Managing Age Diversity in the Workplace
Summary and Keywords
Many researchers have noted the increasing age of the workforce, but less noted is that the workforce is also becoming more diverse in terms of age. Thus, as the workforce ages, the ability to manage age diversity will become increasingly important. Managing workers of different ages requires understanding the physiological, psychological, and motivational changes that accompany age, as well as how individuals of different ages interact in organizational contexts. With an increased awareness of the multidimensional nature of age, employers can consider useful adaptations to their human resource practices. Dispelling invalid age stereotypes may be accomplished through inclusive HR practices, the use of intergenerational interactions, and providing meaningful work to all employees.
The benefits of a diverse workforce have been strongly established by researchers and endorsed by industry leaders (Roberson, Ryan, & Ragins, 2017). Research suggests that diversity promotes innovation, team performance, employee engagement, organizational commitment, and organizational performance (e.g., Dos Reis, Castillo, & Dobón, 2007; Horwitz & Horwitz, 2007; Nishii, 2013; Roberson et al., 2017; Wegge, Roth, Neubach, Schmidt, & Kanfer, 2008). However, discussions on diversity often overlook age diversity; a recent survey by PriceWaterhouse Coopers found that only 8% of CEOs report incorporating age diversity into their talent management and inclusion efforts (Snowden & Cheah, 2015). While focusing on racial and gender diversity is important, ignoring age diversity may lead organizations to overlook the benefits of age diversity as well as leaving organizations vulnerable to age discrimination claims (Dorrian, 2016).
Discussions on age diversity often focus on the needs of older workers as an increasing number of these individuals delay retirement to remain in the workforce (Harter & Argawal, 2014). However, it is important to recognize that the workplace is not only getting older but also more diverse in terms of the ages represented. In fact, the Bureau of Labor Statistics estimates that by 2026, the percentage of labor force participants between 35 and 45 years old will drop from 27% to 22%, which will be similar to the percentages between 25 and 35 years old (22%), 45 and 54 years old (19%), and those over 55 (24%). Thus, it appears that all age groups will be almost equally represented. While the average age among those in the workforce is 41, the distribution of ages is fairly flat. Thus, while it is true that the average age of a member of the workforce has risen, it is also important to note that age diversity appears to be rapidly increasing.
Benefits of Promoting Age Diversity
The belief that organizations can establish a climate that promotes age diversity is nothing new. Organizational researchers have been exploring the benefits of organizational climate—the shared perceptions employees have in regard to the policies and procedures under which the work is accomplished—for many years (Reichers & Schneider, 1990). Climate research has been applied to safety and creativity as well as diversity. In fact, Gelfand and colleagues defined diversity climate generally as an organization where “employees’ shared perceptions of the policies, practices, and procedures that implicitly and explicitly communicate the extent to which fostering and maintaining diversity and eliminating discrimination is a priority in the organization” (Gelfand, Nishii, Raver, & Schneider, 2005, p. 104).
While the research is limited, there have been a few studies demonstrating that promoting age diversity through a positive climate improves the productivity of the organization. In a large sample of German firms, Boehm, Kunze, and Bruch (2014) found that HR practices that promoted age diversity led to greater returns for the overall organization. At least two studies have found that organizations that promote age diversity—as opposed to organizations that conform to legal prohibitions against discrimination—are more attractive to applicants (Avery & McKay, 2006; Rabl & del Carmen Triana, 2014). Other researchers have focused on the workplace practices that promote a climate for age diversity. Researchers have suggested, for example, that promoting a workplace that is friendly to older workers is attractive to these employees. This would include incorporating flexible hours, the ability to telecommute, programs for phased retirement, and training opportunities (Ranzijn, 2004). Further, organizations that provide a more positive learning climate also see more older individuals participating in training (Armstrong-Stassen & Schlosser, 2008).
The Meaning of Age
In exploring the organizational factors that promote age diversity, it is important to note that age itself can be defined many ways (Kooij, de Lange, Jansen, & Dikkers, 2007). While age itself is considered easy to quantify, it is important to note that age has many different meanings and conceptualizations. As Kanfer and Ackerman (2004) once noted, age can be thought of as a “proxy indicator for a broad constellation of age-related processes that exert diverse and indirect effects on work outcomes” (p. 440). Age is associated, of course, with psychological and physiological changes across the life span. One’s age connotes the generation one is a member of (Generation X, Millennials, or Baby Boomers being the most common). Age also often reflects the stage of one’s career. Moreover, one’s age contains information about the employee cohort one belongs to, particularly in organizations that expanded or developed during particular periods. Thus, age potentially reflects a great deal of information about an individual depending on how age is conceptualized.
This study explores the psychological literature around how age is conceptualized and how we understand these differences. It concludes with suggestions for how age can be better understood as a multifaceted individual difference in the workforce. Sterns and Doverspike (1989) conceptualized age as chronological age, functional age, subjective age, organizational age, and life span age. We feel that this model is informative for understanding age as a multidimensional concept with implications for understanding and managing a diverse workforce.
