The multifaceted influence of age on employee work engagement: Examining the interactive effects of chronological age,relational age,and perceived age-related treatment

Abstract

Building upon and extending prior research, this study examines the interplay between chronological age, relational age, and perceived age-related treatment in predicting work engagement. While previous studies have often examined these three facets of age in isolation from one another, this article develops an integrative framework that combines life span theories with relational demography and age-related treatment studies. Findings from a sample of 434 school teachers from 16 schools in Italy supported the hypothesis that the three-way interaction between relational age, chronological age and age-related treatment generates asymmetrical effects on work engagement. Specifically, at high levels of perceived positive age-related treatment, relational age was positively associated with older workers’ engagement, while greater relational age was associated with reduced work engagement when older workers perceived that they were treated unfairly based on their age. In contrast, among younger workers, work engagement was positively related to perceived positive age-related treatment whatever the level of relational age.

Keywords

Introduction

In most countries, due to rising life expectancy and lower fertility rates, the workforce is not only aging, with more people working until later in life, but also becoming more age-diverse, with older and younger people working side by side (Profili et al., 2017). Moreover, as the age differences between co-workers grow and the proportion of older employees continues to expand, employees seem increasingly concerned with how they are treated at work based on their age, with potentially important implications for their attitudes and behaviors at the workplace (Eurobarometer, 2019).

In response, a growing number of studies have begun to look at how age-related factors influence a range of outcomes at work. A variety of both objective and subjective age-related factors have been examined in this context. For ease of presentation, we refer to these various age-related factors as different manifestations of age at the workplace. On the objective side, in addition to chronological age (e.g. Truxillo et al., 2017) these include, for example, individuals’ age relative to others at the workplace (i.e. relational age; Sammarra et al., 2021), and the way the organization treats employees of different ages, as captured by their shared perceptions of the fair and non-discriminatory treatment received by individuals of different age groups at work (i.e. age diversity climate; Boehm et al., 2014). On the subjective side, the parallel set of age-related factors includes individuals’ experienced sense of their own age (i.e. subjective age; Rudolph et al., 2019), the extent to which they prefer to work and interact with others of a similar age (i.e. age similarity preference; Standifer et al., 2013), and their perceptions of the extent to which the organization values and treats people of their own age group fairly and respectfully (i.e. perceived positive age-related treatment; Sammarra et al., 2021). However, despite the growing attention that these different age-related factors have received in the literature, few if any studies to date have attempted to examine their simultaneous effect on employee outcomes. Instead, most studies have tended to focus on a particular aspect of age, while ignoring the possible effect that other aspects of age may have on the outcome of interest.

In the present study, we examine the effect of multiple manifestations of age on employee work engagement, defined as a positive, fulfilling work-related state of mind that is characterized by vigor, dedication, and absorption (Schaufeli et al., 2002: 74). We focus on work engagement for two main reasons. First, because employee work engagement is a key outcome of both theoretical and practical interest at the workplace (Saks, 2019). And second, because different manifestations of age have received increased attention in the work engagement literature. At the same time, however, extant studies in this area have, for the most part, examined the effects of different aspects of age on engagement separately rather than simultaneously. In fact, research in this area has taken three main forms. A first stream of studies has focused on chronological age to examine how aging affects workers’ engagement (Goštautaitė and Bučiūnienė, 2015; James et al., 2011; Kim and Kang, 2017). A second stream of studies in the tradition of relational demography has focused on relational age to explain how age dissimilarity relates to engagement (Avery et al., 2007; Yang and Matz-Costa, 2018). A third stream of studies has focused on the social and psychological meanings associated with age to examine how actual and perceived age-related treatment influences work engagement (Bayl-Smith and Griffin, 2014; Boone James et al., 2013; Yeung et al., 2021).

While these single-factor lines of investigation have offered valuable insights, they are also problematic to some extent in that they may provide only a partial view of intertwined phenomena. In particular, they fail to consider two important issues. First, the relative importance of chronological age, relational age, and age-related treatment as predictors of employee work engagement. And second, how these three manifestations of age potentially combine and interact with each other to influence employee engagement. The latter issue is especially important given, as we discuss more fully below, the somewhat mixed findings concerning the effect that some of the aspects of age have on engagement. Examining the three manifestations of age together and their interactions can help to address some of these inconsistencies by identifying possible boundary conditions under which the age-engagement relationships in question are more or less likely to hold.

More generally, this study adds to the extant literature by being the first investigation that both theoretically and empirically integrates in one framework the life span approach with perspectives on relational demography and perceived age-related treatment to test a multifaceted model of how age affects employee work engagement. In so doing, the study contributes to the area by broadening the scope of the analysis in two main ways. We examine the relative importance of key objective and subjective aspects of age as determinants of employee work engagement. In addition, we contribute to a better understanding of how the impact of age on engagement can vary depending on the broader social and demographic context of the organization and, in particular, on the way management is perceived as treating individuals of different ages at the workplace and on the age composition of the workforce.

