research paper generational differences

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Generations and Generational Differences: Debunking Myths in Organizational Science and Practice and Paving New Paths Forward

Cort w. rudolph.

1 Department of Psychology, Saint Louis University, St. Louis, MO USA

Rachel S. Rauvola

2 Department of Psychology, DePaul University, Chicago, IL USA

David P. Costanza

3 Department of Organizational Sciences & Communication, The George Washington University, Washington, D.C., USA

Hannes Zacher

4 Institute of Psychology – Wilhelm Wundt, Leipzig University, Leipzig, Germany

Talk about generations is everywhere and particularly so in organizational science and practice. Recognizing and exploring the ubiquity of generations is important, especially because evidence for their existence is, at best, scant. In this article, we aim to achieve two goals that are targeted at answering the broad question: “What accounts for the ubiquity of generations despite a lack of evidence for their existence and impact?” First, we explore and “bust” ten common myths about the science and practice of generations and generational differences. Second, with these debunked myths as a backdrop, we focus on two alternative and complementary frameworks—the social constructionist perspective and the lifespan development perspective—with promise for changing the way we think about age, aging, and generations at work. We argue that the social constructionist perspective offers important opportunities for understanding the persistence and pervasiveness of generations and that, as an alternative to studying generations, the lifespan perspective represents a better model for understanding how age operates and development unfolds at work. Overall, we urge stakeholders in organizational science and practice (e.g., students, researchers, consultants, managers) to adopt more nuanced perspectives grounded in these models, rather than a generational perspective, to understand the influence of age and aging at work.

People commonly talk about generations and like to make distinctions between them. Purported differences between generations have been blamed for everything from declining interest in baseball (Keeley, 2016 ) to changing patterns of processed cheese consumption (Mulvany & Patton, 2018 ). In the workplace, generations and generational differences have been credited for everything from declining levels of work ethic (e.g., Cenkus, 2017 ; cf. Zabel, Biermeier-Hanson, Baltes, Early, & Shepard, 2017 ), to higher rates of “job-hopping” (e.g., Adkins, 2016 ; cf. Costanza, Badger, Fraser, Severt, & Gade, 2012 ). Despite their ubiquity, a consensus is coalescing across multiple literatures that suggests that all the attention garnered by generations and generational differences (e.g., Lyons & Kuron, 2014 ; Twenge, 2010 ) has been “much ado about nothing” (see Rudolph, Rauvola, & Zacher, 2018 ; Rudolph & Zacher, 2017 ). That is to say, the theoretical assumptions upon which generational research is based have been questioned and there is little empirical evidence that generations exist, that people can be reliably classified into generational groups, and, importantly, that there are demonstrable differences between such groups that manifest and affect various work-related processes (Heyns, Eldermire, & Howard, 2019 ; Jauregui, Watsjold, Welsh, Ilgen, & Robins, 2020 ; Okros, 2020 ; Rudolph & Zacher, 2018 ; Stassen, Anseel, & Levecque, 2016 ). Indeed, a recent consensus study published by the National Academies of Sciences, Engineering, and Medicine (NASEM) concluded that “Categorizing workers with generational labels like ‘baby boomer’ or ‘millennial’ to define their needs and behaviors is not supported by research, and cannot adequately inform workforce management decisions…” (NASEM, 2020a ; see also NASEM, 2020b ).

Of equal importance to the theoretical limitations, common research methodologies used to study generations cannot unambiguously identify the unique effects of generations from other time-bound sources of variation (i.e., chronological age and contemporaneous period effects). Given all of this, some have argued that there has never actually been a study of generations (Rudolph & Zacher, 2018 ), and thus, the entire body of empirical evidence regarding generations is, to a large extent, wrong. Still, it is easy to find examples of empirical research that claim to find evidence in favor of generational differences (e.g., Dries, Pepermans, & De Kerpel, 2008 ; Twenge & Campbell, 2008 ; Twenge, 2000 ; see Costanza et al., 2012 , for a review) and theoretical advancements that aim to direct such empirical inquiries (e.g., Dencker, Joshi, & Martocchio, 2008 ). Moreover, some see generations as a useful heuristic in the process of social sensemaking: generations are recognized as social constructions, which help give meaning to the complexities and intricacies of aging and human development in the context of changing societies (e.g., Campbell, Twenge, & Campbell, 2017 ; Lyons, Urick, Kuron, & Schweitzer, 2015 ).

Considering all of this, we are faced with a variety of competing and contradictory issues when trying to sort out what bearing, if any, generations have on organizational science and practice. On the one hand, evidence for the existence of generations and generational differences is limited. On the other hand, the idea of generations is pervasive and is used to explain myriad patterns of thinking, feeling, and behaving that we observe day-to-day, especially in the workplace. Thus, there exists a tension between what science “says” about generations and what people “do” with the idea of generations. Given this, the continued popularity of generations as a means of understanding work-related processes is worthy of closer investigation. This popularity begs the question, “What accounts for the ubiquity of generations, despite a lack of evidence for their existence and impact?” This manuscript explores two answers to this question.

One answer to this question is a lack of knowledge about what the science of generations tells us, leading to misunderstandings of the evidence about generations, their existence, and their purported impact. Thus, the first goal of this article will be to review and debunk ten common myths about generations and generational differences at work and beyond. A second answer to this question is a lack of knowledge regarding, and exposure to, alternative theoretical explanations for understanding (a) the role of age and aging at work and (b) the persistence of generations as a tool for social sensemaking. More specifically, we argue that, owing to a lack of knowledge about alternative explanations and supported by their ubiquity and popular acceptance (e.g., in the popular business and management press; see Howe & Strauss, 2007 ; Knight, 2014 ; Shaw, 2013 ), generations are more often than not the “default” mode of explanation for complex age-related phenomena observed in the workplace and beyond (e.g., because they are familiar and comfortable explanations, which are easy to adopt, and seem legitimate on their face).

Accordingly, the second goal of this paper is to further advance two alternative models for understanding age and aging at work that do not rely on generational explanations and that can explain their existence and popularity—the social constructionist perspective and the lifespan development perspective. This is an important contribution, because simply pointing out the obvious pitfalls of generations and generational explanations can only go so far toward changing the way that people think about, talk about, study, and enact practices that involve generations. Just advising people to drop the idea of generations without providing alternative models would be counterproductive to the goal of enhancing the credibility of organizational science and practice. Thus, our hope is that by providing workable alternatives to generations, researchers and practitioners will be encouraged to think more carefully about the role of age and the process of aging when enacting the work that they do.

The social constructionist perspective offers that generations and differences between them are constructed through both the ubiquity of generational stereotypes and the socially accepted nature of applying such labels to describe people of different ages (e.g., consider the recent “OK Boomer” meme; Hirsch, 2020 ). The social constructionist perspective helps address and explain the question of why generations are so ubiquitous. Complementing this, the lifespan perspective is a well-established alternative to thinking about the process of aging and development that does not require one to think in terms of generations. The lifespan perspective frames human development as a lifelong process which is affected by various influences—not including generations—that predict developmental outcomes. Despite its longstanding role in research on aging at work (e.g., Baltes, Rudolph, & Zacher, 2019 ), the lifespan perspective has been infrequently considered as an alternative model to generations, perhaps because it has not often been treated in accessible terms.

These complementary approaches—the social constructionist and the lifespan development perspective—offer alternative paths forward for studying age and age-related processes at work that do not require a reliance on generational explanations. Thus, as described further below, these perspectives by-and-large circumvent the logical and methodological deficiencies of the generations perspective. They also offer actionable theoretical and practical guidance for identifying the complexities involved in understanding age and aging at work.

First, we outline and “bust” ten myths about generations and generational differences (see Table ​ Table1 1 for a summary). These myths were chosen in particular, because we deemed them to be the most pressing for research and practice in the organizational sciences, broadly defined, in that they reflect commonly highlighted topics, and bear potential risks if not properly addressed. Then, we introduce and outline the core tenets of the social constructionist and lifespan development perspectives, giving examples of how their applications can complement each other in supplanting generational explanations in both science and practice. Finally, we conclude by drawing lines of integration between these two perspectives, in the hopes that these alternative ways of thinking will inspire researchers and practitioners to adopt alternatives to thinking about aging at work in generational terms.

Summary of ten myths about generations and generational differences

Debunking Ten Myths About Generations in Organizational Science and Practice

Myth #1: generational “theory” was meant to be tested.

The sheer number of empirical studies purporting to test generational “theory” would suggest that such theory was intended for testing. However, this is far from the case. The concept of generations as we know it stems from early functionalist sociological thought experiments, derived from foundational work by Mannheim (1927/ 1952 ) and others (e.g., Ortega y Gasset, 1933 ; see also Kertzer, 1983 ). Adopting the term in a largely historical, rather than familial or genealogical, sense, these authors offered “generations” as social units that account for broad societal and cultural change. Generations were suggested to emerge through “shared consciousness,” which developed across individuals (e.g., those at similar life stages) after common exposure to formative events (e.g., political shifts, war, disaster; see Ryder, 1965 ). This consciousness, in turn, was theorized to shape unique values, attitudes, and behaviors that characterize a given generation’s members, especially to distinguish one generation from its predecessor. These attributes subsequently impact how these individuals interact with and influence society.

Here, a tautology emerges: culture begets generations and generations beget culture. This is a potentially useful perspective for describing macro-scale interactions between social groups and the social environments in which they live—that is, it is useful as a functionalist sociological mechanism, as the concept of generations was intended. However, this perspective also implies that culture, and the generational groups it forms and is formed by, cannot be disentangled. Generational “theory” is not falsifiable, nor was it intended to be. Attempts to empirically study generations have extended these ideas into positivist and deterministic practices for which they were not intended. Even life course research (e.g., Elder, 1994 ), which centers on the impact of social change and forces on individuals’ lives as opposed to societal change, does not directly “test” for generational differences, per se. Instead, it uses generations conceptually in explicating the roles that historical, biological, and social time play in life trajectories.

In fact, Mannheim’s (1927/ 1952 ) work was partly a critique of the overemphasis on absolutist/biological perspectives in the study of social and historical development, including the objective treatment of time (Pilcher, 1994 ). This makes it all the more puzzling and problematic that generational “theory” has been applied to discrete quantitative increments (i.e., age and year ranges to define cohorts), and in a fashion that ignores the “non-contemporaneity of the contemporaneous” (i.e., the fact that being alive at the same time, or even being alive and of a similar age at the same time, does not mean history is experienced uniformly; Troll, 1970 , p. 201). When considering the roots of “generations,” it is apparent that the concept has been re-characterized and misappropriated.

Myth #2: Generational Explanations Are Obvious

One appealing, if overstated, quality of generations is that there are unique characteristics that are (assumed to be) associated with various cohorts. Moreover, it is assumed that lines can be drawn between generations to distinguish them from one another on the basis of such characteristics. These characteristics, which are said to be influenced by the various events that supposedly give rise to generations in the first place, “make sense” in a way that give generations an air of face validity. For example, it seems very rational and indeed quite self-evident to many that living through the Great Depression made the Silent Generation more conservative and risk-avoidant and that helicopter parents and the rise of social media made Millennials narcissistic and cynical. These and other observed social phenomena such as job-hopping and materialism are frequently ascribed to generations. However, looking more deeply into the identification of these critical events, as well as the mechanisms by which generations supposedly emerge, reveals a far more complex, nuanced picture than a generational explanation would have us believe.

In order to understand why the events that created generations may, or may not, have been impactful, it is important to understand how the critical events purported to give rise to them are identified. As one example, in their popular book, Strauss and Howe ( 1991 ) offer a taxonomy of generations, developed by tracing historical records in search of what they called “age-determined participation in epochal events…” (p. 32). To Strauss and Howe, such events were deemed to be so critical that they contributed to the creation of a unique generation. This post hoc historical demographic approach benefits from the passage of time: it is far easier to identify critical events retrospectively, rather than when they are actually occurring. Although major events like economic depressions and wars likely qualify as epochal, dozens of other events have been proposed to be critical in the formation of generations, only to fade into historical oblivion within a matter of a few years.

