deviations from normality prior to conducting parametric analyses.

Following confirmation of the measurement model, bivariate Pearson correlations were computed between age and the three engagement dimensions to provide an initial overview of the associations among the variables. The main hypotheses were tested using a structural equation model in which chronological age was specified as an observed exogenous variable associated with vigor, dedication, and absorption, while the engagement dimensions were allowed to covary. Parameters were estimated using maximum likelihood. Standardized path coefficients, standard errors, z-values, p-values, and coefficients of determination (R²) were reported for each structural path. Statistical significance was evaluated at the 0.05 level using two-tailed tests.

The structural coefficients represent direct associations within the specified cross-sectional model and should not be interpreted as causal effects. Because the primary model included age as the sole exogenous variable, the estimated paths represent unadjusted associations and do not account for potential confounding by occupational, organizational, demographic, health-related, or work-context variables.

Results

Table 1 presents the descriptive statistics for age, vigor, dedication, and absorption. Participants had a mean age of 41.2 years (SD = 12.4, range = 20–64); indicating that the sample covered early, mid, and later career stages. The mean scores for vigor (M = 19.5, SD = 5.17), dedication (M = 16.4, SD = 4.91), and absorption (M = 13.1, SD = 3.77) suggested moderate to relatively high levels of work engagement across the sample. Skewness and kurtosis values for all variables were within acceptable limits, indicating no serious departures from normality.

Table 1. Descriptives.

Table 2 presents the zero-order correlations among chronological age and the three observed work-engagement dimensions. Age showed small negative correlations with vigor (r = −0.083, p < 0.001), dedication (r = −0.092, p < 0.001), and absorption (r = −0.070, p < 0.001). Thus, at the bivariate level, older employees reported marginally lower scores on each engagement indicator. However, these effect sizes were small, despite reaching statistical significance in the large sample. The observed engagement dimensions were moderately and positively intercorrelated (r = 0.520 to 0.593, all p < 0.001), indicating that they shared substantial variance.

Table 2. Correlations matrix.

^*p < 0.05, ^**p < 0.01, ^***p < 0.001.

Confirmatory factor analysis

A confirmatory factor analysis was conducted to evaluate the measurement model for work engagement using the refined 15-item structure. As shown in Table 3, all retained items loaded significantly on their intended latent factors (p < 0.001). Standardized loadings for vigor ranged from 0.577 to 0.721, those for dedication ranged from 0.572 to 0.913, and those for absorption ranged from 0.369 to 0.754. Overall, the factor loadings indicate that the retained indicators provided acceptable support for the three-factor structure, although one absorption item (“When I am working, I forget everything else around me”) emerged as a comparatively weaker indicator.

Table 3. Factor loadings.

Table 4 reports the latent correlations among vigor, dedication, and absorption were extremely high, ranging from 0.97 to 1.01. Although the three dimensions are conceptually distinct in the UWES framework, these estimates indicate limited discriminant validity in the present sample. In particular, a correlation exceeding 1.00 may indicate an improper solution, such as a Heywood case, or may reflect severe multicollinearity and insufficient empirical separation between factors. Therefore, the results do not support strong claims that vigor, dedication, and absorption functioned as fully independent latent constructs in this dataset. The excellent global fit of the three-factor model should consequently be interpreted alongside these validity concerns.

Table 4. Factor covariances.

Latent factor variance fixed at 1.00 for model identification.

Table 5 summarizes the global fit indices for the three-factor work engagement model. The chi-square test of exact fit was significant, χ² (87) = 127.00, p = 0.004, reflecting some deviation from exact fit.

Table 5. Test for exact fit.

Table 6 shows approximate fit indices indicating excellent model fit, with CFI = 0.99 and TLI = 0.99, and a very low RMSEA of 0.015 [90% CI (0.009, 0.020)], consistent with a close fit of the model to the data.

Table 6. Fit measures.