We test our conceptual model with a field study among teachers from 16 secondary public schools in Italy. This occupational group is of particular interest to the present study not only because education is one of the sectors where workforce aging and age dissimilarity have become most accentuated in recent years (OECD, 2021), but also because research conducted in the school context has shown that teachers who are engaged in their work perform better in their job (Bakker and Bal, 2010) and are more likely to have students who are engaged in learning (Roth et al., 2007). Therefore, shedding light on how age-related factors influence teachers’ work engagement in a time of unprecedented demographic change is both theoretically and practically relevant. Moreover, in terms of research design, public secondary schools offer an ideal setting for the present study because teachers are a large segment of the workforce that, at least in Italy, is quite homogeneous across schools in terms of job characteristics (e.g. autonomy, task variety) and extrinsic rewards (e.g. remuneration, career advancement), all factors that may potentially affect work engagement.

Theoretical background and hypotheses

Beyond a single-facet model of how age affects employee engagement

A number of researchers have claimed that age is a multifaceted concept (Cleveland and Shore, 1992; Kooij et al., 2008; Le Blanc et al., 2017), suggesting that the focus on chronological age should be complemented with other age-related constructs (Truxillo et al., 2017). North (2019) argued that extant perspectives have several merits but also some important limitations due to the prevailing overemphasis on chronological age that leaves open two important challenges: bringing clarity to ambiguous and partially inconsistent results regarding the effect of age on employee outcomes and incorporating context into the conceptualization of age. Beyond a purely numerical perspective, the meaning of being old, middle-aged or young is very malleable and dependent on several contextual factors within organizations, industries and countries (North, 2019). However, while numerous studies have investigated relationships between age and various work outcomes, only a few studies have examined the contextual boundary conditions of these relationships (Zacher and Froidevaux, 2021).

In order to fill this gap, in this study we examine the interplay between what can be thought of as person- and context-grounded measures of age (Cleveland and Shore, 1992). Indeed, in addition to chronological age, we included two other operationalizations of age, one objective and the other more subjective, that capture distinct aspects of the context within which the employee works: relational age and perceived age-related treatment.

Relational age defines personal age relative to the age distribution of other members in the organization/team to account for the individual’s experience of being different at work in terms of age (Tsui et al., 1992). According to the relational demography approach, the same chronological age (e.g. being a 30-year-old) may have quite a different meaning and result in different work experiences for the individual depending on the extent to which he/she is dissimilar in age compared to other people at the workplace and, relatedly, whether he/she belongs to a minority or majority group in terms of age (e.g. Reinwald and Kunze, 2020; Riordan and Wayne, 2008). As an illustration, relational demography generally assumes that the age dissimilarity between a 30-year-old and a 60-year-old is greater than the age dissimilarity between a 30-year-old and a 35-year-old. Moreover, it is expected that being 30 years old in a work context where the majority of other people are in their thirties may have a very different meaning to being 30 years old in a context where the majority of other colleagues are significantly younger and/or older. Therefore, the construct of relational age conceptualizes personal age in comparative terms, relative to others in the surrounding demographic context.

In contrast, perceived age-related treatment (PAT) captures individuals’ perceptions of the extent to which the organization values and treats people of their own age group fairly and respectfully (Sammarra et al., 2021). Previous research has shown that both older and younger workers often perceive that they are being treated unfairly (intentionally or unintentionally) at work based on their age (e.g. Sammarra et al., 2021; Snape and Redman, 2003), due to the widespread diffusion of age norms and age stereotypes in the workplace and society at large. Employee perceptions of negative age-related treatment may develop as a result of interpersonal interactions and/or organizational norms and practices (Kunze et al., 2011). For example, an employee might perceive he/she is being treated unfairly at work due to age if he/she feels excluded from social, recreational or even work activities for being too old or too young. Likewise, an employee may perceive he/she is being treated unfairly when he/she thinks that abilities and opinions of people of his/her age are unlikely to be valued and supported by the organization. We argue that such employee perceptions convey important contextual cues that influence how the individual experiences his/her personal age at work.

Although there is general agreement that a multifaceted model of age is needed to advance the state of the art, research has not kept pace with a perspective that includes multiple facets of the concept when studying engagement.

Drawing on life span theories, a first stream of studies has focused uniquely on chronological age (Goštautaitė and Bučiūnienė, 2015; James et al., 2011; Kim and Kang, 2017), suggesting an age-related increase in work engagement. The evidence, however, is somewhat mixed. Some studies appear to support a positive chronological age—work engagement association (Goštautaitė and Bučiūnienė, 2015; Kim and Kang, 2017), although James et al. (2011) reported a curvilinear relationship. Conversely, Avery et al. (2007) reported a negative and significant bivariate correlation between chronological age and work engagement.

To the best of our knowledge, only two studies have explored whether relational age affects work engagement (Avery et al., 2007; Yang and Matz-Costa, 2018). Using a perceptual measure of relational age, Avery et al. (2007) found that merely working with others of a seemingly similar age is not necessarily associated with higher employee engagement. Interestingly, however, age similarity and engagement were linked more closely among older than among younger employees. Similarly, only three studies have examined work engagement as an outcome of age-related treatment that is perceived as unfair (Bayl-Smith and Griffin, 2014; Boone James et al., 2013; Volpone and Avery, 2013). The expected negative association was confirmed in two of these studies (Bayl-Smith and Griffin, 2014; Volpone and Avery, 2013). Interestingly, Boone James et al. (2013) found that when employees view discrimination as being intentional, the relationship to employee engagement is more negative for younger workers than it is for older workers.