Chronological age is the number of years since an employee’s birth. There are three potential levels of analysis of chronological age, individual, team, and organizational. Individual chronological age refers to the time since birth of each employee, team chronological age refers to the age of a group of individuals working in a team, and organizational chronological age refers to the age of all employees working for an organization.
Individual-Level Chronological Age
Of the five conceptualizations of age, individual chronological age may be the least predictive of work-related outcomes. Ng and Feldman (2008), for example, found that chronological age was unrelated to job performance, training performance, and creativity, though they did find some evidence that age was curvilinearly related to core job performance (see also Sturman, 2003). Other researchers have also found that, across studies, chronological age is unrelated to job performance (McEvoy & Cascio, 1989). Waldman and Avolio (1986) also found that chronological age was associated with small increases in objective measures of performance but small decreases in supervisor-rated performance. Ng and Feldman (2012) also found that chronological age did not predict work motivation, resistance to change, health, or work–life balance. At least one study found that across nine different organizations, chronological age was unrelated to training satisfaction (Schmidt, 2009), though it may be important to note, however, that individual chronological age is a significant predictor of job performance in studies that explore the age of the rater and the subordinate (Shore, Cleveland, & Goldberg, 2003).
Where individual chronological age may be more interesting as a predictor of workplace outcomes is when chronological age is explored as a signal that one has reached important milestones. After all, chronological age does serve as a reminder of eligibility for social programs such as Social Security and Medicare. Thus, individual chronological age may be more relevant in organizations with mandatory retirement ages or age-related policies. In a large study of employees in various industries, Ekerdt and DeViney (1993) found that older workers found their jobs to be more stressful and burdensome as they approached retirement. As workers age, their chronological age may signal the transition from work to retirement, which is often associated with an increased focus on retirement and disengagement with work-related responsibilities (Ekerdt, Kosloski, & DeViney, 2000). Indeed, research has consistently demonstrated that chronological age is associated with the decision to retire (Adams & Rau, 2004; Kim & Feldman, 2000).
Perhaps the limited findings related to individual chronological age may be enhanced by moving from the focus on individual chronological, which is a single number, to a more sophisticated analysis of the diversity of the ages of employees of teams or the whole organization. There is some research on chronological age in both teams and dyads.
Dyads and Organizational-Level Chronological Age
Chronological age has also been predictive of performance ratings when researchers have explored mismatches in ages between managers and subordinates. Specifically, Shore et al. (2003) found that younger managers gave younger employees higher performance evaluations than they did older employees and the older managers gave more positive evaluations to older employees than they did younger employees. This may suggest that age may be an important predictor of subjective measures of job performance, particularly when the age of the rater is considered (see also Waldman & Avolio, 1986).
Team and Organizational-Level Chronological Age
Chronological age has also been explored in research examining age diversity in teams. There is some debate about the effect of age diversity on teams with some researchers finding no effect (Bantel & Jackson, 1989) or negative effects (Zenger & Lawrence, 1989). Wiersema and Bird (1993), for example, found that age diversity predicted higher team turnover in a sample of Japanese employees. On the other hand, the effects may depend on the age of the teammates. Chattopadhyay (1999) found that older employees reported less organizational self-esteem (i.e., how valued they feel at the organization), poorer peer relations, and less altruism when team diversity was higher but younger employees actually reported greater relations, self-esteem, and altruism when diversity was higher. They suggested that older individuals may feel more threatened by younger workers while younger workers may value the opportunity to work with older workers whom they perceive to be more established in their careers. Demographic differences such as age have been shown to impact team performance, but there is some evidence that the effects of demographic differences on team performance fades over time (Harrison, Price, Gavin, & Florey, 2002). Overall, there is considerable research indicating that younger teammates may benefit from working with older employees and some evidence that age difference may have limited impacts on team performance, but there is little research exploring other conceptualizations of age—such as subjective age or relative age—on team diversity. Whether the effects of chronological age diversity on team performance can better be explained by functional or subjective age has yet to be explored.
Of all of the measures of age, chronological age is probably the least informative in terms of appreciating age diversity. Chronological age is not a strong predictor of job performance or work-related outcomes generally, though chronological age does seem to impact subjective performance ratings between supervisors and subordinates when their ages are disparate. Chronological age is also an important predictor of interest in retirement, though researchers have suggested that chronological age—in these contexts—may simply reflect organizational norms or physiological decline, both constructs that may be better captured by subjective assessments of functional age.
However, to encourage research on the topic of both team chronological age diversity and organizational chronological age diversity, we suggest alternative measurement formats. Whereas an individual employee’s age is typically used as a linear variable, there are several methods to measure aggregates of the chronological age of multiple members of a team or employees of an organization. Exploration of these alternative measures should be considered for future research on age diversity.