For example, in defining supposedly seminal events in the development of the Millennial generation, Howe and Strauss ( 2000 ) cite the case of “Baby Jessica” (n.b. on October 14, 1987, 18-month-old Jessica McClure Morales fell into a well in her aunt’s backyard in Midland, Texas. After 56 h, rescue workers eventually freed her from the 8-in. well casing 22 ft below the ground; Helling, 2017 ). Why this event should help form a generation is uncertain, as is whether or not Millennials were or have been systematically impacted by her saga and subsequent rescue.

Rather than being obviously generational, explanations for many social phenomena are more likely to be associated with age or period effects, both of which are other time-based sources of variation that are often conflated with generational cohorts. Specifically, there are three sources of time-based variation that need to be accounted for to make claims about generations: age, period, and cohort effects (see Glenn, 1976 , 2005 ). Age effects refer to variability due to time since birth, in that chronological age is simply an index of “life lived” (e.g., Wohlwill, 1970 ). Period effects refer to variability due to contemporaneous time and refer to the effects of a specific time and place (i.e., the year 2020). Finally, cohort effects are those that are typically taken as evidence for generations, referring to the year of one’s birth. To make claims about generations, therefore, it is necessary to rule out the effect of age (i.e., developmental influences) and period (i.e., contemporaneous contextual influences).

There are numerous examples of how these sources of variability are conflated and confused with one another. Consider that popular press accounts of Millennials have until recently painted them to be dedicated urban dwellers who favored ride-sharing services and eschewed traditional families (e.g., Barroso, Parker, & Bennet, 2020 ; Godfrey, 2016 ). However, adults in this age range have more recently been observed moving to the suburbs, buying houses and cars, and having children (e.g., Adamczyk, 2019 ). This is not a generational effect but rather a phenomenon attributable to the fact that Millennials are reaching the normative age where people get married, start families, and purchase houses. This is a product of age and context, not generation or period. The picture becomes even more complex given other contextual factors not necessarily bound to time, for example, when considering that the average age of first conception is higher in urban, compared to rural, areas (Bui & Miller, 2018 ).

Another example comes from data showing that high school and college students are less likely to hold summer jobs today than 20 years ago (Desilver, 2019 ). This is not a generational effect, but rather is attributable to contemporaneous economic conditions. As a final example, after 9/11, there was a modest increase in the number of people enlisting in the United States Army, which is an example of a period effect (Dao, 2011 ). However, this change has also been misattributed in various ways to a generational effect (e.g., Graff, 2019 ). Notably, in ~ 2019 (i.e., when those born in ~ 2001 turned ~ 18 and were eligible to join the army), there were historically low rates of enlistment (Goodkind, 2020 ). If this rate had been particularly high, one might conclude evidence for a generational effect, such that people born in 2001 grew up in a time and place that demanded enlistment. However, this is not the case—growing up in a post 9/11 world did not make this cohort more likely than others to join the army.

In summary, whereas certain historical events might be easily identifiable as epochal, the extent to which recent events are defined as such might not be known for some time. Moreover, this idea assumes that epochal events actually matter for the formation of distinct generations, a key argument in generations theory that is by-and-large untested, and indeed untestable. Moreover, consider that “global” events (i.e., those that affect all members of a population regardless of age, not just those born in a particular time and place, like a global pandemic) almost certainly manifest as period, not generational cohort effects (Rudolph & Zacher, 2020a , 2020b ). Generations and the events that are purported to give rise to them are far from obvious and to attribute current individual characteristics to the occurrence of specific events is misguided. Furthermore, many of the “obvious” generational effects often attributed to such events are much more likely due to other factors associated with age and/or period.

Myth #3: Generational Labels and Associated Age Ranges Are Agreed Upon

Whereas generational labels are well-known and widely recognized, the specific birth year ranges that define each generational grouping and the consistency with which such groupings are applied across time, studies, and location, vary substantially. For example, Smola and Sutton ( 2002 , p. 364) identified a great deal of variation in the start and end years that define different generational groups and the names used to describe various generations, noting “generations…labels and the years those labels represent are often inconsistent” (p. 364).

In their meta-analysis, Costanza et al. ( 2012 ) found similar discrepancies with variations in start and end dates ranging from 3 to 9 years depending on the study, the variables of interest, and the source of the generational year ranges being used. Similar conclusions were reached by Rudolph et al. ( 2018 ) in their review of generations in the leadership literature.

Beyond these definitional inconsistencies, there are notable differences in the way researchers address cross-cultural variability in generational research. The ubiquity of the labels and their pervasiveness in the literature has led researchers from countries other than the USA to use labels (e.g., “Baby Boomers”) when doing so does not make sense, as the events that supposedly influenced individuals and gave rise to these generations in the first place clearly differ from country to country. Moreover, consider that the term “Millennials” is not meaningful in countries that use Chinese, Islamic, Jewish, Buddhist, Sakka, or Kolla Varsham calendars (Deal, Altman, & Rogelberg, 2010 ) and that generations are often labeled based on political or cultural events and epochs. For instance, members of the Greek workforce have been categorized into the Divided Generation, the Metapolitefsi Generation, and the Europeanized Generation (Papavasileiou, 2017 ). In Israel, generations are identified by wars and thus have shorter ranges (Deal et al., 2010 ). The German media has variously labeled younger people as Generation C64, Generation Golf, or Generation Merkel. In China, generations are pragmatically called the Post-50s generation, Post-60s Generation, and so on, whereas in India, the three main generational groups are labeled Conservatives, Integrators, and Y2K (Srinivasan, 2012 ).

One approach researchers have adopted for dealing with the complexities of cross-cultural variation in generational labeling is to ignore the issue and simply use US-based generational labels and years when studying individuals in other countries. For example, Yigit and Aksay ( 2015 ) looked at Turkish Gen X and Gen Y health professionals, roughly using US date ranges for these groups. A second approach has been to use the date ranges associated with US generations but assign country-specific labels to those same periods. Utilizing this approach, Weiss and Zhang ( 2020 ) picked birth year ranges and adopted or developed generational labels in three different countries. For example, for the years 1946–1965, they labeled the generations as the “68er Generation” in Germany, “Baby Boomer” in the USA, and the “New China Generation” in China. A third approach has been to develop country-specific generational groups based on local events that impacted people in that county, a strategy used by To and Tam ( 2014 ) who identified four distinct post-WWII generations in China.

Inconsistencies in labeling have significant conceptual and computational implications for the study and understanding of generations and especially so if one wishes to conduct comparative cross-national and/or cross-cultural research. Importantly, we would argue that the validity of the generations concept and its utility for understanding individual, group, and organizational phenomena is very limited due to a number of factors, including (a) researchers’ inability to agree on the start and end dates for different generations; (b) inconsistencies in the classification and labeling systems that characterize them; (c) disagreement on the specific significant influencing events that supposedly gives rise to them, such as the extent to which the timing of events plays a role, including the length of time that is associated with their influence, and the lag required to observe such influences; and (d) the issue of cross-cultural equivalencies. As such, defining generations represents a moving target, which is a significant liability for science and evidence-based practice.

Myth #4: Generations Are Easy To Study

Although there have been numerous attempts to study generations and generational differences, it is clear that these phenomena have not been studied very well. Indeed, it is not only difficult to study generations as they have been framed in the literature but also impossible. As noted above, research on generations is typically based upon birth year ranges, which is to say that they are derived from information about birth cohorts. A common problem emerges when one tries to study cohort effects in cross-sectional (i.e., single time point) research designs, which are the most commonly applied designs used to make inferences about generations (see Costanza et al., 2012 ). Namely, age, period, and cohort effects are confounded with each other in such designs.

This confounding is best understood through an example. Let us assume that a hypothetical cross-sectional study is conducted in the year 2020 (i.e., the year constitutes the “period effect” in this case). If we reduce the logic of generations a bit and define a cohort effect in terms of a single birth year (e.g., those born in 1980), then the effect of age (i.e., time since birth; 40 years) is completely confounded with cohort. This is because:

In this example, any differences that researchers observe as a function of assumed cohort variability may instead be due to the age of the individuals when they were studied. This pattern would likewise be extrapolated to any age–cohort combinations studied in a single period. The linear dependency among the three effects means that unique effects of age cannot be separated from whatever cohort effect might exist and vice versa.

One common attempt to circumvent this confounding is to artificially group members of different cohorts together to form generational groups. However, this practice is likewise fraught with the same issues raised just above. Another hypothetical cross-sectional study helps to illustrate why: in this study, let us assume that we want to define two arbitrary groupings of birth cohorts, representing people born between 1981 and 1990 (“Generation A”) and those 1991–2000 (“Generation B”), to disentangle age and cohort effects from one another. The variability due to birth cohort in each generation is 10 years; however, as in our previous example, the age range within cohorts is likewise 10 years. Thus, this approach does little to solve the dependency other than shifting the scaling of age. As the rank order of cohort versus age has not changed (relatively older people are in “Generation A” and relatively younger people are in “Generation B”), there is still a correlation between age and generational groups in this study. Moreover, this approach has other limitations, including the loss of statistical power to detect age effects (see Rudolph, 2015 ) and a confusing logic of cohort versus age effect interpretations (e.g., the oldest members of “Generation A” are closer in age to the youngest members of “Generation B” than to the average age of their own generational group).

From a research design standpoint, this issue of confounding represents an unresolvable problem, which has long been known and lamented in the literature (e.g., Glenn, 1976 , 2005 ). Other research designs are unfortunately no better geared than cross-sectional designs to address this issue, or they do not address variability in cohort effects at all. For example, in typical longitudinal designs, cohort effects are held constant (i.e., from the first time point, people’s birth year does not vary) and period is allowed to vary (i.e., as data are collected from the same people across multiple time points). However, in such designs, period effects are conflated with age (i.e., as people “get older” across time). Expanded longitudinal approaches, such as cohort sequential designs (e.g., sampling 20-year-olds at each time point, T 1 − T k , adding successive cohorts of 20-year-olds at each time point) may be able to separate age/aging from period and cohort effects, depending on how “cohort” is defined. However, such studies require immense resources and time (e.g., 20+ years or more of data collection, including long-term data management and subject retention efforts; see Baltes & Mayer, 2001 ). As such, and perhaps not surprisingly, we are unaware of any applications of such designs to the study of generations at work.

An alternative that has been employed by some researchers (e.g., Twenge, Konrath, Foster, Campbell, & Bushman, 2008 ) is a cross-temporal approach, often employing time-lagged panels or cross-temporal meta-analyses (discussed further below). Cross-temporal approaches use data collections from members of different cohort groups, collected during different periods, holding age constant (e.g., data from panels of 25-year-olds and 50-year-olds collected in 2000, 2010, and 2020 or research done on college students every year from 1990 to the present). The logic of cross-temporal methods is to compare groups of similarly aged individuals (i.e., to “control” for age by holding its value constant) across time and then argue that cohort effects are more likely the cause of any observed differences than period effects. Among other issues, cross-temporal approaches have been criticized for their reliance on ecological correlations (i.e., correlations among variables that represent group means) and design assumptions (see Trzesniewski & Donnellan, 2010 ; Trzesniewski, Donnellan, & Robins, 2008 ) raising significant concerns about them as a way to study generations. Specifically, ecological correlations can misrepresent relationships when contrasted with correlations among individual observations (see Robinson, 1950 ).

Overall, the methodological and design challenges associated with studying generations are substantial and the conceptualization of generations as the intersection of age and period makes them impossible to study. Thus, studying generations is only “easy” to the extent that one is willing to ignore the issues raised here. Given these concerns, we echo the recommendations of Rudolph and Zacher ( 2017 ), who suggest that “…both research and practice would benefit from a moratorium on time-based operationalizations of generations as units for understanding complex dynamics in organizational behavior” (p. 125).

Myth #5: Statistical Models Can Help Disentangle Generational Differences

Given the design challenges noted above, it is perhaps not surprising that researchers have tried a variety of statistical techniques to resolve the age, period, and cohort confounding problem. Unfortunately, the great majority of generational studies to date have employed the least useful approach to doing so, pairing cross-sectional designs with comparisons of generational cohort means (e.g., typically via linear models, such as t tests or other variants of ANOVA-type models). As noted, cross-sectional approaches control for period effects but confound cohort and age effects with one another and this confounding cannot be resolved statistically through any means. To be clear, this is not a function of a lack of innovation regarding statistical modeling techniques. On the contrary, as long as age, period, and cohort are defined in time-related terms, they will be inextricably confounded with one another in cross-sectional research designs.