Taken together, these studies not only show that different conceptualizations of age are relevant to explain workers’ level of engagement, but also point to possible interactive effects among these predictors, suggesting that a more complex conceptualization of age is needed to unravel the interplay among different facets of age on work engagement.

The interactive effect of relational age and perceived age-related treatment on employee engagement

The JD-R model has incorporated fairness as an important antecedent of engagement. According to Maslach et al. (2001), a lack of fairness can accentuate burnout, while a positive perception of fairness can improve engagement. Previous studies on perceived age discrimination have confirmed this view, showing that the perception of unfair age-related treatment negatively affects work engagement (Bayl-Smith and Griffin, 2014; Volpone and Avery, 2013; Yeung et al., 2021).

The relational demography framework (O’Reilly et al., 1989; Tsui et al., 1992, 2002) offers a useful theoretical lens to explain the interactive effect of relational age and perceived age-related treatment on work engagement. Drawing on the theories of social identity (Tajfel and Turner, 1986) and self-categorization (Turner, 1987), relational demography posits that when relational age is high (i.e. when individuals are more dissimilar in terms of age to others in the organization), age is more likely to become a salient category for self-categorization, favoring individuals’ tendency to internalize their membership to specific age groups (e.g. being an older worker) to define their own social identity. Self-categorization fosters social comparison based on age groups, that is juxtaposing the ingroup (e.g. older workers) versus the outgroup (e.g. younger workers). Therefore, when age dissimilarity is high (high relational age), individuals will tend to be more concerned about how they are treated at work compared to other age groups (e.g. Avery et al., 2008; Kunze et al., 2011) and, hence, be more likely to perceive unfavorable age-related treatment as a threat to their age ingroup (Hogg and Terry, 2000; Sammarra et al., 2021). And this, in turn, is likely to favor disengagement from work as a coping strategy to avoid further threats to one’s social identity (Avery et al., 2007). On this basis, therefore, we propose the following hypothesis.

H1: Relational age interacts with perceived age-related treatment (PAT) to predict employee engagement, such that the negative relationship between perception of unfair age-related treatment (low PAT) and engagement is stronger when relational age is higher.

The interactive effect of relational age and chronological age on employee engagement

Based on life span theories, we expect individual differences in chronological age to influence the perceived quality of relationships in the work context, a key antecedent of employee engagement.

According to socioemotional selectivity theory (SST; Carstensen et al., 1999), as people get older, they generally tend to place greater emphasis on the quality of social relations compared to younger individuals. Van Lange et al. (1997) offered empirical support for the assumption that prosocial orientations that emphasize cooperation and equality are more pronounced as age increases. Conversely, they found a stronger individualistic orientation, which focused on self-interest only, among younger adults (Van Lange et al., 1997). Accordingly, in a meta-analysis on age and work motivation, Kooij et al. (2011) found a positive relationship between age and intrinsic motives such as connection with others.

Explanations for these findings rely on two key motivational changes that are related to the aging process: the generativity motive (McAdams and de St. Aubin, 1992) and perceptions of remaining time at work (Kooij et al., 2018). On average, the generativity motive—which refers to the tendency to care for others, act as a parent, and help society and future generations (Kanfer and Ackerman, 2004)—increases with age. Empirical evidence shows that older employees tend to have a higher generativity motive compared to younger employees (Kooij et al., 2011; Krumm et al., 2013), suggesting that older workers may be more willing to perceive inter-group relations with younger colleagues more cooperatively and less competitively. Conversely, previous work by Standifer et al. (2013) indicated that younger workers exhibited a higher age similarity preference and were more inclined to prefer working with others of a similar age, compared to their older colleagues.

Perceived remaining time at work, that generally decreases with age (Kooij et al., 2018; Rudolph et al., 2018), influences how individuals prioritize goals, such that younger workers with a more expansive future time perspective tend to be more concerned with achievement-oriented goals, whereas older employees with a more limited future time perspective tend to focus on positive socioemotional experiences (Carstensen et al., 1999). In line with this argument, Dello Russo et al. (2021) found that age was significantly and positively related to workers’ positive emotions at the day level of analysis. Another recent study on the relationship between intergenerational contact and work engagement showed that older colleagues were more likely than younger colleagues to derive greater motivational benefits from contact with members of different age groups due to their constrained perception of remaining time at work (Burmeister et al., 2021).

Based on these considerations and taking into account motivational changes associated with aging, we expect chronological age to interact with relational age in predicting employee engagement.

H2: Chronological age interacts with relational age to predict employee engagement, such that the negative relationship between relational age and engagement is stronger for younger employees than for older employees.