Measures of Chronological Age Location
These measures summarize the center or location of the distribution of chronological ages of employees. They are mean, median, and mode. Thus, researchers should consider the distinctions between these three measures when examining team and organizational-level measures of employee chronological age. If the distribution of chronological ages is not symmetrical, the choice of mean, median, or mode will result in differences between these three measures. For example, the modal chronological age of a team of employees may be quite young even though the mean would be older if there was a large range of the individual ages on the team. This difference may suggest that the modal age could have a disproportionate effect on team outcomes.
Measures of Chronological Age Dispersion
Perhaps most important to research on age diversity are the measures of chronological age dispersion. These measures include the mean deviation (the average of the absolute deviations from the mean age), the interquartile range (the points at which the age distribution first reaches the values of ¼ and ¾), and the standard deviation (the square root of the average of the squared deviations from the mean age). Each of these three measures of chronological age dispersion can help researchers to measure the important aspects of chronological age diversity in teams and organizations. For example, if one team has a very small standard deviation of chronological age dispersion, that team may be more likely to succumb to the weaknesses of an overreliance on concurrence seeking, whereas another team with a larger standard deviation of chronological age dispersion may more carefully consider alternative options (Wanous & Youtz, 1986).
Measures of the Shape of Chronological Age Distribution
In addition to the location and dispersion of chronological age, researchers should consider the shape of the distribution of chronological ages of employees in teams and organizations. There are several types of such measures. Although most distributions may be unimodal, multimodal chronological age distributions suggest that the peak frequency of ages within a team or organization may occur at more than one age, see Figure 1. It could be possible that there are large numbers of younger and older persons on a team, and this form of age diversity could result in very different outcomes than a team with the most frequent number of ages occurring in the middle. For example, if an organization has a multimodal age distribution, there may be an expected increase in conflict between age groups when compared to another organization that has a unimodal age distribution.
In addition, unimodal age distributions of teams and organizations can vary by skewness and kurtosis. Skewness is the cubed deviations from the mean age. For example a right-skewed chronological age distribution may result in an organization culture or climate that favors or emphasizes the importance of youthful values or perspectives, whereas a left-skewed chronological age distribution would favor older values or perspectives. Kurtosis is the average of the fourth powers of the deviations from the mean. It measures the peakedness of a distribution. Chronological age distributions that are very peaked would be considered more leptokurtic, and those that are less peaked are considered more platykurtic. This aspect of the skewness of employee chronological age distribution could have a significant impact on important organization outcomes. For example, a chronological age distribution that is more platykurtic suggests greater diversity in chronological age, and this could result in greater increases in employee input and empowerment, leading to higher organizational performance. Alternatively, a chronological age distribution that is more leptokurtic may have the opposite effect.
Thus, while chronological age does not seem predictive of individual performance, we suggest that chronological age distribution can provide an organization with important information about age diversity climate. To our knowledge, whether the shape or the skewness of the organization’s age distribution predicts an organization’s age diversity climate has yet to be explored.
Functional age refers to the physical and cognitive changes associated with increasing age. Aging is, regrettably, associated with losses in both physical and mental abilities. Skelton, Greig, Davies, and Young (1994) found that strength steadily declines as one enters later adulthood. Cognitive abilities such as fluid intelligence (Gf) have been shown to decrease as one ages (Fisher, Chaffee, Tetrick, Davalos, & Potter, 2017), though—interestingly—recent research has demonstrated that fluid intelligence has been steadily increasing across generations (Schaie, Willis, & Pennak, 2005). Middle-age individuals experience a period of steadily improving crystallized intelligence (Gc) and declining fluid intelligence (Lachman, Teshale, & Agrigoroaei, 2015) before crystallized intelligence peaks at older ages (Salthouse, 2010). There are also negative consequences that accompany aging related to the ability to recover to optimal performance following injury (Maertens, Putter, Chen, Diehl, & Huang, 2012). It is important to note that while age is generally negatively related to health, the most serious threats to health occur after the age of 75, when most individuals have exited the workforce (Pinquart, 2001), and that there is substantial variation in terms of aging-related declines between individuals (Maertens et al., 2012). Thus, aging is associated with many declines and changes in abilities that may require accommodation in the workplace.
Accommodating older workers in the workforce is often associated with functional age as addressing age diversity often means addressing the physiological and psychological needs that older workers may have in the workplace. Jobs requiring physical activity may become more strenuous for older workers as well (Hedge, Borman, & Lammlein, 2006). Sharit and Czaja (2012) noted several adjustments to the workplace that can be made to limit the impact on older workers, such as reducing glare and noise, providing the opportunity to take notes, avoiding repetitive motion, providing flexible workstations that allow for standing and sitting, and allowing for breaks. Aging is associated with declines in perceptual abilities, such as the ability to see small text clearly. Proper lighting becomes increasingly important for older workers (Nylén, Favero, Glimne, Teär Fahnehjelm, & Eklund, 2014). Gonzalez and Morer (2016), for example, describe numerous adjustments that can be made to traditional workstations in knowledge-based jobs. One study of pilots found that younger pilots performed better than older pilots and that the work experience of older pilots did not mitigate this difference. They did note, however, that allowing the older pilots to take notes eliminated this difference (Morrow et al., 2003). Thus, modifying the work and training environment for older workers seems to reduce the impact of functional age on work and training performance.