With respect to cross-temporal approaches, some researchers have implemented a specific technique referred to as “cross-temporal meta-analysis” (CTMA). CTMA shares certain features with traditional meta-analysis (e.g., studies assumed to be representative of a population of all possible studies on a given phenomenon are taken from the literature and synthesized). In a typical CTMA, age is more or less held constant by narrowing the sampling frame of studies included (e.g., by only considering studies of college age students). By holding age constant and looking at the effects of time on outcomes (i.e., by considering the relationship between year of publication and mean levels of a given phenomenon derived from contributing studies), CTMA models change over time in a phenomenon. However, although age is to some extent held constant, recall that cross-temporal methods inherently confound period and cohort effects with one another. Thus, any identified cohort effect cannot be unambiguously separated from period effects in CTMA. Although research employing CTMA has argued that generations are more likely than period effects to explain observed differences, such work also recognizes that period effects are equally likely explanations for any results derived therefrom (e.g., Twenge & Campbell, 2010 ). Furthermore, a recent paper by Rudolph, Costanza, Wright, and Zacher ( 2019 ) used Monte Carlo simulations to test the underlying assumptions of CTMA, finding that it may misestimate cohort effects by a factor of three to eight times, raising questions about both the source and magnitude of any differences identified.

A final analytic technique that has been occasionally employed to disentangle age, period, and cohort effects is cross-classified hierarchical linear modeling (CCHLM; Yang & Land, 2006 , 2013 ). Applying CCHLM to generational research, age is treated as a fixed effect and period and cohort are allowed to vary as random effects. Importantly, however, decisions about how such effects should be specified are somewhat arbitrary, because it is also possible that cohort and period could be fixed and age random in the population, resulting in different outcomes and conclusions from such models that are largely dependent on analytic decisions rather than reflecting “true” population effects. Thus, without generally unknowable insights into “what” to hold constant in estimating such models, CCHLM results in ambiguous parameter estimates for age, period, and cohort effects.

To this end, a series of simulation studies by Bell and colleagues (Bell & Jones, 2014 ; see also Bell & Jones, 2013 , for further commentaries) has shown that the Yang and Land methodology for separating age, period, and cohort effects simply does not “work.” Even ignoring this issue, CCHLM does little to solve the problem of age, period, and cohort confounding, because the three variables are still linearly dependent upon each other and hence computationally inseparable. Something (typically age) has to be held constant in such models to separate these variables from one another, and even then, ambiguities in how to interpret confounded effects of period and cohort still abound. In short, none of the statistical techniques that have been used to study generations can fully separate age, period, and cohort effects (see Costanza, Darrow, Yost, & Severt, 2017 , for a full discussion) and cannot solve the conceptual or design problems noted earlier. This known issue has befuddled social scientists for quite some time. For example, more than 40 years ago Glenn ( 1976 ) referred to this problem as “a futile quest.”

Myth #6: Generations Need To Be Managed at Work

Given the proliferation of research and popular press articles identifying generational differences, it is not surprising that practitioners and academics have suggested that people in different generations need to be managed differently at work (e.g., Baldonado, 2013 ; Lindquist, 2008 ). There are two main problems with these recommendations.

First, as has been noted, research generally does not and cannot support the existence of generational differences. Conceptual, theoretical, methodological, and statistical issues abound in this literature, and absent clear, convincing, and valid evidence for the existence of generational differences, there is no justification for managing individuals based on their supposed generational membership (NASEM, 2020a , 2020b ; Rudolph & Zacher, 2020c ). Eschewing the notion of generations does not mean that one must ignore that individuals change over the course of their lifespan or that their needs at different stages in their careers will vary. However, it is important to note that there is not a credible body of evidence to suggest that such changes are generational or that they should be managed as “generational differences” at work.

Indeed, as already noted, much of what lay people observe as “generational” at work is likely more accurately attributed to either age or career stage effects masquerading as generational differences. There is a broad and well-supported literature on best practices for HR, leadership, and management (e.g., Kulik, 2004 ) and customizing policies and practices based on those recommendations rather than generational stereotypes makes much more sense. Furthermore, there is a burgeoning literature on the positive influence that age-tailored policies (e.g., age-inclusive human resource practices that foster employees’ knowledge, skills, and abilities, motivation, effort, and opportunities to contribute, irrespective of age) for building positive climates for aging at work and supporting worker productivity and well-being (see Böhm, Kunze, & Bruch, 2014 ; Rudolph & Zacher, 2020d ). For example, research suggests that workers of all ages benefit from flexible work policies that allow for autonomy in choosing the time and place where work is conducted (see Rudolph & Baltes, 2017 ).

Second, as alluded to earlier, management strategies that are based on generations have the potential to raise legal risks for organizations. For example, in the USA, provisions of The Civil Rights Act of 1964, the Age Discrimination in Employment Act of 1967, and the Americans with Disabilities Act of 1991 disallow the mistreatment of individuals from certain groups based on a variety of characteristics. Although generational membership is not directly covered by such legislation, under the ADEA, age is a protected class for workers aged 40+. Given the conflation of generational effects with age, life, and career stage, employment-related decisions tied to generations could be interpreted as prima facie evidence of age-related discrimination (e.g., Swinick, 2019 ). Indeed, organizations that market themselves to and build personnel practices around generations and generational differences have been implicated in age discrimination lawsuits (e.g., Rabin vs. PriceWaterhouseCoopers, 2017 ). Combined with the absence of valid studies supporting generationally based differences, organizations open themselves up to an unnecessary liability if they manage individuals based on generational membership (Costanza & Finkelstein, 2015 ; for a related discussion of various policy implications of managing generations, see Rudolph, Rauvola, Costanza, & Zacher, 2020 ).

Recently, Costanza, Finkelstein, Imose, and Ravid ( 2020 ) reviewed the applied psychology, HR, and management literatures looking for studies about how organizations should manage generations in the workplace. They identified a range of inappropriate inferences and unsupported practical recommendations and systematically refuted them based on legal, conceptual, practical, and theoretical grounds. We echo their conclusion here, regarding advice from managing based on generational membership (p. 27): “Instead of customizing HR policies and practices based on such [generational] differences, organizations could use information about their overall workforce and its characteristics to train recruiters, develop and refine policies, and offer customizable benefits packages that appeal to a broad range of employees, regardless of generation.”

That said, we do not think that the idea of generations should be ignored altogether in the development of management strategies. Instead, the focus should be shifted away from managing assumed differences between members of different generations and toward managing perceptions of generations and generational differences. Considering evidence that people’s beliefs and expectations about age and generations feed into the establishment of stereotypes that interfere with work-relevant processes (e.g., King et al., 2019 ; Perry, Hanvongse, & Casoinic, 2013 ; Raymer, Reed, Spiegel, & Purvanova, 2017 ; Van Rossem, 2019 ), this is a particularly important consideration and is, in and of itself, a topic worthy of further study.

Myth #7: Members of Younger Generations Are Disrupting Work

While it may feel “new” to blame members of younger generations for changes in the work environment, this is a form of uniqueness bias: we think our beliefs and experiences are new, when in reality similar complaints have been levied against relatively younger and older people for millennia. Indeed, generationalized beliefs about the inflexibility and “out of touch” nature of older generations, or the laziness, self-centeredness, and entitlement of younger generations, have repeated with remarkable consistency across recorded history (Rauvola, Rudolph, & Zacher, 2019 ). One of the more obvious examples is in referring to generations with self-referent terminology: New York Magazine wrote about youth in the so-called “Me” Decade (Wolfe, 1976 ) over 30 years prior to Twenge’s ( 2006 ) work on “Generation Me,” Time Magazine’s (Stein, 2013 ) publication on the “Me Me Me” generation, and even the British Army’s recent use of the phrase “Me Me Me Millennials…Your Army needs you and your self belief” in recruitment ads (Nicholls, 2019 ).

Lamentations about young people “killing things” are far from radical as well. Modern claims are made about youth ending an absurd number of facets of life, ranging from institutions such as marriage and patriotism to household products like napkins, bar soap, and “light” yogurt (Bryan, 2017 ). Moreover, similar concerns have been voiced throughout the years regarding the rise and fall of consumer preferences, including concerns about young people upending and revolutionizing romantic relationships and transportation (e.g., Thompson, 2016 ), or being corrupted by new forms of popular media like the radio in the 1930s (Schwartz, 2015 ).

A more realistic explanation exists for both shifts in consumer preferences as well as changes and disruptions in the nature of work: the contemporaneous environment, and innovations and unexpected changes therein. To take a recent example, the global COVID-19 pandemic has tremendously impacted and transformed how and where work is conducted (Kniffin et al., 2020 ; Rudolph et al., 2020 ). While “non-essential” workers are conducting more work virtually and with more flexible hours, other workers deemed “essential” are working in environments with new health and safety protocols and often with different demands and resources in place (e.g., with respect to physical equipment, coworker and customer contact). Even more workers have been furloughed or laid off altogether, with the need to turn to alternative forms of work to maintain income or, when feasible, resorting to early retirement (see Bui, Button, & Picciotti, 2020 ; Kanfer, Lyndgaard, & Tatel, 2020 ; van Dalen & Henkens, 2020 ).

These changes have led to a dramatic pivot for many organization, managers, and individual workers, far surpassing the speed and degree to which more gradual, “generational” workplace changes have supposedly occurred. Not only this, but such changes have had outcomes for workers and society that contradict what generational hypotheses would predict. For example, generational stereotypes suggest that relatively older workers would struggle with technological changes at work while relatively younger workers would thrive. However, the move to work-from-home arrangements has resulted in positive benefits for some, including helpful and flexible accommodations, or health and safety protections, as well as new challenges for others, such as the need to balance childcare or eldercare with work while at home, while still others face newfound isolation and lack of in-person social support coupled with great uncertainty (Alon, Doepke, Olmstead-Rumsey, & Tertilt, 2020 ; Douglas, Katikireddi, Taulbut, McKee, & McCartney, 2020 ). These changes create a diverse set of advantages and disadvantages for individuals of all ages. Rather than blaming those of younger generations for disrupting work and life more generally, societal trends and events are a more appropriate, fitting, and ultimately addressable explanation (i.e., through non-ageist interventions and policies).

Myth #8: Generations Explain the Changing Nature of Work (and Society)

Generations are an obvious and convenient explanation for the changing nature of work and societies. However, as discussed previously, convenience and breadth in applying generational explanations does not translate into validity. Because they can easily and generally be applied to explain age-related differences, generations give a convenient “wrapper” to the complexities of age and aging in dynamic environments (i.e., both within and outside of organizations). However, this wrapper restricts and obscures the complexities inherent to both individuals and the environments in which they operate. Generations are highly deterministic, suggesting that individuals “coming of age” at a particular time (i.e., members of the same cohort) all experience aging and development uniformly (i.e., cohort determinism; Walker, 1993 ). With so many other demonstrable age-related and person-specific factors (e.g., social identities, personality, socioeconomic status) that have bearing on individuals’ attitudes, values, and behaviors, as well as how these interact with contextual and environmental influences, the prospect of generations overriding all such explanations is implausible. Assuming otherwise wipes away a tremendous amount of potentially useful detail and heterogeneity.

Moreover, this perspective stipulates that events in a given time period impact younger people and not older people, such that historical context only influences individuals up to a certain (early) point in their development. This aligns with the idea that identity is “crystallized” or “ratified” at a certain age and development or change is more or less halted thereafter (Ryder, 1965 ). However, ample evidence suggests that this is far from the case, with age-graded dynamics in such areas as personality emerging across the breadth of the lifespan (e.g., Bianchi, 2014 ; Donnellan, Hill, & Roberts, 2015 ; Staudinger & Kunzmann, 2005 ) and alongside external forces (e.g., economic recessions). Our ability to dismiss crystallization claims is not merely empirical: although current methods and analyses used cannot fully disentangle age from cohort, lifespan development theory promotes the ideas of lifelong development, multiple intervening life influences, and individuals’ agency in shaping their identity and context (e.g., Baltes, 1987 ). Accordingly, it is more rational and defensible to suggest that individuals’ age, life stage, social context, and historical period intersect across the lifespan. These intersections, in turn, produce predictable as well as unique effects that translate into different attitudes, values, and behaviors, but not as a passive and predetermined function of an individual’s generation.