The revised 15-item three-factor measurement model demonstrated excellent global fit to the data, p = 0.004, CFI = 0.990, TLI = 0.988, and RMSEA = 0.015, 90% CI (0.009, 0.020). However, global fit indices should be interpreted alongside evidence of reliability and discriminant validity. Although all retained indicators loaded significantly on their intended factors, the internal-consistency estimates were modest for vigor (α = 0.62) and low for dedication (α = 0.44) and absorption (α = 0.46). In addition, the latent correlations among vigor, dedication, and absorption were extremely high (r = 0.97 to 1.01), suggesting substantial overlap among the three dimensions. Therefore, while the specified three-factor model showed good overall fit, the empirical distinction among the dimensions was limited in the present sample. The dimension-specific structural results should consequently be interpreted with caution.

Structural equation modeling

The structural equation model was then estimated to test whether age significantly predicted vigor, dedication, and absorption. The overall model fit was satisfactory, as indicated by the statistics reported in Table 7: χ² (99) = 136.97, p = 0.007, with approximate fit indices again suggesting good model fit. Thus, the structural model provided an acceptable basis for hypothesis testing.

Table 7. Model fit.

Although the zero-order correlations between age and the three engagement indicators were statistically significant, the structural paths from age to vigor, dedication, and absorption were not statistically significant in the SEM. These findings are not necessarily inconsistent because the two analyses estimate different relationships. The bivariate correlations describe the simple association between age and each observed engagement score considered separately. In contrast, the SEM estimated direct associations while simultaneously modeling the strong covariance among the latent engagement dimensions. Given the extremely high latent correlations among vigor, dedication, and absorption, the dimensions shared substantial common variance, leaving limited unique variance within each dimension for chronological age to explain. Therefore, the non-significant structural paths indicate that age did not account for meaningful unique direct variance in the separate, highly overlapping engagement dimensions within the specified model.

The significant zero-order correlations should also be interpreted in relation to their magnitude. With a sample of 2,133 employees, small associations can attain statistical significance even when their practical importance is limited. Accordingly, the SEM findings do not establish that chronological age is unrelated to work engagement; rather, they indicate that the small bivariate associations did not emerge as robust dimension-specific direct effects in the specified latent-variable mode.

Table 8 reports the standardized factor loadings, standard errors, z values, and confidence intervals for the indicators of Vigor, Dedication, and Absorption. All items loaded significantly on their intended latent factor (p < 0.001), with Vigor items showing moderate loadings, Dedication items generally displaying stronger loadings, and Absorption items showing moderate loadings with one comparatively weaker but still significant indicator. These results indicate that the three engagement dimensions are measured adequately by their respective item sets.

Table 8. Factor loadings.

Figure 1 presents the path diagram for the structural model. The diagram shows positive factor loadings from the observed indicators to the three latent engagement dimensions and very strong correlations among vigor, dedication, and absorption. At the same time, the paths from age to the three latent factors are close to zero, visually reinforcing the statistical finding that chronological age did not meaningfully explain variation in engagement in this sample.

The non-significant structural paths should be interpreted in the context of the very high overlap among the three engagement dimensions. When vigor, dedication, and absorption were simultaneously represented as highly correlated latent outcomes, little unique variance remained within each dimension for chronological age to explain. Thus, the non-significant paths indicate that age did not account for unique direct variance in the separate engagement dimensions within the specified model; they do not demonstrate that age is unrelated to work engagement in all forms or contexts.

The zero-order correlations showed small but statistically significant negative associations between age and vigor, dedication, and absorption. Given the large sample size, these small associations reached statistical significance, but their practical magnitude was limited. The difference between the bivariate and SEM findings suggests that the simple age–engagement associations were not robust dimension-specific direct effects after the substantial common variance among engagement dimensions was modeled.

The substantive findings indicated that age did not significantly predict any of the three engagement dimensions in the structural model. Although the bivariate correlations suggested small negative associations between age and engagement, the standardized structural paths from age to Vigor, dedication, and absorption were negligible and statistically non-significant. Because the estimated direct paths from chronological age to vigor, dedication, and absorption were not statistically significant, H1, H2, and H3 were not supported in the specified structural model.

The results suggest that work engagement may function primarily as a broad, highly integrated construct in this sample; therefore, future research should compare correlated three-factor, one-factor, higher-order, and bifactor representations of the UWES before making strong claims about dimension-specific antecedents.

