Age May Not Decide How Engaged Indian Employees Feel at Work, Study Finds

Does getting older drain the energy, enthusiasm, and focus that people bring to their jobs? A large new study from India suggests the answer is far more complicated than a simple yes or no. Researchers at Amity University Madhya Pradesh surveyed 2,133 employees across the country’s organized sector, spanning public-sector organizations, private companies, and self-employment, and found that chronological age, on its own, was not a meaningful direct predictor of work engagement. The findings, published in Frontiers in Organizational Psychology, challenge a persistent assumption in workplace research and management practice: that the number of years an employee has lived tells us something reliable about how engaged they will be at work.

Work engagement is one of the most intensively studied constructs in organizational psychology, typically defined as a positive, fulfilling work-related state comprising three dimensions. Vigor captures high levels of energy and mental resilience while working, along with the persistence to keep going even when difficulties arise. Dedication reflects a sense of significance, enthusiasm, pride, and inspiration in one’s work. Absorption describes being fully concentrated and happily engrossed in one’s tasks, so much so that time seems to fly. The study used the Utrecht Work Engagement Scale, a 17-item instrument widely regarded as the standard measure in the field, to assess all three dimensions among participants ranging from 20 to 64 years old, with an average age of about 41 years.

The theoretical backdrop matters here. The researchers drew on two influential frameworks. The first is the Job Demands–Resources model, which holds that job resources such as autonomy, feedback, and social support fuel motivational processes that build engagement, while excessive job demands erode energy and wellbeing. The second is a set of lifespan-developmental perspectives, including socioemotional selectivity theory, which proposes that as people perceive their future time as more limited, they increasingly prioritize emotionally meaningful goals and relationships. In the workplace, this could translate into older employees feeling deeper identification with meaningful work and greater emotional regulation, particularly on the dedication dimension. Selection, optimization, and compensation theory adds another layer, suggesting that workers adapt to age-related changes by choosing valued goals and compensating for declining capacities with experience and strategy.

Yet these mechanisms point in competing directions, which is precisely what makes the question empirically interesting. Experience and emotional regulation might boost dedication in older workers, but vigor, which depends partly on physical energy, health, workload, and recovery, could plausibly decline with age. Absorption might hinge more on task characteristics, autonomy, and cognitive demands than on age itself. Prior evidence has been genuinely mixed: some studies report higher engagement among older workers, while others find weak or dimension-specific associations once job conditions and career variables are taken into account. In India specifically, most engagement research has focused on organizational and managerial predictors, often treating age as a routine control variable or lumping employees into broad generational categories rather than modeling age as a continuous variable.

The study’s methodology was deliberately rigorous on the statistical side. Participants completed questionnaires in parallel Hindi and English versions, and after screening, 2,133 usable responses were retained. The researchers first refined the measurement instrument: confirmatory factor analysis revealed that two absorption items, including statements about getting carried away while working and finding it difficult to detach from the job, showed unstable or near-zero loadings, so they were removed, yielding a 15-item three-factor solution. This refined model showed excellent global fit to the data, with a comparative fit index of 0.990, a Tucker-Lewis index of 0.988, and a root mean square error of approximation of just 0.015, values well within conventional benchmarks for good structural equation modeling performance.

The results unfolded in two stages that tell a subtle statistical story. At the bivariate level, age showed small but statistically significant negative correlations with all three engagement dimensions: roughly negative 0.08 for vigor, negative 0.09 for dedication, and negative 0.07 for absorption. In a sample of over two thousand people, even effects this small reach conventional significance thresholds. Older employees, in other words, reported marginally lower scores on each engagement measure. But when the researchers moved to the structural equation model, which estimated direct paths from age to each latent engagement dimension while simultaneously accounting for the covariance among them, all three paths from age turned out to be statistically non-significant. All three exploratory hypotheses were therefore not supported.

Why the discrepancy? The answer lies in how the three engagement dimensions behaved in this dataset. The latent correlations among vigor, dedication, and absorption were extraordinarily high, ranging from 0.97 to 1.01, values that suggest the three factors shared nearly all of their variance and were barely distinguishable as separate constructs. When outcomes overlap that heavily, an exogenous variable like age may show small simple correlations with each observed score but have essentially no unique variance left to explain once the shared variance among the dimensions is modeled. The non-significant structural paths do not mean age is unrelated to engagement; they mean that chronological age, by itself, could not account for meaningful dimension-specific variance in this sample.

The measurement findings add an important layer of caution that the authors are explicit about. Internal consistency was marginal for vigor, with a Cronbach’s alpha of about 0.62, and low for dedication and absorption, at roughly 0.44 and 0.46 respectively, well below conventional psychometric benchmarks. A correlation estimate exceeding 1.00 among latent factors can also signal an improper solution or severe multicollinearity. The researchers therefore advise that dimension-specific results be interpreted cautiously and recommend that future work formally compare alternative representations of engagement, including one-factor, higher-order, and bifactor models, to determine whether a single general engagement factor better captures the construct in Indian samples. Excellent global fit statistics, they note, do not by themselves establish that latent constructs are reliably measured or empirically distinct.

Several design limitations frame what these findings can and cannot claim. The study used a convenience-sampling strategy, recruiting through professional and organizational networks, so the sample, while large and multisector, is not nationally representative of India’s organized workforce. The cross-sectional design cannot distinguish age-related developmental change from cohort effects, career experiences, or selection processes, and data were collected through both paper and online questionnaires without a formal test of measurement equivalence between modes. The model also intentionally treated age as the sole predictor, meaning the estimated paths are unadjusted associations that do not isolate age from factors such as tenure, career stage, health, job demands, job resources, or supervisory support. Engagement was measured by self-report at a single time point, leaving room for common survey-context effects.

What the study ultimately delivers is a corrective rather than a conclusion. Chronological age, the authors argue, is best understood as a broad demographic indicator rather than a direct psychological mechanism. The processes that might link aging to engagement, including shifts in future time perspective, emotional regulation, goal prioritization, health, and accumulated experience, are likely to operate indirectly, non-linearly, or in interaction with job characteristics, and may matter more through constructs such as subjective age, how old people feel, than through birth certificates. For employers in India and beyond, the message is straightforward: assuming that a younger workforce is inherently more energetic or an older one more committed, or the reverse, is not supported by this evidence. Understanding engagement, in this sample at least, requires looking past age to the conditions of the work itself, and future longitudinal, multi-source research will be needed to pin down when and how age-related processes actually shape the way people experience their jobs.

Subject of Research: The association between chronological age and the three dimensions of work engagement among employees in India's organized sector, examined using structural equation modeling.

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

Article References: Singh, H., & Chahal, J. (2026). Chronological age and work engagement among Indian employees: a structural equation modeling study. Frontiers in Organizational Psychology, 4, Article 1907256. https://doi.org/10.3389/forgp.2026.1907256

Image Credits: AI Generated

DOI: 10.3389/forgp.2026.1907256

Keywords: work engagement, chronological age, Indian employees, structural equation modeling, Job Demands-Resources model, vigor, dedication, absorption, Utrecht Work Engagement Scale, organizational psychology, lifespan development, psychometrics