Myth # 9: Studying Age at Work Is the Antidote to the Problems with Studying Generations

Age and aging research are neither remedies for nor equivalent approaches to the study of generations. First, there are a broad range of phenomena encompassed in both research on “age at work” and “aging at work” (e.g., see discussion of “successful aging” research components in Zacher, 2015a ). These two areas are related but distinct, spanning the study of age as a discrete or sample-relative sociodemographic (i.e., age as a descriptive device, especially between person), age as a compositional unit property (e.g., age diversity in a team, organization), and age as a proxy for continuous processes and development over time (i.e., age representing the passage of time, especially within-person in longitudinal research). Each of these forms has a multitude of potential contributions to our understanding of the workplace, and these contributions should not (and cannot) be reduced to generational cohort-based generalizations. Second, and as noted earlier, although aging research is confounded by cohort effects, it draws on sound theories, research designs, and statistical modeling approaches (Bohlmann, Rudolph & Zacher, 2018 ). The study of generations at work, however, relies upon theories unintended for formal testing and flawed data collection methods and analyses (Costanza et al., 2017 ).

Moreover, whereas both age and aging research treat time continuously, generational research groups people into cohort categories. This results in a loss of important nuance and information about individuals, with results prone to either over- or underestimated age effects. The practice of cohort grouping also creates a “levels” issue in generational research to which age and aging research are not subject: studying aging focuses on the individual level of analysis, whereas (sociological) generational research “groups” individuals into aggregates and then incorrectly draws inferences about individual outcomes. This mismatch of levels can produce ecological or atomistic fallacies (i.e., assumptions that group-level phenomena apply to the individual level and vice versa), depending on whether group- or individual-level data are used to draw conclusions (Rudolph & Zacher, 2017 ). Thus, although age and aging research present robust opportunities for understanding how to support the age-diverse workforce, generational research provides incomplete conclusions about, and unclear implications for, understanding trends in the workplace. Studying age alone is not a substitute for generational research; rather, it transcends generational approaches and engenders more useful and tenable conclusions for researchers and practitioners alike.

Myth #10: Talking About Generations Is Largely Benign

Talking about generations is far from benign: it promotes the spread of generationalism, which can be considered “modern ageism.” Just as “modern racism” is characterized by more subtle and implicit, yet no less discriminatory or troubling, racist beliefs about black, indigenous, and people of color (BIPOC; e.g., McConahay, Hardee, & Batts, 1981 ), generationalism is defined by sanctioned ambivalence and socially acceptable prejudice toward people of particular ages. These beliefs are normalized and pervasive, reiterated across various forms of popular media and culture to the point that they seem innocuous. However, generationalism leads to decisions at a variety of levels (e.g., individual, organizational, institutional) that are harmful, divisive, and potentially illegal.

Media outlets play a large role in societal tolerance and acceptance of generationalism (Rauvola et al., 2019 ). New “generations” are frequently proposed in light of current events, and age stereotyping becomes further trivialized with each iteration. Adding to this, an abundance of generational labels “stick” while others do not—“iGen,” “Generation Wii,” “Generation Z,” and “Zoomers” all vie to define the “post-Millennial” generation (Raphelson, 2014 ), and “Generation Alpha” (a name inspired in part by naming conventions during the 2005 hurricane season; McCrindle & Wolfinger, 2009 ) now faces competition from “Gen C” to define the next generation. “Gen C” (or “Generation Corona;” see Rudolph & Zacher, 2020a , 2020b ) has gained traction in the media alongside the recent COVID-19 pandemic, with some suggesting that “coronavirus has the potential to create a generation of socially awkward, insecure, unemployed young people” (Patel, 2020 ). These labels differ markedly by country as well, as noted earlier, adding to the trivialization and confusion. More and more, these labels are also used to add levity, and/or to avoid blatant ageism, to deep-seated sociopolitical divides and conflicts portrayed in the media. Take, for example, the rise of “OK Boomer” alongside resentment toward conservatism (Romano, 2019 ), or the labeling of the “Karen Generation” to encapsulate white privilege and entitlement, especially among middle- to upper-class suburban women (Strapagiel, 2019 ).

Although often treated as harmless banter, this lexicon filters into influential research and policy-based organizations (e.g., “Gen C” in The Lancet, 2020 ), legitimizing the use of generational labels and associated age stereotypes in discourse and decision-making. As suggested above, in many countries, age is a protected class and the use of generations to inform differential practices and policies in organizations (e.g., hiring, development and training, benefits) poses great risk to the age inclusivity, and the legal standing, of workplaces (see also Costanza et al., 2020 ). Whether a generational label is new and catchy or accepted and seemingly mundane, it is built on the back of modern ageism, and generationalism—just like other “isms”—is far from benign.

Moving Beyond Generations: Two Alternative Models

With the preceding ten myths serving as a backdrop, we next introduce two models—the social constructionist perspective and the lifespan development perspective—that serve as alternative and complementary ways of thinking about, and understanding thinking about, generations and generational differences. Indeed, we propose that these are complementary models. Specifically, whereas the social constructionist perspective serves as a way of understanding why people tend to think about age and aging in generational terms , the lifespan development perspective serves as an alternative to thinking about age and aging in generational terms .

The Social Constructionist Perspective

Considering the ten myths reviewed above, it is clear that the evidence for the existence of generations and generational differences is lacking. Moreover, when applying a critical lens, what little evidence does exist does not hold up to theoretical and empirical scrutiny. What, then, are we left to do with the idea of generations? That is to say, how can we rationalize the continued emphasis that is placed on generations in research and practice despite the lack of a solid evidence base upon which these ideas rest? On the surface, this may seem to be a conceptually, rather than a practically, relevant question. However, there is a booming industry of advisors, gurus, and entire management consulting firms based around the idea of generations (e.g., Hughes, 2020 ). In whatever form it takes, generationally based practice is built upon the rather shaky foundations of this science, putting organizations and their constituents at risk—not only of wasted money, resources, and time, but of propagating misplaced ideas based on a weak, arguably non-existent evidence base (Costanza et al., 2020 ). As the organizational sciences move toward the ideals of evidence-based practice, generations and assumed differences between them are quickly becoming yet another example of a discredited management fad (see Abrahamson, 1991 , 1996 ; Røvik, 2011 ).

Borrowed from sociological theoretical traditions, the social constructionist perspective focuses on understanding the nature of various shared assumptions that people hold about reality, through understanding the ways in which meanings develop in coordination with others, and how such meanings are attached to various lived experiences, social structures, and entities (see Leeds-Hurwitz, 2009 )—including generations. Comprehensive treatments of the core ideas and tenets of the sociological notion of social constructionism can be found in Burr ( 2003 ) and Lock and Strong ( 2010 ). The social constructionist perspective on generations, which is based upon the idea that generations exist as social constructions, has been advanced as a means of understanding why people often think about age and aging in discrete generational, rather than continuous, terms (e.g., Rudolph & Zacher, 2015 , 2017 ; see also Lyons & Kuron, 2014 ; Lyons & Schweitzer, 2017 ; Weiss & Perry, 2020 ). The social constructionist perspective has utility as a model for understanding various processes that give rise to generations and for understanding the ubiquity and persistence of generations and generationally based explanations for human behavior. In an early conceptualization of this perspective, Zacher and Rudolph ( 2015 ) proposed that two processes reinforce each other to support the social construction of generations. Specifically, (1) the ubiquity and knowledge of generational stereotypes drive (2) the process of generational stereotyping, which is by-and-large socially sanctioned. These two processes fuel the social construction of generational differences, which have bearing on a variety of work-related processes, not least of which is the development of “generationalized” expectations for work specific attitudes, values, and behaviors. Such generationalized expectations set the stage for various forms of intergenerational conflicts and discrimination (i.e., generationalism; Rauvola et al., 2019 ) at work.

The social constructionist perspective on generations is grounded in three core principles: (1) generations are social constructs that are “willed into being”; (2) as social constructs, generations exist because they serve a sensemaking function; and (3) the existence and persistence of generations can be explained by various processes of social construction. The social constructionist perspective is gaining traction as a viable alternative to rather rigid, deterministic approaches of conceptualizing and studying generations, even among otherwise staunch proponents of these ideas. For example, Campbell et al. ( 2017 ) offer that “…generations might be best conceptualized as fuzzy social constructs” (p. 130) and Lyons et al. ( 2015 ) echo similar sentiments about the role and function of generations. To further clarify this perspective, we next expand upon these three core ideas that are advanced by the social constructionist perspective, providing more details and examples of each, and offering supporting evidence from research and theory.

First, the social constructionist perspective advances the idea that generations and generational differences do not exist objectively (see Berger & Luckman, 1966 , for a classic treatment of this idea of the “socially constructed” nature of reality). Rather, generations are “willed into being” as a way of giving meaning to the complex, multicausal, multidirectional, and multidimensional process of human development that we observe on a day-to-day basis, especially against the backdrop of rapidly changing societies. Adopting a social constructionist framework motivates an understanding of the various ways in which groups of individuals actively participate in the construction of social reality, including how socially constructed phenomena develop and become known to others, and how they are institutionalized with various norms and traditions. To say that generations are “social constructs,” or that generations reflect a process of “social construction,” implies that our understanding of their meanings (e.g., the “notion” of generations; the specific connotations of implying one generation versus another) exists as an artifact of a shared understanding of “what” generations “are,” and that this is accepted and agreed upon by members of a society.

Moreover, and to the second core principle, the social constructionist perspective suggests that generations serve as a powerful, albeit flawed, tool for social sensemaking. Generations provide a heuristic framework that greatly simplify people’s ability to quickly and efficiently make judgments in social situations, at the risk of doing so inaccurately. In other words, generations offer an easy, yet overgeneralized, way to give meaning to observations and perceptions of complex age-related differences that we witness via social interactions. This idea is borrowed from social psychological perspectives on the development, formation, and utility of stereotypes. When faced with uncertainty, humans have a natural tendency to seek out explanations of behavior (i.e., their own, but also others’; see Kramer, 1999 ). This process reflects an inherent need to makes sense of one’s world through a process of sensemaking. An efficient, albeit often flawed, strategy to facilitate sensemaking is the construction and adoption of stereotypes (Hogg, 2000 ). Stereotypes are understood in terms of cognitive–attitudinal structures that represent overgeneralizations of others—in the form of broadly applied beliefs about attitudes, ways of thinking, behavioral tendencies, values, beliefs, et cetera (Hilton & Von Hippel, 1996 ).

Applying these ideas, the adoption of generations, and the accompanying prescriptions that clearly lay out how members of such generations ought to think and behave, helps people to make sense of why relatively older versus younger people “are the way that they are.” Additionally, generational stereotypes can be enacted as an external sensemaking tool, as described, but also for internal sensemaking (i.e., making sense of one’s own behavior). Indeed, there is emerging evidence that people internalize various generational stereotypes and that they enact them in accordance with behavioral expectations (i.e., a so-called Pygmalion effect, see Eschleman, King, Mast, Ornellas, & Hunter, 2016 ).

Third, the social constructionist perspective offers that generations are constructed and supported through different mechanisms. The construction of generations can take various forms, for example, in media accounts of “new” generations that form as a result of major events (e.g., pandemics; Rudolph & Zacher, 2020a , 2020b ), political epochs (e.g., “Generation Merkel” Mailliet & Saltz, 2017 ; “Generation Obama,” Thompson, 2012 ), economic instability (e.g., “Generation Recession,” Sharf, 2014 ), and even rather benign phenomena, such as growing up in a particular time and place (e.g., “Generation Golf,” Illies, 2003 ).