Much of the research around selection, optimization, and compensation strategies reflects individuals’ attempts to cope with declining resources in older age. The model suggests that successful aging involves selecting goals to pursue (and abandoning goals that should no longer be pursued), gathering resources to optimize goals that are being pursued, and finding methods to compensate for age-related declines. These practices often begin in middle age, where individuals first encounter biological declines associated with age and must adapt to their capabilities by either selecting new goals or compensating for losses (Lachman et al., 2015). Older workers, for example, may develop methods of lifting or moving objects that protect themselves from injury or use tools to mitigate the harm of strenuous labor (Sanders & McCready, 2009).
Functional age also relates to emotional regulation. While chronological age is often associated with declines, physiological changes such as slowing of the autonomous nervous system and reduced activity in the amygdala may actually lead to advantages for older individuals (Scheiebe & Zacher, 2013). These gains in emotional regulation may explain why older adults are less likely to experience emotional burnout and interpersonal conflict at work (Ng & Feldman, 2010). Age is also associated with the use of more effective emotional regulation strategies, which leads to greater job satisfaction for older workers (Cheung & Tang, 2010).
Functional age has received a great deal of attention in the training literature. Given that older adults experience declining levels of Gc, older learners are frequently presumed to be less effective in training (Maurer & Rafuse, 2001). Indeed, in a large meta-analysis, Ng and Feldman (2012) noted a small but negative association between age and training performance. However, there is some evidence that training can be adapted to promote the performance of older adults. Beier and Ackerman (2005), for example, found that extending the time allowed for training improved knowledge acquisition and training performance in older adults. They also noted that linking trained information to previously learned information made the acquisition of knowledge less dependent on fluid intelligence. Likewise, providing structure and encouraging exploration in training has also been shown to benefit older trainees (Carter & Beier, 2010; see also Beier, Teachout, & Cox, 2012). Other researchers have found that offering meta-cognitive prompts (e.g., mini-quizzes to test for learning), structure, and adaptive guidance can greatly improve the performance of older adults in technology training specifically (Wolfson, Cavanagh, & Kraiger, 2014).
To promote a climate of age diversity, however, it is important to consider selection as well. Older adults may face discrimination in selection methods as a consequence of functional age. Researchers have found that individuals who appear older receive lower hirability ratings than younger looking applicants because they are perceived to be less fit (Kaufmann, Krings, Zebrowitz, & Sczesny, 2017). Declines in cognitive function may lead to adverse impact against older individuals as well (see Fisher, Truxillo, Finkelstein, & Wallace, 2017). There is also the potential for younger workers to perform less well on common selection methods such as conscientiousness. In a meta-analytic analysis, Roberts, Walton, and Viechtbauer (2006) found that individuals tend to improve on measures of extraversion, conscientiousness, and emotional stability over the course of their life span. As conscientiousness is commonly used in selection, it may be that younger individuals may be disadvantaged when this trait is used to predict performance (Fisher et al., 2017).
Psychosocial or Subjective Age
Psychosocial age (or subjective age) refers to the both how one identifies with ones’ age as well as how one is perceived as a function of age. Subjective age specifically reflects that individuals vary in terms of how old they feel, think, and act relative to others with whom they interact (Kooij et al., 2007). Individuals experience a myriad of physiological changes associated with age, and both the experience and the anticipation of these changes lead individuals to adjust their views of themselves, their goals, and their relationships (Zacher, 2015).
Psychosocial age also includes self-stereotypes about aging, that is, the changes in self-perception that accompany one’s aging. Self-stereotypes about aging have many potential negative consequences for older individuals. Specifically, individuals with negative attitudes toward aging are more likely to experience negative health outcomes and experience greater loss in physical abilities (Levy, Slade, & Kasl, 2002). Negative attitudes toward aging are positively associated with negative health-related behaviors such as tobacco use and excessive drinking and negatively associated with positive health-related behaviors like visiting the doctor or maintaining a healthy diet (Levy & Myers, 2004). One study found, for example, that negative attitudes toward aging held among individuals in their thirties predicted the likelihood of a cardiovascular event by the time the individual turned sixty (Levy, Zonderman, Slade, & Ferrucci, 2009). There is also some evidence that negative attitudes toward aging are associated with reduced social contact and participation in cognitively stimulating activities (Robertson & Kenny, 2016). Further, these self-stereotypes may also impact employment opportunities for older workers. In one recent meta-analysis, Wanberg, Kanfer, Hamann, and Zhang (2016) found that the relationship between age and employment status was partially mediated by self-efficacy and intention to seek re-employment. The researchers interpreted their findings as reflecting the idea that older individuals do not see the benefit of exerting much effort at their age as finding employment is unlikely. Psychosocial age, again, affects the extent to which individuals pursue employment opportunities (Kooij, Jansen, de Lange, & Dikkers, 2007). Interventions designed to promote self-efficacy for older workers may improve their ability to be rehired.