Discussion

Main findings

The present study examined whether chronological age was directly associated with Vigor, dedication, and absorption among employees in India's organized sector. At the bivariate level, chronological age showed small negative associations with all three observed engagement dimensions. However, chronological age did not demonstrate statistically significant unique direct associations with Vigor, dedication, or absorption in the specified structural equation model.

This pattern should not be interpreted as evidence that age is irrelevant to work engagement. Rather, it indicates that the small zero-order associations did not remain robust as separate direct effects after the substantial shared variance among the highly overlapping engagement dimensions was modeled. The findings are therefore specific to the present cross-sectional model, the sample, and the measurement properties observed in this dataset.

The very high latent correlations among Vigor, dedication, and absorption are particularly important for interpreting the findings. Although these dimensions are theoretically distinguished in the UWES framework, the present measurement model indicated limited empirical separation among them. When outcomes overlap extensively, an exogenous variable may show small simple associations with each observed outcome but may not explain unique variance in each latent dimension after their common variance is modeled simultaneously. Accordingly, the present SEM findings are best interpreted as showing that chronological age was not a strong dimension-specific direct predictor of work engagement in this sample.

Theoretical implications

The findings support the view that chronological age is a broad demographic indicator rather than a direct psychological mechanism. Lifespan-developmental theories suggest that age-related differences in work-related attitudes and motivation may occur through more proximal processes, including changes in future time perspective, emotional regulation, goal priorities, health, work experience, and career-related resources (Baltes and Baltes, 1990; Carstensen et al., 1999; Zacher and Frese, 2009).

Socioemotional selectivity theory proposes that as perceived future time becomes more limited, individuals may prioritize emotionally meaningful goals and experiences (Carstensen et al., 1999). In occupational settings, this process may be associated with stronger identification with meaningful work, greater emotional regulation, and increased emphasis on intrinsically valuable work experiences. Such mechanisms may be particularly relevant to dedication, which reflects feelings of significance, pride, enthusiasm, and inspiration at work.

Selection, optimization, and compensation theory similarly proposes that individuals adapt to changing resources across the lifespan by selecting valued goals, optimizing available resources, and compensating for limitations through experience, strategies, or support (Baltes and Baltes, 1990). These strategies may help employees sustain involvement in meaningful work and preserve performance across the working lifespan. However, their implications may differ across engagement components. For example, occupational experience and emotional regulation may support dedication, whereas Vigor may depend more strongly on health, physical energy, job demands, recovery opportunities, and resource availability.

The present findings therefore do not rule out age-related engagement processes. Instead, they suggest that chronological age alone may be too distal and broad to explain dimension-specific engagement once the substantial common variance among vigor, dedication, and absorption is considered. Age may influence engagement indirectly through subjective age, occupational future time perspective, organizational tenure, occupational tenure, career stage, health, job resources, job demands, meaningful work, and age-inclusive organizational practices. Age may also moderate the relationship between job characteristics and engagement rather than operating only as a direct predictor.

Measurement implications

The measurement findings require cautious interpretation of the structural results. Although the revised three-factor CFA demonstrated excellent global fit, the low internal-consistency estimates for dedication and absorption and the extremely high latent correlations among vigor, dedication, and absorption raised concerns regarding reliability and discriminant validity. Global model fit does not by itself establish that latent constructs are reliably measured or empirically distinct (Hair et al., 2019; Kline, 2016).

The findings suggest that the three engagement dimensions may have functioned primarily as highly overlapping indicators of a broader engagement construct in the present sample. Therefore, the separate direct paths from age to Vigor, dedication, and absorption should not be interpreted as evidence of independent age effects on three clearly differentiated outcomes. Instead, the results reflect the relationship between age and three strongly overlapping representations of work engagement.

Future research should formally compare alternative measurement models, including one-factor, correlated three-factor, second-order, and bifactor representations of work engagement. Such analyses would help determine whether a general engagement factor provides a more parsimonious and psychometrically appropriate representation of the UWES in Indian employee samples (Schaie, 1965; Spector, 2019). Future studies should also report composite reliability and McDonald's omega in addition to Cronbach's alpha and should examine whether weak items, particularly within absorption, operate consistently across occupational groups and language versions.