A major source of generational construction can be traced to various “think tank”-type groups that purport to study generations. From time to time, such groups proclaim the end of one generation and the emergence of new generational groups (e.g., Dimock, 2019 ). These organizations legitimize the idea of generations in that they are often otherwise trusted and respected sources of information and their messaging conveys an associated air of scientific rigor. Relatedly, authors of popular press books likewise tout the emergence of new generations. For example, Twenge has identified “iGen” (Twenge, 2017 ) as the group that follows “Generation Me” (Twenge, 2006 ), although neither label has found widespread acceptance outside of these two texts. Importantly, all generational labels, including these, exist only in a descriptive sense, and it is not always clear if the emergence of the generation precedes their label, or vice versa. For example, consider that Twenge has suggested that the term “iGen” was inspired by taking a drive through Silicon Valley, during which she concluded that “…iGen would be a great name for a generation…” (Twenge, as quoted in Horovitz, 2012 ), a coining mechanism far from Mannheim’s original conceptualization of what constitutes a generation.

The contemporary practice of naming new generations has its own fascinating history (see Raphelson, 2014 ). Indeed, the social constructionist perspective recognizes that the idea of generations is not a contemporary phenomenon; there is a remarkable historical periodicity or “cycle” to their formation and to the narratives that emerge to describe members of older versus younger generations. As discussed earlier, members of older generations have tended to pan members of younger generations for being brash, egocentric, and lazy throughout history, whereas members of younger generations disparage members of older generations for being out of touch, rigid, and resource-draining (e.g., Protzko & Schooler, 2019 ; Rauvola et al., 2019 ). Likewise, the social constructionist perspective underlines that generations are supported through both the ubiquity of generational stereotypes and the socially accepted nature of applying such labels to describe people of different ages.

In summary, the social constructionist perspective offers a number of explanations for the continued existence of generations, especially in light of evidence which speaks to the contrary. Specifically, by recognizing that generations exist as social constructions, this perspective helps to clarify the continued emphasis that is placed on generations in research and practice, despite the lack of evidence that support their objective existence. Moreover, the social constructionist perspective offers a framework for guiding research into various processes that give rise to the construction of generations and for understanding the ubiquity and persistence of generations and generationally based explanations for human behavior. Next, we shift our attention to a complementary framework—the lifespan perspective—which likewise supports alternative theorizing about the role of age and the process of aging at work that does not require the adoption of generations and generational thinking. Then, we will focus on drawing lines of integration between these two perspectives.

The Lifespan Development Perspective

The lifespan development perspective is a meta-theoretical framework with a rich history of being applied for understanding age-related differences and changes in the work context (Baltes et al., 2019 ; Baltes & Dickson, 2001 ; Rudolph, 2016 ). More recently, the lifespan perspective has also been advanced as an alternative to generational explanations for work-related experiences and behaviors (see Rudolph et al., 2018 ; Rudolph & Zacher, 2017 ; Zacher, 2015b ). Contrary to generational thinking and traditional life stage models of human development (e.g., Erikson, 1950 ; Levinson, Darrow, Klein, Levinson, & McKee, 1978 ), the lifespan perspective focuses on continuous developmental trajectories in multiple domains (Baltes, Lindenberger, & Staudinger, 1998 ). For instance, over time, an individual’s abilities may increase (i.e., “gains,” such as accumulated job knowledge), remain stable, or decrease (i.e., “losses,” such as reduced psychomotor abilities).

Baltes ( 1987 ) outlined seven organizing tenets to guide thinking about individual development ( ontogenesis ) from a lifespan perspective. Specifically, human development is (1) a lifelong process that involves (2) stability or multidirectional changes, as well as (3) both gains and losses in experience and functioning. Moreover, development is (4) modifiable at any point in life (i.e., plasticity); (5) socially, culturally, and historically embedded (i.e., contextualism); and (6) determined by normative age- and history-graded influences and non-normative influences. Regarding the final tenet, normative age-graded influences include person and contextual determinants that most people encounter as they age (e.g., decline in physical strength, retirement), normative history-graded influences include person and contextual determinants that most people living during a certain historical period and place experience (e.g., malnutrition, recessions), and non-normative influences include determinants that are idiosyncratic and less “standard” to the aging process (e.g., accidents, natural disasters). Finally, Baltes ( 1987 ) argued that (7) understanding lifespan development requires a multidisciplinary (i.e., one that goes beyond psychological science) approach. In summary, the lifespan perspective recognizes that individuals’ development is continuous, malleable, and jointly influenced by both normative and non-normative internal (i.e., those that are genetically determined; specific decisions and behaviors that one engages in) and external factors (i.e., the sociocultural and historical context).

A generational researcher may ask research questions like (a) “How does generational membership influence employee attitudes, values, and behaviors?” or (b) “What differences exist between members of different generations in terms of their work attitudes, values, or behaviors?” Then, likely based on the results of a cross-sectional research design that collects information on age or birth year and work-related outcomes, a generational researcher would likely categorize employees into two or more generational groups and take mean-level differences in outcomes between these groups as evidence for the existence of generations and differences between them. Contrary to this, a lifespan researcher would be more apt to ask research questions like (a) “Are there age-related differences or changes in work attitudes, values, and behaviors?” or (b) “What factors serve to differentially modify employees’ continuous developmental trajectories?” They would seek out cross-sectional or longitudinal evidence for age-related differences or changes in attitudes, values, and behaviors, as well as evidence for multiple, co-occurring factors, including person characteristics (e.g., abilities, personality), idiosyncratic factors (e.g., job loss, health problems), and contextual factors (e.g., economic factors, organizational climate) that may predict these differences or changes.

The lifespan perspective generally does not operate with the generations concept, but does distinguish between chronological age, birth cohort, and contemporaneous period effects. As described earlier, generational groups are inevitably linked to group members’ chronological ages, as they are based on a range of adjacent birth years and typically examined at one point in time. Accordingly, tests of generational differences involve comparisons between two or more age groups (e.g., younger vs. older employees). In contrast to tenets #1, #2, and #3 of the lifespan perspective, generational thinking is static in that differences between generations are assumed to be stable over time. The possibility that members of younger generations may change with increasing age, or whether members of older generations have always shown certain attitudes, values, and behavior, are rarely investigated. Moreover, generational thinking typically adopts a simplistic view of differences between generational groups (e.g., “Generation A” has a lower work ethic than “Generation B”) as compared to the more nuanced lifespan perspective with its focus on stability or multidirectional changes, as well as the joint occurrence of both gains and losses across time.

With regard to the lifespan perspective’s tenet #4 (i.e., plasticity), generational researchers tend to treat generational groups as immutable (i.e., as they are a function of one’s birth year) and their influences as deterministic (i.e., all members of a certain generation are expected to think and act in a certain way; so-called cohort determinism). In contrast, the lifespan perspective recognizes that there is plasticity, or within-person modifiability, in individual development at any age. Changes to the developmental trajectory for a given outcome can be caused by person factors (e.g., knowledge gained by long-term practice), contextual factors (e.g., organizational change), or both. For instance, lifespan researchers assume that humans enact agency over their environment and the course of their development. Development is not only a product of the context in which it takes place (e.g., culture, historical period) but also a product of individuals’ decisions and actions. This notion underlies the principle of developmental contextualism (Lerner & Busch-Rossnagel, 1981 ), embodied within the idea that humans are both the products and the producers of their own developmental course.

Research on generations and intergenerational exchanges originated and still is considered an important topic in the field of sociology (Mayer, 2009 ), which emphasizes the role of the social, institutional, cultural, and historical contexts for human development (Settersten, 2017 ; Tomlinson, Baird, Berg, & Cooper, 2018 ). In contrast, the lifespan perspective, which originated in the field of psychology, places a stronger focus on individual differences and within-person variability. Nevertheless, the lifespan perspective’s tenet #5 (i.e., contextualism) suggests that individual development is not only influenced by biological factors but also embedded within the broader sociocultural and historical context. This context includes the historical period, economic conditions, as well as education and medical systems in which development unfolds. Even critics have acknowledged that these external factors are rather well-integrated within the lifespan perspective (Dannefer, 1984 ). That said, most empirical lifespan research has not distinguished between birth cohort and contemporaneous period effects.

For example, studies in the lifespan tradition have suggested that there are birth cohort effects on cognitive abilities and personality characteristics (Elder & Liker, 1982 ; Gerstorf, Ram, Hoppmann, Willis, & Schaie, 2011 ; Nesselroade & Baltes, 1974 ; Schaie, 2013 ). Possible explanations for these effects may be improvements in education, health and medical care, and the increasing complexity of work and home environments (Baltes, 1987 ). An important difference to generational research is that these analyses focus on individual development and outcomes and not on group-based differences.

In contrast to research in the field of sociology, the lifespan perspective generally does not make use of the generations concept and associated generational labels. Instead, in addition to people’s age, lifespan research sometimes focuses on birth year cohorts (Baltes, 1968 ). However, the lifespan perspective does not assume that all individuals born in the same birth year automatically share certain life experiences or have similar perceptions of historical events (Kosloski, 1986 ). According to Baltes, Cornelius, and Nesselroade ( 1979 ), researchers interested in basic developmental processes (e.g., child developmental psychologists) that were established during humans’ genetic and cultural evolution may treat potential cohort effects as error or as transitory, historical irregularities. In contrast, other researchers (e.g., social psychologists, sociologists) may focus less on developmental regularities and treat cohort effects as systematic differences in the levels of an outcome, with or without explicitly proposing a substantive theoretical mechanism or process variable that explains these cohort differences (e.g., poverty, access to high-quality education). Empirical research on generations is typically vague with regard to concrete theoretical mechanisms of assumed generational differences (i.e., beyond the notion of “shared life events and experiences,” such as the Vietnam war, 9/11, or the COVID-19 pandemic) and typically does not operationalize and test these mechanisms.

In proposing the general developmental model, Schaie ( 1986 ) suggested decoupling the “empty variables” of birth cohort and time period from chronological age and re-conceptualizing them as more meaningful variables. Specifically, he re-defined cohort as “the total population of individuals entering the specified environment at the same point in time” and period as “historical event time,” thereby uncoupling period effects from calendar time by identifying the timing and duration of the greatest influence of important historical events (Schaie & Hertzog, 1985 , p. 92). Thus, the time of entry for a cohort does not have to be birth year and can include biocultural time markers (e.g., puberty, parenthood) or societal markers (e.g., workforce entry, retirement; Schaie, 1986 ). Similarly, the more recent motivational theory of lifespan development has discussed cohort-defining events as age-graded opportunity structures (Heckhausen, Wrosch, & Schulz, 2010 ). Thus, from a lifespan perspective, cohorts are re-defined as an interindividual difference variable, whereas period is re-defined as an intraindividual change variable (Schaie, 1986 ).

Tenet #6 of the lifespan perspective suggests that individuals have to process, react to, and act upon normative age-graded, normative history-graded, and non-normative influences that co-determine developmental outcomes (Baltes, 1987 ). The interplay of these three influences leads to stability and change, as well as multidimensionality and multidirectionality in individual development (Baltes, 1987 ). Importantly, the use of the term “normative” is understood in a statistical–descriptive sense here, not in a value-based prescriptive sense; it is assumed that there are individual differences (e.g., due to gender, socioeconomic status) in the experience and effects of these influences (Baltes & Nesselroade, 1984 ). Moreover, the relative importance of these three influences can be assumed to change across the lifespan (Baltes, Reese, & Lipsitt, 1980 ). Specifically, normative age-graded influences are assumed to be more important in childhood and later adulthood than in adolescence and early adulthood (i.e., due to biological and evolutionary reasons). In contrast, normative history-graded determinants are assumed to be more important in adolescence and early adulthood than in childhood and old age (i.e., when biological and evolutionary factors are less important). Finally, non-normative influences are assumed to increase linearly in importance across the lifespan (Baltes et al., 1980 ; see also Rudolph & Zacher, 2017 ). Indeed, the assumed differential importance of these influences across the lifespan differs markedly from the cohort deterministic approach implied in generational theory and research.