The interactive effect of perceived age-related treatment and chronological age on employee engagement

Socioemotional selectivity theory (SST) offers a useful theoretical approach to explain the interactive effect of perceived age-related treatment and chronological age on work engagement. Specifically, SST suggests that patterns of motivation change as people age, with increases in some types of motivation (e.g. intrinsic motivation, generativity) and decreases in others (e.g. extrinsic motivation, training motivation). Accordingly, Kooij et al. (2011) showed a negative relationship between age and extrinsic motives (i.e. preferences for job characteristics and outcomes that occur as a consequence of work, such as pay and advancement). Other studies have shown that intrinsic values are stronger in older versus younger employees (Inceoglu et al., 2009). Drawing on SST, Brienza and Bobocel (2017) explored how older workers, vis-à-vis younger workers, react to perceptions of fairness, finding that distributive and procedural justice were significant predictors of deviance and emotional exhaustion for younger (but not older) employees, whereas interpersonal justice predicted deviance and emotional exhaustion for older (but not younger) employees.

Thus, based on previous studies that adopt SST-based predictions, we expect that younger workers, who tend to prioritize extrinsic rewards, are likely to be more negatively affected by the perception of unfair treatment and thus exhibit lower levels of work engagement compared to older workers. Therefore, we posit the following:

H3: Chronological age interacts with perceived age-related treatment (PAT) to predict employee engagement, such that the negative relationship between perceived negative age-related treatment (low PAT) and engagement is stronger for younger employees than for older employees.

The effect of the three-way interaction between relational age, chronological age and perceived age-related treatment on employee engagement

We expect engagement to increase with relational age when both chronological age and perceived age-related treatment are high. Based on the relational demography framework, we argue that as age dissimilarity increases, age becomes more salient as a social category, leading older employees to be more likely to internalize their age-group membership and to react to fair and respectful treatment toward employees of their age in-group with increased work engagement. Moreover, drawing on SST, we expect that when older workers perceive they are being treated fairly at work, being more dissimilar in age from other organizational members may provide them with a greater opportunity to fulfill generativity goals, thereby increasing their engagement at work. Hence, we expect the combination of high perceived positive age-related treatment and high chronological age to have a positive conjoint moderating effect on the relationship between relational age and engagement.

Conversely, when both chronological age and perceived age-related treatment are low, we expect the association between relational age and engagement to be negative. Based on SST, we expect younger workers to prioritize different work goals to older workers, and given their stronger individualistic orientation (Van Lange et al., 1997), working with age-dissimilar colleagues may be less likely to fulfill younger employees’ work goals. At the same time, under conditions of negative perceived age-related treatment (low PAT), younger employees perceive that their own age group is treated unfairly and this is likely to elicit an identity threat as age dissimilarity to other colleagues increases, leading to reduced work engagement as a coping strategy (Sammarra et al., 2021). Therefore, we expect the combination of low chronological age and negative perceived age-related treatment (low PAT) to have a negative conjoint moderating effect on the relationship between relational age and engagement.

Under conditions of high chronological age and negative perceived age-related treatment (low PAT), it is less clear what the effect of increasing relational age would be on older workers’ engagement. Indeed, we expect that when older workers perceive they are being treated unfairly, this threat-inducing contextual cue will elicit negative bias against younger colleagues. This may compensate or inhibit the positive effect of fulfilling generativity needs that older workers would possibly experience as relational age increases.

Finally, under conditions of perceived positive age-related treatment (high PAT) and low chronological age, relational age may not have an effect on engagement. More specifically, based on SST, we expect that for younger workers, who prioritize individualistic and achievement-oriented goals over generativity needs, working with age-dissimilar colleagues may be irrelevant to their work engagement as long as they perceive that they are being treated fairly and respectfully. In this case, therefore, we do not expect age dissimilarity to have much of an effect, either positive or negative, on younger workers’ engagement.

Putting these various arguments together, we propose the following overall three-way interaction hypothesis:

H4: There is a three-way interaction between relational age, chronological age, and perceived age-related treatment in predicting work engagement, such that the relationship between relational age and employee engagement is (a) positive when both chronological age and perceived age-related treatment are high, (b) negative when both chronological age and perceived age-related treatment are low, and not significant when either (c) chronological age is low and perceived age-related treatment is high, or (d) chronological age is high and perceived age-related treatment is low.

Method

Respondents and procedure

Data were collected in 16 public secondary schools located in central Italy through a questionnaire administered in the 2017–2018 academic year. Participants were assured anonymity and guaranteed that their responses would be reported as an aggregate score only. The sample included 434 teachers, 66% of whom were women, with a mean age of 49 (SD = 9.27 range: 26–71 years old). In the same academic year, the mean age of the overall population of schoolteachers in Italy was 50, 68% of whom were women (OECD, 2019). Teachers in the sample had an average of 9.7 years of tenure in the current school (SD = 8.1), and 19.7 years practicing as a teacher (SD = 10.5).

Measures

Employee engagement

We used the shortened version of the Utrecht engagement scale (Schaufeli et al., 2002), which has been validated in the school setting (Bakker and Bal, 2010). It consists of three subscales capturing the core dimensions of vigor (three items; e.g. “At my work, I feel bursting with energy”), dedication (two items; e.g. “I find the work that I do full of meaning and purpose”), and absorption (three items; e.g. “I feel happy when I am working intensely”). The three subscales were combined to measure the overall level of engagement (Cronbach’s alpha = 0.75).