Self-perception of age is also related to one’s perception of time. Socio-emotional selectivity theory (SST) argues that older individuals and younger individuals view time differently, with older individuals focusing on enjoying what time is left while younger individuals focus on time since birth (Carstensen, 1991). The theory postulates that older individuals focus more on positive emotional experiences than younger people do while younger individuals value novel experiences more than older individuals do, which has been supported by numerous studies (Carstensen & Turk-Charles, 1994; Carstensen, Isaacowitz, & Charles, 1999; Carstensen, 2006). This theory has important implications for the workplace as well. Wang, Burlacu, Truxillo, James, and Yao (2015) found that feedback quality—the extent to which performance feedback is delivered in a respectful and supportive manner—predicted whether older employees responded favorably to the feedback, whereas feedback quality—the extent to which feedback was useful—predicted feedback favorability for younger workers. That is, older workers responded more favorably to respectful (i.e., emotionally satisfying) feedback while younger employees responded more favorably to informative feedback.
Self-stereotypes about aging also impact training performance. Indeed, Maurer (2001) has argued that individuals lose confidence in their ability to learn as they age, leading to less participation in work-related training. Researchers have also found that older workers’ entity beliefs—the beliefs that their abilities are fixed as a function of age—predicted their willingness to participate in training. Interestingly, one study found that more positive attitudes toward aging also predicted the use of technology to compensate for age related declines (Sun, McLaughlin, & Cody, 2016).
Given the abundance of methods to conceptualize age, it is not a surprise that there is also a myriad of examples of age discrimination in the workplace. Surveys consistently find that age discrimination against older individuals is common; the AARP recently conducted a survey of working adults over 45 and found that 61% reported experiencing or witnessing age discrimination, while 91% reported that age discrimination was common (Terrell, 2018). One large meta-analysis found that age was a strong predictor of re-employment after being fired from a position. Older individuals also take longer to find jobs after being fired, receive fewer job offers, and are less likely to find a new job after being terminated (Wanberg et al., 2016).
Several meta-analyses have concluded that discrimination against older workers is pervasive across a variety of industries (Bal, Reiss, Rudolph, & Baltes, 2011; Gordon & Arvey, 2004; Kite & Johnson, 1988). Finkelstein, Burke, and Raju (1995) found evidence that younger workers received higher ratings than older workers in simulated settings. Interestingly, younger raters demonstrated an ingroup bias in favor of younger workers, but older workers did not demonstrate a similar ingroup bias. Likewise, raters favored younger workers for jobs stereotyped as more appropriate for younger workers but did not prefer older workers for jobs stereotyped as more appropriate for older workers. More recently, Bal et al. (2011) found that younger workers were evaluated more positively and given more positive ratings for interpersonal abilities. Among the most persistent stereotypes about older workers is the perception that they are unable to learn. Maurer, Wrenn, and Weiss (2003) and Wrenn and Maurer (2004) found that negative attitudes about aging (i.e., associating aging with loss in abilities) predicted negative attitudes about older adults’ ability to learn.
Researchers have also identified several stereotypes about midlife that are not confirmed by data (Lachman et al., 2015). Middle adulthood is often portrayed as a period of stress and crisis as individuals cope with issues related to responsibility and mortality (Lachman, 2004). Conversely, middle age is also portrayed as being the best years of life in which individuals have reached the peak of their careers and abilities (Almeida & Horn, 2004). In reality, longitudinal survey data reveals that individuals in midlife are not more likely to experience a stressful life event than they are at other periods of life (Lachman et al., 2015).
Age discrimination against younger individuals is also increasingly common. In one large survey of European countries, Bratt, Abrams, Swift, Vauclair, and Marques (2018) found that younger individuals experienced the highest level of age discrimination, though some countries actually demonstrated a U-shaped pattern, with the oldest and youngest respondents reporting the highest levels of age discrimination. In their meta-analysis, Bal et al. (2011) also found that younger workers were perceived to be less reliable than older workers. This suggests that organizations should be encouraged to focus on the actual job-related knowledge, skills, abilities, and other characteristics of their employees; and not focus on their ages—either younger or older.