According to Baltes et al. ( 1980 ), idiosyncratic life events become more important predictors of developmental outcomes with increasing age due to declines in biological and evolutionary-based genetic control over development and the increased heterogeneity and plasticity in developmental outcomes at higher ages. Despite the assumed relative strengths of these normative and non-normative influences across the lifespan, they are at no point completely irrelevant to individual development. For example, in the work context, the theoretical relevance of history-graded influences on work-related outcomes may be a factor that determines the strength of potential effects (Zacher, 2015b ). For instance, experiencing a global pandemic is more likely to influence the development of individuals’ attitudes—not an entire generations’ collective attitudes—toward universal health care than it is to influence their job satisfaction. Moreover, individuals’ level of job security may not only be influenced by the pandemic but also by their profession and levels of risk tolerance.

In summary, the lifespan development perspective offers a number of alternative explanations for the role of age and the process of aging at work that do not rely in generational explanations. Specifically, by recognizing that development is a lifelong process that is affected by multiple influences, this perspective helps to clarify the complexities of development, particularly the processes that lead to inter- and intraindividual changes over time. With a clearer sense of these two alternative perspectives, we next shift our attention to outlining various points of integration between them.

Integrating the Social Constructionist and Lifespan Development Perspectives

With a clearer sense of the core tenets of the social constructionist and lifespan development perspectives, we now turn our attention to clarifying lines of integration between these two approaches. While seemingly addressing different “corners” of the ideas presented here, there are a number of complementary features of the social constructionist and lifespan development perspectives to be noted. First, both perspectives generally eschew the idea that generations exist objectively and are meaningful units of study for explaining individual and group differences. Second, both perspectives offer that the complexities that underlie the understanding of age and the process of aging at work cannot be reduced to rather simple mean-level comparisons. Third, both perspectives are generative, in that they encourage research questions that go beyond common ways of thinking. Fourth, and relatedly, both perspectives provide frameworks for more “directly” studying aging and development—whether in the form of how we collectively understand and conceptualize these processes (the social constructionist perspective), or how individuals continuously and interactively shape their own life trajectory (the lifespan development perspective). Together, rather than relying on determinism, these perspectives capitalize on the subjective, dynamic, and agentic aspects of life in organizations and society, allowing for more rigorous and representative research into meaning, creation, stability, and change in context.

Commonalities Between Social Constructionist and Lifespan Development Perspectives

Beyond these complementary features, we propose six additional commonalities that serve as the basis for a more formal integration of these two perspectives with one another (see Table ​ Table2 2 for a summary). First, both perspectives recognize the role of context, in that both development (the lifespan development perspective) and sensemaking (the social constructionist perspective) occur within social contexts. Second, both perspectives describe processes of action, creation, negotiation, and/or codification. Whereas the lifespan perspective focuses on how these processes create identity, beliefs, and habits or behaviors that emerge over time through active self-regulatory, motivational processes, discovery, and (self)acceptance/selectivity, the social constructionist perspective focuses more so on the development of truths and meaning that emerge from collective dialogues, understandings, and traditions through acceptance and institutionalization. Third, both perspectives acknowledge the fundamental roles of internal and external comparisons. For example, the lifespan perspective offers that successful development is judged both externally (e.g., in comparison with important others, normative age expectations, or timetables) and internally (e.g., in comparison with younger or desired state selves). Similarly, social constructions can be focused externally (e.g., in the form of stereotypes) as well as internally (i.e., to make sense of one’s own behavior or identity).

Commonalities between the social constructionist and lifespan perspectives

Fourth, both perspectives highlight learning and reinforcement processes that are derived from environmental sources. The lifespan perspective offers that adaptiveness (e.g., how successfully someone is developing/aging) and the self (as well as identities, values, behaviors, etc.) are learned from and reinforced by feedback from various aspects of the environment. Similarly, social constructions are derived from and reinforced by multiple environmental sources, including those with perceived status, “weight,” and legitimacy. Fifth, by offering that development is a modifiable, discontinuous process (the lifespan development perspective) and that social constructions are constantly re-defined and re-emerge into public consciousness (the social constructionist perspective), both perspectives focus on continuous evolution, revision, and change. The final commonality to be drawn across these two perspectives is that they both focus on predictable influences that characterize certain spans of time, especially around significant events or “turning points.” The lifespan perspective offers that, although complex and plastic, development does have some predictable aspects and influences due to their significance in the life course (e.g., age-graded events). Complimenting this, many social constructions, although in constant flux and redefinition, fall back on the same key concepts due to their pervasiveness in public consciousness (e.g., the laziness of youth) at certain “key moments” in history (e.g., to explain or cope with societal change).

Limitations of These Alternative Perspectives

Beyond the benefits of considering alternative models to generations, and integrations thereof, it is important to mention the limitations of these alternative perspectives. For example, it could be argued that, because it does not provide formalized predictions, the social constructionist perspective is “hard to study.” Additionally, the lifespan perspective can be criticized, just as it is lauded, for its focus on individual agency: as noted earlier, psychological perspectives often place a premium on studying individual-level mechanisms rather than other levels of influence (Rauvola & Rudolph, 2020 ). Thus, without directed efforts on the part of researchers to attend to these aspects of lifespan development theory in their work, it can be easy to fall into the “trap” of ignoring structural factors (e.g., socioeconomic status, governmental policy, institutionalized discrimination) that have bearing on and may constrain individuals’ agentic influence on their life trajectory (for an integration of the psychological lifespan perspective and the sociological life course perspective in the context of vocational behavior and career development, see Zacher & Froidevaux, 2020 ). Still, and for the many reasons noted throughout this manuscript, we do not contend that generational cohort membership is one of these structural factors, and a generational approach ignores these other forces even more flagrantly.

Recommendations for Adopting Alternative Theoretical Perspectives on Generations

Overall, we argue that organizational researchers and practitioners should move beyond the notion of generations for understanding the complexities of age at work. To do so, we urge the adoption of the alternative theoretical models we have outlined here, as well as considerations of their integration. To this end, those interested in studying the role of age at work should adopt a lifespan, rather than a generational, perspective, whereas those interested in studying the persistence of generational thinking would be well served to consider the adoption of a social constructionist perspective. Moreover, to understand more holistically the role of age and the construction of aging at work, it may be useful to adopt an integrative view on these two perspectives, embodied within the six commonalities between them that we have outlined above (see also Table ​ Table2 2 ).

Generational thinking is problematic because it assumes that aggregate social phenomena can explain individual-level attitudes, values, and behavior. In contrast, adopting a lifespan perspective means taking a multidisciplinary lens to understanding age-related differences and changes at work by specifically focusing on how the interplay between person characteristics and contextual variables serve to modify individual development. Moreover, the social constructionist perspective offers guidance for unpacking the meanings people attach to assumptions that are made about these aggregate social phenomena, further aiding in understanding the complexities at play here. We consider recommendations for research and practice adopting these perspectives, next.

Recommendations for Adopting the Social Constructionist Perspective

The social constructionist perspective on generations highlights a number of potential areas for research and practice. Given their longstanding and culturally/historically embedded nature, the social constructionist perspective recognizes that the idea of generations is not likely to go away, even with a lack of empirical methods or evidence to support their existence. Instead, this perspective calls for a paradigm shift in generational research and practice, away from the rather positivist notion of “seeking out” generational differences and instead toward a focus on studying and understanding those processes that support the social construction of generations to begin with. Considering research, the focus could be on those antecedents (e.g., intergroup competition and discrimination; North & Fiske, 2012 ; i.e., to address the question, “Why do these social constructions emerge?”) and outcomes (e.g., self-fulfilling prophecies—i.e., to address the question, “What are the consequences of willing generations into being?”) of socially constructed generations.

Conducting research from a social constructionist perspective requires adopting methodologies that may not be common in organizational researchers’ “tool kits.” For example, Rudolph and Zacher ( 2015 ) used sentiment analysis, a natural language processing methodology, to analyze the content of Twitter dialogues concerning various generational groups to understand the relative sentiment associated with each. Indeed, it would arguably be difficult to study generations from this perspective by adopting a typical frequentist approach to hypothesis testing. This perspective is less about gathering evidence “against the null hypothesis” that generations or differences between them exist in a more or less “objective” (i.e., measurable) way. Instead, it is more about understanding, phenomenologically, the various processes that give rise to people’s subjective construction of generations, the systems that facilitate attaching meaning to generational labels, and the structures that support our continued reliance on generations as a sensemaking tool in spite of logical and empirical arguments against doing so.

More practically, understanding why people think in terms of generations can help us to develop interventions that are targeted at helping people think less in terms of generations and more in terms of individuating people on the basis of the various processes outlined in our description of the lifespan perspective (i.e., personal characteristics; idiosyncratic and contextual factors). The social constructionist perspective also encourages changing the discourse among practitioners, shifting the focus away from managing generations as discrete groups and toward developing more age-conscious personnel practices, policies, and procedures that support workers across the entirety of their working lifespans (e.g., Rudolph & Zacher, 2020c ). We thus urge practitioners to adopt a social constructionist perspective and shift focus away from promoting processes to manage members of different generations to a focus on managing the perceptions of generations and their differences. By recognizing the constructed nature of generations, the social constructionist perspective decouples beliefs about generations from these broad and overgeneralized assumptions about their influence on individuals.

Recommendations for Adopting the Lifespan Perspective

Just as the social constructionist perspective highlights a number of potential areas for research and practice, so too does the lifespan perspective. To this end, and to move research on the lifespan perspective on generations forward, Rudolph and Zacher ( 2017 ) argued that, at the individual level of analysis, the influence of age-graded and historical/contextual influences are inherently codetermined and inseparable. Accordingly, in their lifespan perspective on generations, they proposed that the influence of historically graded and sociocultural context variables occurs at the individual level of analysis only, and not as a manifestation of shared generational effects (proposition 1). They suggested that future research should focus on individual-level indicators of historical and sociocultural influences. Furthermore, they argued that age, period, and cohort effects are both theoretically and empirically confounded and, thus, inseparable (proposition 2). Finally, consistent with Schaie’s ( 1986 ) general developmental model, they suggested that cohorts should be operationalized as interindividual differences, whereas period effects should be defined in terms of intraindividual changes (proposition 3).

In terms of more practical implications of the lifespan perspective, we urge practitioners to adopt principles of lifespan development in the design of age-conscious work processes, interventions, and policies that do not rely on generations as a means of representing age. Indeed, researchers and practitioners alike should take steps to avoid the pitfalls of “generational thinking,” which yields several dangers that can be overcome by lifespan thinking (Rauvola et al., 2019 ; Rudolph et al., 2018 ; Rudolph & Zacher, 2020c ). First, generational thinking categorizes individuals into arbitrary generational groups based on a single criterion (i.e., birth year) and is therefore socially exclusive rather than inclusive; in contrast, the lifespan perspective conceptualizes and operationalizes age directly as a continuous variable (Baltes, 1987 ). Second, generational thinking reduces complex age-related processes into a simplistic dichotomy at a single point in time; the lifespan perspective adopts a multidimensional, multidirectional, and multilevel approach to represent the complexities of aging more appropriately. Third, generational thinking overemphasizes the role of (ranges of) birth cohorts in influencing work outcomes; in contrast, the lifespan perspective emphasizes interindividual differences and intraindividual development (as well as interindividual differences in intraindividual development). Finally, generational thinking is dangerous because it assumes that generational group membership determines individual attitudes, values, and behavior. In contrast to this, the lifespan perspective, which entails the notion of plasticity, suggests that intraindividual changes in developmental paths are possible at any age and that individuals can enact control and influence their own development.

Conclusions

This manuscript sought to achieve two goals, related to helping various constituents better understand the complexities of age and aging at work, and dissuade the use of generations and generational differences as a means of understanding and simplifying such complexities. First, we aimed to “bust” ten common myths about generations and generational differences that permeate various discussions in organizational sciences research and practice and beyond. Then, with these debunked myths as a backdrop, we offered two complementary alternative models—the social constructionist perspective and the lifespan perspective—with promise for helping organizational scientists and practitioners better understand and manage age and the process of aging in the workplace and comprehend the pervasive nature of generations as a means of social sensemaking. The social constructionist perspective calls for a shift in thinking about generations as tangible and demonstrable units of study, to socially constructed entities, the existence of which is in-and-of-itself worthy of study. Supplementing these ideas, the lifespan perspective offers that rather than focusing on simplified, rather deterministic groupings of people into generations, development occurs in a continuous, multicausal, multidirectional, and multidimensional process. Our hope is that this manuscript helps to “redirect” talk about generations away from their colloquial use to a more critical and informed perspective on age and aging at work.