Limitations and future research

Several limitations should be considered when interpreting the findings. First, the study used a non-probability convenience-sampling approach. Although this approach allowed the recruitment of a large and occupationally diverse sample, it limits the representativeness of the findings. Participants were recruited through professional and organizational networks; therefore, employees who were more accessible, connected to these networks, or willing to participate may have been overrepresented (Etikan, 2016). The findings should consequently be interpreted as evidence from a large multisector convenience sample rather than as nationally representative evidence for all employees in India's organized sector.

Second, the cross-sectional design limits causal and developmental inference. Cross-sectional age differences may reflect cohort effects, historical conditions, career experiences, or selection processes rather than within-person developmental change (Schaie, 1965; Spector, 2019). Longitudinal, cohort-sequential, and repeated-measures research is required to distinguish age-related change from cohort and period influences (Schaie, 1965).

Third, data were collected using both online and paper-based questionnaires. Although the same questionnaire and voluntary-participation procedures were used across administration modes, the study did not formally test measurement equivalence between online and paper respondents. Establishing measurement invariance is important before comparing results across groups or administration conditions (Vandenberg and Lance, 2000). Differences in access, participation, response styles, or survey-completion conditions may have affected the findings. Future research should test measurement invariance across online and paper administration modes and report mode-specific descriptive and measurement-model results where feasible.

Fourth, the cross-sectional design precludes causal and developmental inference. Although chronological age is temporally prior to work engagement in a broad sense, the study cannot establish whether aging processes directly influence engagement over time. Observed age-related differences may reflect cohort membership, organizational tenure, occupational tenure, job level, career stage, health, work experience, or selection into particular occupational roles rather than age-related developmental processes alone. Longitudinal, cohort-sequential, and repeated-measures designs are needed to examine how engagement changes within individuals over time.

Fifth, chronological age and work engagement were assessed in the same self-administered questionnaire. Although age is relatively objective compared with attitudinal predictor variables and anonymity was maintained to reduce evaluation apprehension, common survey-context effects, social desirability, transient mood, and response-style bias cannot be excluded (Podsakoff et al., 2003). Future studies should use multi-wave and multi-source designs, including objective employment records, supervisor-rated job characteristics, and longitudinal employee self-reports.

Finally, the model intentionally treated chronological age as the sole exogenous predictor to examine its direct association with work engagement. However, this parsimonious model does not estimate the unique role of age after accounting for potentially relevant factors such as organizational tenure, occupational tenure, job level, career stage, health, job demands, job resources, and supervisory support. Work engagement is generally understood within the JD–R framework as being shaped by the interaction of job demands and job resources, alongside personal resources (Bakker and Demerouti, 2007; Xanthopoulou et al., 2009). Future studies should estimate adjusted models and test mediating and moderating mechanisms that may clarify when, how, and for whom chronological age relates to engagement.

Conclusion

This study examined the direct association between chronological age and Vigor, dedication, and absorption among employees in India's organized sector. Chronological age showed small negative bivariate associations with the three observed engagement dimensions but did not demonstrate statistically significant unique direct associations with the highly overlapping latent dimensions in the specified structural model.

The findings should not be interpreted as evidence that chronological age is irrelevant to work engagement. Rather, they indicate that chronological age alone was not a strong dimension-specific direct predictor in this cross-sectional convenience sample. The role of age may be indirect, non-linear, context-dependent, or better explained by more proximal constructs, including subjective age, future time perspective, tenure, career stage, health, job resources, job demands, and organizational context.

Given the low reliability of two subscales, limited discriminant validity among engagement dimensions, convenience sampling, mixed survey modes, cross-sectional data, self-report measurement, and the absence of occupational covariates, the findings should be regarded as preliminary and context-specific. Future longitudinal, multi-source, and representative research should evaluate global work engagement alongside its specific dimensions and test the mechanisms through which age-related processes may influence employee engagement.

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Footnotes

Edited by: Christian Vandenberghe, Université de Montréal, Canada

Reviewed by: José Félix Brito Ortíz, Universidad Autónoma de Baja California, Mexico

Dawid Szostek, Nicolaus Copernicus University in Toruń, Poland