Further, there is increasing evidence of the negative effects of age stereotypes on younger workers in the recent research on metastereotypes, the perceptions that individuals have that others hold stereotyped views toward members of a group to which the individuals belong (Finkelstein, King, & Voyles, 2015). Researchers have found that one group who may be most vulnerable to the effects of metastereotypes are younger employees, as researchers have found that many stereotypes about younger people are negative and younger people are aware that others hold these stereotypes (Finkelstein, Ryan, & King, 2013). Indeed, one study found that the extent to which younger employees believed they were being stereotyped—that is, their awareness of negative metastereotypes—led to more negative attitudes toward their older coworkers. Thus, younger workers may be negatively affected by both the negative stereotypes others hold and their awareness of the negative stereotypes others hold.
One of the most common examples of psychosocial age is generational labels used to categorize members of different generations. These labels are used to describe individuals born between particular years and reaching maturity during specific historical events. Terms like Generation X, Generation Y, and Millennials are often used to describe these groups, though only the Baby Boomers (those born between 1945 and 1965) are officially defined by the U.S. government. These labels emerged from research by Mannheim (1952) exploring differences between cohorts due to shared historical experiences with cohorts. The theory has enjoyed a great deal of acceptance in academic research and in the popular press. Generational membership has also informed other theories of age-related differences; for example, generational identity is also similar to Joshi, Dencker, Franz, and Martocchio’s (2010) term age-based generational identity, which explores age differences due to different experiences of historical events.
Generational theory has been popular for a number of years, though recently psychologists have started to question the validity of the generational labels. A recent article by Costanza and Finkelstein (2015) noted several specific concerns with generation cohort theory. First, different authors have historically used different years to denote generational membership leading to inconsistent definitions between researchers. Second, much as there is variation in definition, there is also variation in the names applied to the different generations. Third, the authors argue, the theory to explain how the experience of a historical event leads to consistent differences between generations has yet to be fully developed (Parry & Urwin, 2011). Relatedly, and perhaps most importantly, few substantial differences between cohorts have been consistently observed in the workplace (Costanza, Badger, Fraser, Severt, & Gade, 2012; Lyons & Kuron, 2014). Thus, the validity of generational differences has recently come into some doubt.
Pew researchers recently surveyed members of different generations to explore their perceptions of their own generation. When describing their own generation (e.g., when Baby Boomers describe Baby Boomers), Millennials described their generation as less hard-working and responsible and more self-absorbed and greedy than other generations. This echoes research by Perry, Hanvongse, and Casoinic (2013) that found that Millennials are often described as being entitled and arrogant in the popular press. Moreover, these descriptions were specific to generational membership; that is, while Millennials are frequently described in both positive (e.g., “tech-savvy”) and negative (e.g., “impatient”) ways, “younger employees” generally are mostly described in positive terms in the popular press. This suggests that Millennials may be viewed uniquely negatively and may explain why Millennials are less likely than other generations to embrace their generational label (Pew Research Center, 2015).
In their discussion of generational labels, Costanza and Finkelstein (2015) noted that the use of generational labels may lead individuals to discriminate against older workers. In fact, generational labels are frequently discussed as valid predictors of differences in motivation, performance, and potential. They argue that anti-discrimination rules and laws prevent discrimination based on age but that there is no similar legislation based on generational label. Indeed, research indicates that attitudes toward older workers is predicted by the perception that older workers are protected by anti-discrimination legislation (Cox & Barron, 2012). In fact, one study using an inbox task found that job candidates described as “older workers” received more favorable ratings than job candidates described as “Baby Boomers.” This would suggest that even the label Baby Boomer, which Baby Boomers embrace more than Millennials embrace the term Millennial (Pew Research Center, 2015), could increase discrimination against workers in that cohort.
Much of the research on psychosocial and subjective age has focused on improving attitudes about older workers. Improving stereotypes about different ages may be accomplished by developing intergenerational teams. Intergroup contact theory suggests that intrateam contact can improve attitudes toward stereotyped groups (Pettigrew & Tropp, 2006). Indeed, at least one study has found that intergroup contact between generations promotes more positive attitudes through the creation of a shared dual-identity; that is, intergroup contact was successful when participants saw themselves both as members of their generation and members of their organization (Iweins, Desmette, Yzerbyt, & Stinglhamber, 2013). Interestingly, there is also some recent research indicating that age-inclusive HR policies can dispel negative age stereotypes (Kunze, Boehm, & Bruch, 2013). Further, Kunze, Raes, and Bruch (2015) found that both inclusive HR policies and the belief that one’s work was meaningful were both associated with lower subjective age ratings. This suggests that increasing the meaning of work—by increasing job autonomy, increasing responsibilities, or increasing the variety of skills utilized, for example—may actually reduce employee’s self-reported subjective age (for a full discussion of how job design may promote more positive age outcomes, see Barnes-Farrell & Piotrowski, 1991).