Perceived age-related treatment (PAT) was measured at the individual level using a four-item scale already adopted in previous studies (Sammarra et al., 2021) and adapted from the fairness subscales developed by McKay et al. (2008). Sample items were “This school respects the view of people of my age” and “Not everyone of my age in this school is treated fairly (R).” The Cronbach’s alpha for the scale was 0.75.

Chronological age (CA) is a self-reported measure computed as number of years since birth.

Relational age (RA) was conceptualized as an objective measure of age dissimilarity. Following previous studies (e.g. O’Reilly et al., 1989) we computed the Age Dissimilarity Index as the difference between an individual’s age and the ages of all other individuals in his/her school. The index was calculated as the square root of the summed squared differences between an individual’s age and the age of every other individual in the same school, divided by the total number of respondents in the unit.

Control variables

Gender, health status and professional identification were added as control variables. Gender (0 = male, 1 = female) was included to check whether men and women have the same opportunities to experience psychological conditions that lead to personal engagement (Banihani and Syed, 2020). Health was chosen because physical and psychological health is not only likely to be related to age (Ferraro, 2006), but is also likely to be relevant for explaining employees’ psychological availability, which leads to high levels of engagement (Kooij et al., 2013). This variable was measured via a single item, derived from the European Social Survey, capturing self-reported general health (“How is your (physical and mental) health in general?”). The responses were based on a five-point scale (1 = “very bad” and 5 = “very good”). Finally, we included professional identification because identification is not only likely to be positively related to age (Baltes et al., 2012) but is also a resource that can help employees handle challenging demands at work and can, therefore, have a positive effect on engagement (Hirschi, 2012). Professional identification was measured using a three-item scale adapted from Mael and Ashforth’s (1992) organizational identification scale. Sample items were “When someone criticizes my profession, it feels like a personal insult” and “I’m very interested in what others think about my profession.”

Analysis procedures and preliminary analysis

To take account of the nested data structure, data were analyzed using multilevel regression with maximum likelihood estimation. Specifically, we tested all our hypotheses at the individual level (n = 434 observations), treating individuals as the first level in the analysis and the 16 schools in which individuals were nested as the second level grouping variable. The second level of analysis was included to take account of the possible non-independence of observations within schools. All variables were measured at the individual level (level 1) and, except for gender and engagement, were standardized for the main analysis.

Prior to conducting the main analysis, we ran a null model to check the proportion of the variance in the dependent variable that resided at the school level. We obtained an Interclass Correlation Coefficient (ICC1) of 0.04 indicating that around 4% of the variance in employee engagement was due to differences across schools, with the remaining 96% attributable to teacher differences.

We estimated a CFA model to verify the potential influence of common method bias and to establish the discriminant validity of the scales, including employee engagement, perceived age-related treatment, and professional identification. As part of the CFA, we examined standard goodness-of-fit indices and compared a main three-factor model where all the items for the three measures loaded on their respective hypothesized factors, to four alternative models. As shown in Table 1 these included a one-factor model where all items were made to load on a single common factor, and three alternative two-factor models where, in turn, the items for each one of the three measures were made to load on one factor and the items for the combination of the remaining two measures were made to load on the second factor. The CFA results are shown in Table 1. The three-factor model showed a good fit (χ² = 203; degrees of freedom [df] = 87; comparative fit index [CFI] = .93; Tucker Lewis index [TLI] = .91; root mean square error of approximation [RMSEA] = .05; standardized root mean square residual [SRMR] = .05; χ²/df = 2.3). Importantly, as the sequential χ² difference test results in the last column of the table show, the three-factor model fits the data significantly better than any of the other alternative models. This suggests that the variables in the study are distinct.

Table 1.

Measurement model comparisons: Confirmatory factor analysis results.

Models	χ²	df	RMSEA	CFI	TLI	SRMR	Hypothesized model vs alternative models
Full measurement model	203	87	0.05	0.93	0.91	0.05
Model A	362	89	0.08	0.83	0.80	0.07	∆df = 2, ∆ χ² = 159***
Model B	427	89	0.09	0.79	0.76	0.09	∆df = 2, ∆ χ² = 224***
Model C	654	89	0.11	0.65	0.59	0.09	∆df = 2, ∆ χ² = 451***
Model D	811	90	0.13	0.56	0.49	0.10	∆df = 3, ∆ χ² = 608***

n = 487; TLI = Tucker-Lewis Index; CFI = Comparative Fit Index; RMSEA = Root Mean Squared Error of Approximation; SRMR = Standardized Root Mean Square Residual.

***

p < 0.001.

Model A: employee engagement and professional identification combined into a single factor.

Model B: perceived age-related treatment and professional identification combined into a single factor.

Model C: employee engagement and perceived age-related treatment combined into a single factor.

Model D: all items combined into a single factor.

Results

Table 2 shows the means, standard deviations, correlations, and internal consistencies (Cronbach’s alpha) for all the variables in the analysis. The control variables were all significantly correlated with our outcome of interest, with women, healthier and more identified teachers all exhibiting higher levels of work engagement. While chronological age and perceived age-related treatment were positively and significantly associated to engagement, relational age was not significantly related to engagement.

Table 2.

Descriptive statistics and bivariate correlations.