Publisher’s Note

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ORIGINAL RESEARCH article

Generational diversity in the workplace: psychological empowerment and flexibility in spanish companies.

• 1 Facultad de Ciencias Económicas y Empresariales, ICADE, Universidad Pontificia Comillas, Madrid, Spain
• 2 Facultad de Ciencias Humanas y Sociales, CHS, Universidad Pontificia Comillas, Madrid, Spain

Intergenerational diversity is a universal fact in sustainability and today’s work environment. Current studies seek to find differences that exist between these generational groups that coexist, cooperate, and sometimes compete in business organizations. Sixteen focus groups have taken place, four for each generation to find the differences that may exist depending on that group membership. Specifically, the psychological empowerment and psychological flexibility variables have been analyzed, which have already shown their relevance to improve performance. Results show differences between the older generations (BB and Gen X) and the younger ones (Gen Y and Gen Z).

Introduction

The development of the Internet and data analysis ( Geczy et al., 2014 ), the abundance of information ( Southwell, 2005 ), the globalization ( Mark, 1996 ), the growing interest in diversity ( Guajardo, 2014 ), the increased consumer power ( Kucuk, 2008 ), or what is known as the sharing economy ( Belk, 2018 ), all represent deep changes which are affecting people and organizations to a great extent. This environment is now defined as VUCA ( Whiteman, 1998 ), an acronym of Volatility, Uncertainty, Complexity, and Ambiguity.

Companies are responding to this new environment in very different ways. One of the most common is the intensification of work, which is understood both as the hours worked as well as the intensity of the work. This intensification is reaching the acceptable limits ( Brown, 2012 ) and at the same time has resulted in pressure on employees moving from peaks and troughs to becoming something continuous. This has associated implications both for people and companies ( Dawson et al., 2001 ).

At the same time, employees’ commitment levels are at very low levels. As a result, only 13% of employees say that they are committed to their company ( Gallup, 2013 ). This requires greater attention if we remember the direct link between commitment and performance, a link which has been widely demonstrated ( Harter et al., 2002 ).

The Human Resources function therefore has many aspects to manage which were not present in past decades. In a survey from 2013 carried out among 1,300 Human Resources professionals, 70% said they could not deal with complexity, with 60% saying they had serious doubts about their organization’s ability to deal with this increasing complexity ( Lumesse, 2013 ).

Given that the ability to adapt is essential in order to achieve good results ( Heugens and Lander, 2009 ; Reeves and Deimler, 2011 ), people management in organizations needs to adopt new tools and/or review existing ones in order to continue adding value to organizations according to this new VUCA environment. In modern organizations, we may find employees of four different generations. Generational diversity is essential to face the volatility and uncertainty but at the same time it may increase complexity regarding people management ( Amayah and Gedro, 2014 ). A better understanding of this generational diversity will help to orientate politics and human resources practices.

Within this review of existing tools, we have identified two which have a significant impact with regard to performance and helping people to adapt to their professional environment: psychological empowerment and psychological flexibility. Up to date, there are no studies that analyze these concepts with the generational aspect of the employees. This study seeks to strengthen our understanding of these topics while identifying possible differences by analyzing them from a generational perspective, knowing that the diversity of human capital is present in modern organizations ( Shen et al., 2009 ; Page, 2010 ).

Generation, an Ambiguous Concept

Generational differences in the workplace as a research and intervention topic have recently grown significantly in popularity ( Joshi et al., 2011 ; Lyons et al., 2015 ; Campbell et al., 2017 ). The number of widely circulated articles, media reports, and blogs has grown even more significantly too. At the same time, in the management world, there are numerous human resources consulting initiatives which consider intergenerational diversity and intervention policies are being created based on these.

Karl Mannheim, a pioneer in the conceptualization of the term generation, proposed that a generation, any generation, is determined by participation in the same events. These events are the source of vital contents that are fixed in the consciences of people as the “natural” way in which the world exists. As a result, a natural image of the world is formed which guides others, is the base from which subsequent events are understood; it is the code for interpreting everything that happens. For Mannheim (1993) , the process is very determinant because it happens in the first stage of life. The active participation in the social currents that constitute and give meaning to the historical moment creates the generational bond. This is how one generation creates a new historical situation ( Mannheim, 1993 ; Edmunds and Turner, 2005 ).

Growing in a group does not only involve making assessments based on these interpretation principles which the group are characterized by, it also involves capturing certain aspects, those nuances, and meanings of certain concepts in which reality is present within the group ( France and Roberts, 2015 ). The individuals are linked through a generational connection, only to the extent that they participate in social events which represent and give meaning to the respective historical moment, and to the extent that they take part (both actively and passively) in new interactions which make up the new situation ( Mannheim, 1993 ; Pilcher, 1994 ).

To define and identify this great complexity with the date of birth is a great simplification ( Dimock, 2019 ). This limitation does not prevent the occurrence of many and very diverse investigations in which the date of birth has been used as a key criterion of differentiation ( Kowske et al., 2010 ; Andert, 2011 ; Suomäki et al., 2019 ).

It is easy to think that, if someone has grown up and developed in a different world to someone else in history, they might have different ways of thinking, even if they are from the same place. In the academic and empirical studies environment, there is some controversy surrounding the suitability of the “generation” concept, its explanatory characteristic, and its reliability and applicability. The fundamental reproaches to these studies relate to the explanatory weakness of the generation concept ( Giancola, 2006 ; Ng and Feldman, 2010 ; Constanza et al., 2012 ; Constanza and Finkelstein, 2015 ). Similarly, and equally as important, is the intrinsic link between the generation concept and other variables such as age, historical period, and cohort when it comes to belonging to a group ( Campbell and Twenge, 2014 ; Segers et al., 2014 ), which according to these criticisms make this an ambiguous concept.

On the other hand, it is recognized as an area of research which lacks maturity and empirical contrast, although it is growing and slowly consolidating ( Lyons and Kuron, 2014 ).

There are studies that talk about differences in generations, for example, Twenge and Campbell (2008) , show how generation Y (Gen Y) has higher levels of self-esteem, anxiety, and narcissism. On the other hand, other studies show that there are practically no differences between generations ( Hart et al., 2003 ), Korn (2010) concludes that at the organizational level the differences between generations are not very significant ( Korn, 2010 ).

It is important to mention that one of the areas where this increase is most evident is in the study of how the differences in generational identity have consequences in the workplace. From the initial studies focused on the concept of generational identity itself ( Dencker et al., 2008 ; Joshi et al., 2010 ), there has been a slow but steady increase and deepening in the consequences of values at work, motivation, and other variables relating to workplace performance ( Twenge et al., 2010 ; Sakdiyakorn and Wattanacharoensil, 2017 ).

Until very recently, bureaucratic organizations had a holistic culture in which habits and ways of working were created and determined, and these concealed diversity as well as the novelty of new agents or employees ( Lok and Crawford, 2004 ). These days, although these socialization phenomena are still present in company culture, they are no longer so prevalent; autonomy and self-expression are considered essential for workers’ knowledge ( Robbins and Judge, 2009 ).

Employees’ Psychological Empowerment

The concept of empowerment (applied in companies), started to become relevant when Conger and Kanungo (1988) identified it as a key component for organizational management and effectiveness, defining it as “a motivational construct aimed at enablement rather than delegation”. Kanter (1993) considered empowerment as the mobilization of resources, information, and support to get things done, incorporating the concept of reporting lines, both formal and informal.

There are two different interpretations of empowerment in the literature, the first of which is known as structural, based on resources and the organization’s ability to act with regard to its workers ( MacDuffie, 1995 ; Wright et al., 2003 ; Gibson et al., 2007 ). The second interpretation of empowerment is linked to intrinsic motivation as well as employees’ reaction to resources, information, and support which are made available ( Spreitzer, 1995 ). This interpretation is more closely linked to the beliefs of the employees themselves ( Harrim and Alkshali, 2008 ), and is known as psychological empowerment.

Thomas and Velthouse (1990) defined psychological empowerment as being formed of four aspects: meaningfulness, competence, choice, and impact. Based on this theoretical model, Spreitzer (1995) created a measurement scale, substituting “meaningfulness” with “meaning” and “choice” with “self-determination” ( Liden et al., 2000 ). Spreitzer’s (1995) model provides psychological empowerment with a motivational dimension; that is, people who are empowered should demonstrate an active attitude toward work, incorporating their own beliefs to their role within the organization ( Fernández et al., 2015 ).

These four factors can be seen as a description of the relationship between the employee and their work. Therefore, competence considers the relationship between the person and the tasks they carry out; meaning describes the link between the employee’s objectives and goals with those of the organization. Self-determination describes the freedom with which the employee carries out tasks and the relationship with the organization’s rules. Finally, impact reflects the perception that the employee has with regard to the results of their performance.

In recent decades, psychological empowerment has been widely used in studies on workplace characteristics ( Aryee and Chen, 2006 ; Chen et al., 2007 ); a strong link between intrinsic motivation and creativity ( Zhang and Bartol, 2010 ), supervision and leadership styles ( Kim and Kim, 2013 ) was identified. Relationships between this variable and results in the workplace have also been identified, with negative impacts on employee turnover being identified ( Kim and Fernandez, 2017 ) and positive impacts between empowerment and workplace satisfaction ( Koberg et al., 1999 ; Liden et al., 2000 ; Carless, 2004 ; Aryee and Chen, 2006 ), with the level of commitment and improvement in the company’s performance ( Sahoo et al., 2010 ; Yao et al., 2013 ).

Although psychological empowerment has been widely investigated, there are no studies that relate it with the generations which would help to better orientate HR policies and practices.

Psychological Flexibility

Psychological flexibility is the objective of clinical intervention known as Acceptance and Commitment Therapy (ACT). As a result, it is the final outcome of a process in which a number of psychological variables (and their evolution) are taken into account.

ACT is a therapy based on Relational Frame Theory, which facilitates a change in behavior based on the way that people establish relationships between words and events ( Hayes et al., 2001 ). As well as cognitive and behavioral aspects, ACT also introduces a more transcendent component with elements such as values. Its objective is to introduce greater flexibility in terms of cognition, helping the person to confront situations from a different perspective, allowing the person to establish a new Relational Frame (Relational Frame Theory), and as a result, new behavior ( Hayes, 2004 ).

ACT is present across different types of intervention among which the following can be highlighted: practicing mindfulness, the use of metaphors, personal experience processes, learning linked to the definition and achievement of goals and objectives, identification of values, etc. ( Hayes et al., 2006 ).

ACT has been shown to be hugely effective in helping people tackle complex situations such as anxiety, stress, depression, psychosis, addictions, acute pain, etc., and has also proven highly effective in reducing and transforming negative thoughts ( Zettle and Hayes, 1986 ; Bach and Hayes, 2002 ; Ruiz, 2010 , 2012 ; Jansen et al., 2017 ). In summary, ACT is a collection of tools which are proven to be effective in helping people change their thoughts and behavior, even with complex problems.

This therapeutic approach is based on a series of components which are essential for understanding and achieving psychological flexibility. According to Hayes (2004) , who created this approach, there are six: contact with the present moment, values, committed action, self as context, defusion, and acceptance ( Hayes et al., 2006 ). These six elements revolve around two poles: awareness and acceptance, and commitment and adopting new behavior ( Hayes et al., 2006 ). The six elements mentioned are presented in a hexagon known as the “hexaflex” ( Hayes et al., 2006 ), as shown in Figure 1 .

Figure 1 . Prepared by the authors based on Hayes et al. (2006) , p. 25.

The aim of ACT is to help individuals to be in touch with, embrace, and evaluate their current circumstances in order to act in a better way in various situations ( Bond et al., 2006 ). This means being psychologically flexible. We understand psychological flexibility as the ability to connect with the present moment, with an attitude that embraces whatever is happening in the moment, and as a result of this acceptance, acting with awareness and consistently based on the person’s own values ( Hayes et al., 2004a , b ). It is very closely linked to feeling like a protagonist rather than a victim, as well as the ability to choose and keep up the pace to achieve the end result, despite any difficulties that may be encountered on the way.