Sterns and Doverspike’s (1989) concept of organizational age refers to the length of time an individual has been working in a particular job in a particular organization. Though organizational age and tenure are not often researched, there is evidence that they predict important organizational outcomes; in a large meta-analysis, Ng and Feldman (2013) found that organizational tenure and age were generally not related to innovation in organizations. More recently, Joshi et al. (2010) expanded the concept of organizational age into a broader model of organizational cohorts and generational identity. Specifically, those researchers argued that age can be viewed in terms of cohort-based generational identity, referring to the cohort with which one entered an organization. They also argued for an incumbency-based generational identity, which reflects the expertise and knowledge associated with being in a job for a particular period of time. Thus, an employee’s organizational age can be reflected both in how long an individual has occupied a particular job or how long the individual has been part of an organization.
Organizational age diversity can also reflect individual differences in career timetables. Career timetables refer to the extent to which individuals are accomplishing career goals at expected rates (Lawrence, 1988). Within organizations, the theory argues, established norms exist at which individuals will reach particularly career milestones. Tsui, Porter, and Egan (2002), for example, found that when subordinates are older than their supervisors, they receive lower ratings than when supervisors are the same age or older than the subordinates. They suggested that individuals who are perceived to be achieving career objectives slower—and thus transgressing organizational norms related to age—may experience more discrimination. Indeed, one study demonstrated that working with younger employees reduced older employees organizational self-esteem (i.e., how valued they felt at the organization), which lowered their own altruism, while the opposite effect was found with older workers. The authors suggest that younger workers may value the chance to work with older workers who are further on their career timetable while older workers may feel working with younger employees reflects that they are not as far along on the career schedule (Chattopadhyay, 1999). Shore et al. (2003) found similar results in their exploration of age-mismatch in manager and subordinate dyads; younger managers consistently rated older subordinates lower than younger subordinates in terms of performance, potential, and promotability. These findings may reinforce the idea that organizations have age norms that set expectations for when individuals will accomplish particular goals (Lawrence, 1996).
Organizational age may be particularly important when organizations experience change. However, research needs to carefully distinguish the effects of worker age and experience. One recent study found that age was negatively associated with performance in three different organizations after (but not before) the organizations experienced large-scale changes. Specifically, one organization adopted a new technology platform and changed their work processes, and the other adopted new software. In each of these organizations, age was negatively associated with performance after the change. However, the negative association between age and performance was mediated by organizational tenure, leading the researchers to suggest that experience at the organization (i.e., as reflected by greater organizational age) may lead to difficulty in learning new methods of working (Niessen, Swarowsky, & Leiz, 2010). This may occur because older workers with more experience must also unlearn past methods, whereas younger workers do not. This difficulty in learning new methods of performing tasks may be reflected in the observation that older learners seem to perform less well in organizational training programs (Ng & Feldman, 2012).
Overall, organizational age has a great deal of potential to promote age diversity. Understanding employees in terms of their tenure in their jobs and in the organization can expand understanding of their needs and motivations (Joshi et al., 2010). As individuals reach career milestones, their motivation and goals may change (Lawrence, 1988). Further, employees who fail to reach these milestones may be viewed negatively, particularly by individuals who do reach these milestones. Finally organizational age may help predict which employees may struggle with organizational change as new processes and procedures must be learned over previously trained information (Niessen et al., 2010).
The life-span theory of age approaches age from the perspective of motivations, obligations, and career activities associated with particular stages of life. This perspective originated with research from Super (1957, 1990) who identified five life stages: growth, exploration, establishment, maintenance, and decline. Each of these stages may bring different work-related concerns; Wang and Wanberg (2017), for example, argued that adults are focused on training and development during the first stages and retirement during the final stage. Levinson (1981) also developed a theory of life-span development that conceptualized career stages as requiring a balance between career and family life with transitional periods between stages of development. Haynie and Shepherd (2011) expanded this theory to explore the transitions military veterans make between military life and civilian life, particularly after traumatic injury. Maurer and London (2018) similarly noted the importance of the transition periods between stages and argued that individuals experience role identity shift between stages that makes individuals more capable of and interested in learning and development.
Much of the organizational research utilizing life-span theory has focused on retirement decisions, with researchers noting that retirement can be a positive, negative, or neutral experience depending on how the retirement experience is structured (Wang & Wanberg, 2017). Wang (2007) found that employees who held a bridge job, planned for retirement, and were married to someone not working were more likely to retire successfully. Wang, Zhan, Liu, and Schultz (2008) discussed three types of bridge employment, with individuals either retiring, choosing a new career, or selecting a reduced role in their current field. Talaga and Beehr (1995) noted that the predictors of retirement vary by gender, with women more likely to retire and men less likely to retire if their spouse was in poor health. Examining retirement decisions from life-span theory’s framework of stages and transitions has informed an increasing amount of research into this important area.