Variable	Mean	SD	2.	3.	4.	5.	6.	7.
1. Gender^a			−0.120**	0.093*	0.027	−0.074	0.025	0.136**
2. Health	3.26	1.05		−0.005	0.015	−0.023	0.160***	0.206***
3. Professional identification	3.62	0.79		(0.65)	0.111*	−0.014	0.119**	0.309***
4. Chronological age	48.78	9.27				−0.166***	0.056	0.113*
5. Relational age	2.09	0.95					0.138**	−0.014
6. Perceived age-related treatment	3.36	0.76					(0.75)	0.235***
7. Employee engagement	3.99	0.50						(0.75)

n = 443.

0 = male; 1 = female; Internal consistency reliabilities in parentheses on the diagonal when applicable.

*p < 0.05. **p < 0.01. ***p < 0.001.

The results of the main analysis are shown in Table 3. Model 1 shows that female teachers have a higher level of work engagement than male teachers and that employees’ health and professional identification are both positively related to work engagement.

Table 3.

Tests of hypotheses: Multilevel analysis results.

Variables
	Model 1		Model 2		Model 3
	β	s.e.	β	s.e.	β	s.e.
Gender	0.133**	0.046	0.121*	0.047	0.131**	0.046
Health	0.108***	0.021	0.097***	0.021	0.106***	0.021
Professional identification	0.146***	0.021	0.138***	0.022	0.146***	0.022
Chronological age (CA)			0.039^†	0.022	0.019	0.026
Relational age (RA)			−0.003	0.025	−0.026	0.027
Perceived age-related treatment (PAT)			0.069**	0.022	0.072**	0.0.22
CA*RA					−0.010	0.021
PAT*RA					0.031	0.023
CA*PAT					−0.044^†	0.023
CARAPAT					0.057**	0.020
Intercept	3.90***		3.918***		3.90***
$σ_{υ}$	0.005		0.007		0.008
$σ_{ε}$	0.202		0.195		0.190
Χ²(df)	82.22(3)		98.00(6)		112.85(10)
ICC	0.024		0.034		0.040

n = 434; Organizations = 16; Empty model: Intercept = 3.99***; s.e = 0.036; ICC = interclass Correlation Coefficient; †p < 0.10; *p < 0.05; **p < 0.01; ***p < 0.001.

To examine the effect of the three facets of age on work engagement we introduced these variables in Model 2. As expected, perceived age-related treatment was positively and significantly related to employee engagement (β = 0.069, SE = 0.022, p = 0.002), while only a weak or non-significant relationship was found between either chronological age (β = 0.039, SE = 0.022, p = 0.077) or relational age (β = −0.003, SE = 0.025, p = 0.898) and our outcome of interest.

Finally, Model 3 shows the results of the regression designed to test our hypotheses. Hypothesis 1 and Hypothesis 2, postulating that chronological age (H1) and perceived age-related treatment (H2) would interact with relational age to predict employee engagement, are not confirmed. Hypothesis 3, postulating that chronological age would interact with perceived age-related treatment to predict employee engagement, was marginally confirmed (β = −0.044, SE = 0.023, p = 0.065). To determine the form of the interaction, we plotted the result showing the effect of perceived age-related treatment on engagement one standard deviation above and one standard deviation below the mean of chronological age, respectively (Aiken et al., 1991) (see Figure 1). Simple slope tests indicated that, in line with hypothesis 3, the positive relationship between perceived age-related treatment and engagement is stronger for younger workers than for older ones (b_low = 0.07, t = 3.17, p = 0.002; b_high = 0.03, t = 0.83, ns).

Figure 1.

Two-way interaction between chronological age and perceived age-related treatment in relation to employee engagement.

Finally, the results of Model 3 show that the three-way interaction effect between chronological age, relational age and perceived age-related treatment is positive and significant (β = 0.057, SE = 0.020, p = 0.005), suggesting that the three proposed conceptualizations of age jointly interact in predicting employee engagement. Figure 2 shows the graphical plotting of the results. In line with hypothesis 4 (a), plot 1 shows that when both chronological age and perceived age-related treatment are high, the effect of relational age on engagement is positive and significant just above the five percent level (t = 1.912, p = 0.057).

Figure 2.

Three-way interaction between relational age, chronological age, and perceived age-related treatment in relation to employee engagement.

In contrast, and not consistent with hypothesis 4 (d), when chronological age is high and perceived age-related treatment is low, the effect of relational age on engagement is unexpectedly negative and significant (t = −2.298, p = 0.022) (plot 3). For the other two combinations of the moderators (Low CA, High PAT; Low CA, Low PAT) the relationship between relational age and engagement is non-significant. The non-significant low chronological age, high perceived age-related treatment result (plot 2) is consistent with expectations (hypothesis 4 (c)), but the non-significant low chronological age, low perceived age-related treatment result (plot 4) is not since in this case the expectation was that the effect of relational age on engagement would be negative (hypothesis 4(b)). Overall, therefore, hypothesis 4 is only partially confirmed. Specifically, the results provide support for the idea that relational age, chronological age and perceived age-related treatment jointly interact to predict work engagement. However, not all hypothesized elements of the three-way interaction found support in the data. More generally, taken together, our results suggest that older workers are more sensitive to relational age than younger workers, and that their reaction to relational age is contingent on perceived age-related treatment. Specifically, at high levels of perceived positive age-related treatment the effect of relational age on engagement for older workers is positive (slope 1), while at low levels of perceived positive age-related treatment the effect is negative (slope 3). In contrast, younger workers’ level of engagement does not vary depending on relational age, suggesting that this age cohort is not sensitive to age dissimilarity. However, results show that younger workers are sensitive to perceived age-related treatment, as slope 2 (high PAT) is higher than slope 4 (low PAT).

Discussion

This study emphasizes the importance of conceptualizing age more broadly by showing that the interaction between chronological age, relational age and perceived age-related treatment explained employee work engagement better than any of the individual dimensions did on their own. Moreover, our results direct attention to a number of key theoretical and empirical points, including the operation of important asymmetric effects linked to age.

Interestingly, perceived age-related treatment was the only dimension that affected employee work engagement both independently and in conjunction with the other two conceptualizations of age. This result points to the importance of supporting an age-friendly work environment, extending existing research in at least two ways. First, while previous studies on age diversity climate have shown that creating an environment where individuals of all ages are fairly treated can foster organizational-level work-related outcomes (Boehm et al., 2014; Profili et al., 2016), with this study we focus on the effect that perceptions of an age-inclusive context have on a key individual level work-related outcome (i.e. engagement), contributing to gaining a better understanding of a far less commonly explored relationship (Bellotti et al., 2022). Second, we expand previous findings on the relationship between age-related climate and work outcomes by examining how, at the individual level, the interrelation between perceptions of fair treatment with respect to age and two objective age-related factors (i.e. chronological age and relational age) affects work engagement. In this respect, our findings add to the literature by showing that fair and respectful age-related treatment is especially important for younger workers’ engagement, and that, distinctively for older workers, perceived age-related treatment was a key contextual boundary condition that shaped how mature employees responded to age dissimilarity. Hence, fair and respectful age-related treatment may be very crucial in sustaining the engagement of all employees, especially in the context of an aging and age diverse-workforce.

Our findings did not support the positive direct relationship between chronological age and employee engagement found in some previous studies (Goštautaitė and Bučiūnienė, 2015; Kim and Kang, 2017). However, consistent with our hypothesis, chronological age did play a key role in the triple interaction, suggesting that the asymmetrical age effects observed in this study may be due to individual motivational differences across the lifespan. Thus, consistent with SST, our findings supported the view that younger workers - who tend to prioritize extrinsic rewards and have a more open-ended time perspective at work - are especially likely to be negatively affected by the perception of unfair treatment and react to low perceived age-related treatment with reduced work engagement, irrespective of the level of age dissimilarity.

Moreover, we are the first to show that older workers may respond differently to relational age depending on their perceived age-related treatment. At low levels of perceived positive age-related treatment, we found that older workers’ engagement was negatively related to age dissimilarity. This finding was not expected as, based on our hypothesis, it was not necessarily clear what the effect on older workers’ engagement would be of increasing relational age under conditions of low perceived age-related treatment (hypothesis 4d).

A possible explanation for this result is that older workers may feel threatened by the presence of younger employees in the organization when they perceive they are being treated unfairly in comparison with other age groups (Sammarra et al., 2021) and that this threatening contextual cue may also reduce older workers’ sensitivity to generativity motives. This finding could also be explained by older workers, with a constrained future time perspective, possibly feeling more threatened by the perceived unfair treatment by the organization when relational age is high as this would entail greater competition over organizational resources.

In contrast, and according to our hypothesis (4a), at high levels of perceived positive age-related treatment, we found that the association between relational age and older workers’ engagement was positive, although reaching only marginal significance. This may suggest that perceptions of fair and respectful age-related treatment provide an identity affirmation contextual cue (Sammarra et al., 2021), creating the condition for older workers to view age dissimilarity as an opportunity to fulfill their generativity work needs. Moreover, when relational age is high, perceptions of positive age-related treatment help employees feel they are valued members of the organization despite age differences. Based on SST, this is expected to satisfy older workers’ affect-related goals and help them experience a more positive state of mind (Yaldiz et al., 2018).

Taken together, an important theoretical implication of the asymmetrical effects found in this study between younger and older workers is that theory on age-related differences at work (Kanfer and Ackerman, 2004; Kanfer et al., 2013; Kooij et al., 2011) should take into account contextual factors in order to explain how motivational changes across the life span may be associated with divergent effects.

Finally, this study makes three unique contributions to theory building concerning relational demography. First, to our knowledge, only two studies have examined empirically the association between relational age and employee engagement (Avery et al., 2007; Yang and Matz-Costa, 2018) and both of them have used subjective measures of relational age. Focusing specifically on how objective relational age relates to engagement, therefore, directly contributes to this strand of research. Second, this study extends the extant literature on moderators of demographic dissimilarity suggesting that contextual factors (i.e. perceived age-related treatment) need to be examined together with individual factors (i.e. chronological age) in order to discern how the effect of age dissimilarity on work engagement may vary from negative, to neutral, and even to positive. Third, drawing on life span theories, this study suggests that individual differences in work motives and emotion regulation across the life span contribute to explaining why younger and older individuals react differently to age dissimilarity. In doing so, we respond to the recent call to incorporate affective and emotional responses to dissimilarity in relational demography (Chattopadhyay et al., 2016) and expand the explanation of asymmetrical effects of age dissimilarity (Chattopadhyay et al., 2004; Sammarra et al., 2021).