Other researchers have noted that different ages are associated with different responsibilities and stressors. Younger employees are often focused on both starting their careers and starting their families (Scheibe & Zacher, 2013). Younger employees may also experience more job insecurity at work than older adults (Mauno, Ruokolainen, & Kinnunen, 2013). Young and middle-age adults also report experiencing fewer stressful events than older individuals (Almeida & Horn, 2004; Stawski, Sliwinski, Almeida, & Smyth, 2008). Middle-age individuals experience greater amounts of individual stressors but also experience higher levels of control over these stressors than they do at other periods in life (Heckhausen, Wrosch, & Schulz, 2010). Zacher and Winter (2011) noted that middle age often involves caring for both children and older parents, which may explain why these individuals often report more stress than older and younger individuals (Rauschenbach & Hertel, 2011). Senior employees may be freer from family responsibilities and thus be able to experience renewed freedom and explore retirement (Wang & Shultz, 2010). Increased experience later in life may also lead to greater abilities to regulate emotions and identify emotions in others accurately (Schiebe & Zacher, 2013). Indeed, research suggests that older individuals use more effective and less taxing emotional-regulation strategies such as deep acting and cognitive reappraisal (John & Gross, 2004; Cheung & Tang, 2010). Researchers have suggested that the increased ability to proactively regulate emotions by focusing on avoiding stressful encounters and seeking positive ones may explain why job autonomy and control are sought by older employees (Kooij et al., 2011; Schiebe & Zacher, 2013).
Overall, life-span diversity suggests that individuals experience stages of life with various responsibilities before moving through transitional periods of change and growth (Wang & Wanberg, 2017). Moving through these life stages and learning to cope with different stressors may be the reason why older adults are more capable of emotional regulation. Effective management of life-span diversity may involve recognizing and understanding the different stressors associated with different life-span stages. Younger ages appear to be more associated with career stress while middle age is more associated with stress from caring for family. Research suggests that organizational support can mitigate the experience of stress from family care impacting job performance (Zacher & Winter, 2011). Older employees, who are better able to manage workload stress, may also benefit from increased control and autonomy in their work.
Intersections of Age Identities
One interesting implication of understanding age from different perspectives is that examples of these identities interacting soon become apparent. For example, researchers have found that employees deal with the losses associated with their functional age by utilizing technology to compensate for these losses (i.e., the selection, optimization, and compensation methods). However, adults with a more positive view of aging will be more likely to feel comfortable utilizing technology (Sun et al., 2016). Thus, subjective age may impact the methods by which employees compensate for deficits related to relative age.
Researchers have also suggested that selective optimization strategies that employees may use to compensate for functional age losses can be predicted by life-span theories. Heckhausen et al. (2010) suggested that adults will rely less on strategies focused on control as they age as most individuals view the losses that accompany age as uncontrollable and, instead, will disengage from goals that seem unobtainable. Thus, functional age may impact the methods that individuals choose in order to maintain strong job performance across their life spans.
Table 1. Summary of Age Conceptualizations, Potential Management Issues, and Possible Solutions
Definition of Age
Chronological age: age as reflected by number of years lived
Functional age: age as reflected in one’s physical and mental capacities
Psychosocial/Subjective age: age as reflected in one’s self-image related to age
Organizational age: age as reflected in one’s organizational tenure
Life-span age: age as reflected in one’s stage of life
Managing age diversity requires appreciating age as a multidimensional construct. Table 1 provides a summary of age conceptualizations, management issues, and possible solutions. Successful managers will need to appreciate the variety of ways age impacts the members of the workforce and understand that a chronologically older employee may need some accommodations in training (due to functional age) but be motivated to remain in the workforce for some time still (as a function of life-span age; for a more detailed discussion, see Segers, Inceoglu, & Finkelstein, 2014). Chronological age itself can impact intergroup relations, though there has been very little research examining whether these effects can be better accounted for by other measures of age.
With an increased awareness of the multidimensional nature of age, employers can consider useful adaptations to their recruiting, selection, and training programs. For example, recruitment programs should consider going beyond sex and race diversity to include age diversity. When hiring workers, consideration should be given to the potential advantages of making sure that the composition of the workforce includes age diversity. Also, training programs should consider the inclusion of workers of different ages but also consider designing training programs to include individual trainee needs that may differ, e.g., based on their need to unlearn previous routines if they have more experience.
Awareness of functional age involves understanding the psychological and physiological consequences of aging, including a decline in perceptual, cognitive, and physical abilities along with increases in conscientiousness and emotional regulation. The impact of psychosocial and subjective age are primarily focused both on self and other stereotypes of aging, which can negatively impact individuals’ health and relations with others. Dispelling invalid stereotypes may be accomplished through inclusive HR practices, the use of intergenerational teams, and providing meaningful work to all employees. Organizational age and tenure may impact employees’ abilities to change and adopt new practices. Finally, life-span theory may help managers recognize the changing nature of responsibilities and motivations employees have across their life span, as well as recognizing moments of transition between which employees may be more open to change and development.
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