Taken together, at the conceptual level, the integrative framework developed in this paper provides an important addition to current thinking on how age affects employee outcomes since it combines life span theories with relational demography and age-related treatment perspectives, thereby helping to tie together disparate theoretical elements that, to date, have been unconnected and treated separately.

Our findings also have several relevant practical implications. First, organizations should acknowledge that chronological age per se cannot necessarily help to predict the level of employees’ work engagement. Instead, chronological age is important because it interacts with other facets of age which are context-related. Specifically, our results point to the fact that in order to develop a high level of engagement amongst both older and younger workers organizations should foster the creation of an age-friendly work environment. For example, organizations can implement formal mentoring programs where older employees take on roles as mentors and experts, therefore acknowledging their experience and reinforcing their feeling of being valued. Or they can introduce age-inclusive HR practices aimed at increasing the perceptions of a positive age-related treatment (e.g. training for an age-diverse work environment, including programs which highlight the strengths of each age cohort in the workplace) (Pahos et al., 2021; Sousa et al., 2021). Organizations should encourage managers to engage and retain younger workers, providing training and networking opportunities (Fasbender et al., 2020) or enhancing their leisure time (Twenge et al., 2010). Employers who do not create a positive age-related climate may undermine engagement among all their employees.

Second, our findings suggest that age diversity and perceived age-related treatment mutually reinforce each other in enhancing older workers’ engagement. Therefore, the more organizations have an age-diverse work setting, the more they will benefit from investing in a positive age diversity climate and, conversely, the greater the risk that employee perceptions of poor age-related treatment will result in older workers’ disengagement.

Overall, these results should encourage employers to overcome the widespread and simplistic idea that older workers may be less engaged and that older age is a competitive disadvantage. Organizations may only be able to nourish work engagement among all their employees by viewing age in a wider context and taking into account the interactions between its different facets

As with most empirical research, our study has several limitations. First, one concern in moderated multiple regression is spurious results. However, in the present study, spurious effects are likely to have a negligible impact on results (Aguinis and Gottfredson, 2010) for several reasons: (i) the variables included as predictors and moderators are weakly correlated among one another; (ii) we did not perform median splits on any of the variables involved in the interactions; (iii) only one moderator (perceived age-related treatment) and the dependent variable use ordinal-level measurement, but none of them consist of scales that are composed of binary items.

Second, some of the core variables in our analysis were self-reported, which implies that same-source bias could have impacted our results. However, we took several steps to minimize this risk. We located the scales items non-consecutively in the survey to create temporal and psychological separations. We also tested a one-factor model which showed a poorer fit to the data compared to a multifactor solution. In addition, two of our predictors—chronological age and relational age—are based on objective data rather than being measured using perceptual Likert scales. Importantly, all the hypotheses test interaction effects that are less likely to be affected by common method bias.

Third, although we built our hypotheses on well-established and empirically validated theories, we were not able to test some implicit assumptions in our theoretical arguments (e.g. motivational changes associated with aging). Future studies would benefit from directly measuring the processes implied in our study.

Fourth, another limitation derives from the sample, which included only secondary school teachers from a single country. Although exploring how and when different age-related variables affect engagement of this professional group is particularly relevant, future research may benefit from focusing on organizations where age diversity has received greater attention from employers (i.e. implementing specific age-inclusive HR practices) as this may change individual perceptions of age-related treatment. Future research should also explore if differences exist in terms of age-related accounts of engagement across organizations with different age distribution.

We also acknowledge that our results need to be interpreted with caution because of the cross-sectional design of this study. However, this limitation is mitigated by the fact that two of our independent variables (i.e. chronological age and relational age) are based on objective rather than perceptual data, and that having these two age-related variables as predictors reduces the possibility for reverse causality. Future research should be conducted using longitudinal or experimental designs in order to replicate and extend our results.

Finally, and more broadly, future research could also expand the present analysis in two ways. First, by considering the simultaneous impact of the three aspects of age on a wider range of employee outcomes such as organizational commitment and job satisfaction, and, second, by extending the analysis to other dimensions of age at the workplace that we have not considered in the present paper.

Conclusion

Building on and extending prior research, this study developed and tested a multifaceted model of how age affects employee work engagement. Our findings showed asymmetric effects between younger and older workers. Younger workers were relatively insensitive to relational age and, in terms of their engagement, they responded primarily to the age-related treatment they perceived they received from the organization. In contrast, older workers responded differently to relational age depending on their perceived age-related treatment. When older workers perceived that their own age group was treated fairly and respectfully they responded positively to age dissimilarity, with an associated positive effect on their engagement. On the other hand, when they thought that they were not being treated fairly or respectfully because of their age they responded negatively to age dissimilarity, with an associated negative effect on their engagement. Taken together, our results emphasize the importance of conceptualizing age more broadly, extending the focus on chronological age to other operationalizations of age that take into account the context in which a person works.

Footnotes

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iDs

Alessia Sammarra

Silvia Profili

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