One of the areas in which human beings confront situations where their psychological flexibility is put into practice is the workplace. There have been many empirical studies that have explored psychological flexibility in the workplace, more specifically with regard to health in the workplace ( Flaxman and Bond, 2010 ; Lloyd et al., 2013 ).

Multiple longitudinal studies have shown that there is a correlation between higher levels of psychological flexibility, and work related results, including better productivity, improved mental health, and increased ability to learn new skills at work ( Bond and Bunce, 2003 ; Bond and Flaxman, 2006 ; Bond et al., 2016 ). It has also been found that people with higher levels of psychological flexibility make better use of the resources available to them in the work environment. Bond et al. (2008) demonstrate that the highest levels of psychological flexibility improved the positive impact of a job role redesign. Although all these investigations indicate that psychological flexibility may help organizations to help people to adapt to new changes, there is no information about the differences in psychological flexibility trough generations. This knowledge would help to be more effective in HR actions and facilitate company’s adaption to environment challenges.

Objective of the Research

The investigation tries to increase the current knowledge of the generational diversity within the professional environment to help Human Resources areas to orientate their practices. In a more specific sense, this research is to try to better understand two variables which have an important impact on helping workers to adapt to an ever-changing environment. Therefore, we will analyze these based on a third component: generational diversity. This research aims to answer the question of whether there are differences in the aforementioned discourse depending on the generational group, in relation to their psychological empowerment and psychological flexibility at work.

Our initial hypothesis is that there may be differences in both psychological variables due to being from a different generation. Those generations with more experience and more opportunities to reflect on their experiences show greater levels of flexibility, and those groups with more professional experience and a greater sense of their role in the company also show clear differences with regard to psychological empowerment.

Methodology

This is a qualitative study based on focus groups. These focus groups have been conducted by a model and a method with the aim of discussing and concluding the objectives of the research.

Focus Groups

All participants were volunteers. They were selected by their managers and HR Directors looking for diversity in educational level, years in the company, sex, and hierarchical level. In total, 16 focus groups took place, four for each age group that was being studied; 156 workers participated in this stage of the research, of which 88 were male and 68 were female.

The research team is incredibly grateful to the companies who provided these employees: Baxter, BBVA, Enagás, Ferrovial, Gas Natural Fenosa, Heineken, Mapfre, Meta4, Orange, Sabadell, Sandoz, Santander, Pascual Hermanos, REPSOL, and Universia. These companies are leaders in their sectors, and represents baking, energy, construction, consumer goods, and pharma industries. All the groups were recorded, and these recordings were transcribed in order to analyze the discussion. As a result of these groups, a “content base” was created to hold all the information collected during the discussions.

Throughout the process, ethical standards were respected according to the Helsinki Declaration ( World Medical Association, 2001 ). All participants gave their written informed consent to be recorded and to use the information extracted from the groups. There was complete transparency with the participants.

As previously said, the concept of generation includes historical, social, and psychological variables. It is a concept with multiple faces and related to each other with great complexity, setting the limits of that complexity between two birth dates is a simplification.

The generational dimension which this intergenerational study hoped to provide presented various challenges due to the various grouping options and the lack of clear consensus defining each generation. Based on the meta-analysis by Constanza et al. (2012) , the team decided to define the following four groups, according to their year of birth: Baby Boomer – BB (1955–1969), Generation X – Gen X (1970–1981), Generation Y or Millennials (1982–1992), and lastly Generation Z – Gen Z, those born after 1993 1 .

Their availability to attend the group meetings was also taken into account. This simplified and arbitrary way of defining a generation has been widely criticized ( Constanza et al., 2012 ; Constanza and Finkelstein, 2015 ), and the need to carry out a deeper analysis on the variables involved in the generation concept has been emphasized, so more than just the date of birth is considered ( Lyons and Kuron, 2014 ; Wang and Peng, 2015 ). Lyons and Schweitzer (2017) adopt a more comprehensive approach, based on the phenomena of social categorization and identity ( Lyons et al., 2015 ).

In all the focus groups in which people had been categorized as members of a generation, there was discussion among the group in terms of their awareness of belonging to that group and how that categorization fits with their own perceptions. The aim of this article is not to review the components of social categorization, but we should highlight that only two of the participants across all the groups were uncomfortable with this categorization and identified themselves as belonging to a different category. The rest were satisfied with the proposed examples, which is much higher than in previous studies ( Roberto and Biggan, 2014 ; Lyons and Schweitzer, 2017 ).

The four groups from the BB generation took place between March 2016 and January 2017, with a total of 36 people taking part, of which 22 were women and 14 were men. The groups were made up of five, nine, 11, and 11 people. The four Gen X groups took place between February 2016 and September 2016. In total, 41 people took part, of which 19 were women and 22 were men. The groups were made up of 15, seven, eight, and 11 people in each. The four Gen Y groups took place between March 2016 and May 2016 with 43 people taking part. There were 22 women and 21 men, and each group was made up of 12, 11, seven, and 13 people. Gen Z was studied between May 2016 and March 2017, with a total of 36 people taking part (25 women and 11 men). Four groups took place with six, eight, 10, and 11 people.

All the participants were current employees or interns. Interns were included because of the young age of the last generation represented (younger than 23 years old), of the companies that provided samples the number of under 23 s was negligible. Interns were included and, although they do not have permanent employment with the company, it is the only opportunity to see how members of this youngest generation are adapting to the workplace. In addition, interns represent many of the other employees’ discourses. It is common for these interns to be recognized as the main source of young talent and a “breath of fresh air” in the company.

It is also necessary to mention that from this generation there has also been access to young people who are “enjoying” a graduate program, something which demonstrates exceptional initiative, preparation and ability. In either case, the representatives of Gen Z which we have had access to (interns, employees, or graduates), are not the typical example of this generation; rather they are at the cutting edge.

Model and Method

Both psychological empowerment and psychological flexibility have been studied quantitatively using scales. The Psychological Empowerment scale, known as the “Psychological Empowerment Instrument” was created by Spreitzer (1995) , and consists of 12 items divided into four factors, with each of these made up of three items. The original scale for measuring psychological flexibility was created by Hayes et al. (2004a , b) and consists of seven items. Subsequently, Bond et al. (2011) created the AAQ – II. Finally, Bond et al. (2013) created the WAAQ adaptation of the scale in a professional context.

However, this study does not aim to measure but rather better understand the generational component of each concept relating to current employees who are experiencing the pressures of a job market full of uncertainty and volatility. We were interested to understand perceptions of key aspects in their environment, both of themselves and of the possibilities within the world of work.

The focus groups were between one hour and an hour and a half long. They were led by the research team and were always organized around three key factors, which we could say are existential.

Figure 2 shows the general framework which all the focus groups were based on. The questions are illustrative; the aim was for the discussion in the group to flow naturally, while facilitating spontaneous access to the topics based on an open and trusting environment. All the groups did start with the same question: “How do you see the world in which you live in?” The moderator was responsible for facilitating the discussion, encouraging members to speak, asking overly talkative members to let others speak and encouraging all members to participate. In addition, the moderator was responsible for taking notes that may led to emerging questions. In this case, the moderator also presented to the participants of the focus group the questions that are shown in Figure 2 , only when it was necessary. In many cases, the group itself was generating the discourse ( Onwuegbuzie et al., 2009 ).

Figure 2 . Prepared by the authors. Examples of the questions asked to the employees in the focus groups.

The objective is to be able to analyze the consistency of the discourse, as well as identify elements of psychological empowerment and flexibility, based on the detailed discussion on the realities faced in the workplace, avoiding the more typical questions on empowerment and flexibility so as not to steer the participants and skew the results.

The analysis of the employees’ discussion content started with the creation of an initial matrix which uses all the concepts such as empowerment (meaning, competence, impact, and self-determination), as well as psychological flexibility (connection with the present, expressed values, committed actions, cognitive defusion, acceptance, self as context), separating self-attributions from external ones. This first classification filter was organized both by individual or personal self-attributions as well as groups or generations, and the same for the external attributions.

The research team adopted a form of discourse analysis inspired by Wetherell and Potter (1988) and Klevan et al. (2018) . Although presented as a step-by-step description, a strict sequence has not been followed. The identification of possible discourses in the text and how they are featured are better understood as constructs resulting from the back-and-forth movements between the steps, which were as follows:

(1)Read the text repeatedly to become familiar with the data.

(2)Coding of the sections in the material, focusing on the content of possible discourses and how they were expressed.

(3)organize the coded material into clusters according to the content and the way in which it was expressed.

(4)Organize the content clusters in possible discourses and finally.

(5)Question possible discourses in relation to each focus group with all the data as a whole, looking for possible patterns in terms of variations and consistency.

Following this, the data were summarized (separating units, grouping, and classifying elements), arranged, and transformed. Based on this initial transformation of the text corpus, an analysis was carried out in various stages of recurrent open coding for each category, in a continuous coding and categorization process in order to facilitate comprehensive analysis of the recurrent elements, the responses are organized and grouped into emergent categories.

Results and Discussion

The study carried out among these 16 focus groups brings into question the existence of significant differences between the four generations. The discussions gain differential consistency by being separated into two groups. During the analysis of the texts, it has been demonstrated that separation among youngsters, with little work experience (born after 1982, Gen Y and Gen Z) and older people, with greater experience and who have been working longer (born before 1982, BB and Gen X) generates greater and clearer variability between groups. Based on the data collected, it seems that differences are potentially related to the amount of personal and professional experiences that older people accumulate.

It is evident that in these two groups, the most extreme generations (older people about to retire – BB and young interns still in education – Gen Z) have a certain ability to be differentiated, and in some cases, it is possible to see some differences, although very specific between the four generations. In either case, it is necessary to highlight that a single characteristic has not been identified that is unique to each generation, and as with many other differential aspects, the variability within the group is greater than the variability between groups.

It is obvious that due to the simple fact of making this social categorization and activating it in terms of creating the focus groups (four in each of the generations), there is almost instantly homogenization with the group and differentiation between groups.

In addition, most groups manifested that the focus groups had contributed to increase their awareness of themselves as members of a specific generation and their capacity to influence in their jobs (psychological empowerment) and in their lives (psychological flexibility).

Psychological Empowerment

All the groups, regardless of their age or experience, have a good perception of themselves in terms of the relation between their competences and the work they carry out. In general, they see themselves as having the power and ability to instigate effective change in the world they have chosen, especially with regard to the meaning they give to their career path and the perception of their own competences. In both dimensions (competence and meaning), the discussions are truly positive.

It is important to mention that there is also a more negative discussion with regard to the lack of control and lack of awareness for the meaning of life, but it is a minority and not exclusive to any of the generational groups.

The meaning of life and work for older people (BB and Gen X) is based on their sense of responsability for what is going on in the world, as well as on what is going on in their workplace and home. They are people who feel and express the weight of responsibility over others, whether they are colleagues or children. In some instances, during the discussions, a sense of urgency is even detected with regard to the opportunity of improving things. They live and feel with free reign and they are the ones who have this meaning of life.

The youngest group (Gen Y and Gen Z) is very different. An idea that has been expressed frequently in the groups is that they have been charged with being the leaders of change. The purpose of their work is to change things, transform, and make all these bureaucratic, administrative and hierarchical processes more effective as they are making decision-making too slow. The objective of their work is to transform it, not only to improve it but also to make it fun and motivating.

Gen Y and Gen Z clearly identify as having less impact and being less capable of self-determination. This frequently manifests itself as a complaint, highlighting the obstacles they face in terms of empowerment, and also showing the contradictory nature of the “official discourse” on the importance of young talent, who also continuously face endless challenges emanating from a hierarchy they consider to be obsolete and out of place. In other cases, they do this by accepting they have less experience and therefore realize there is a need to have challenges and leaders who help them to improve their skills and power.

The approach that BB and Gen X take in terms of their impact and self-determination is much more active and satisfactory. They use more tools, skills, and capabilities, which helps to put them in a position of responsibility. In this sense, among these older people (who have a greater sense of perspective), it is more common for them to reflect on the relevance of their contributions and the ultimate impact they have had.

Multiple references to psychological flexibility variables have been found, although there is no clear differentiating discourse in an age group. It should be mentioned that by merely participating in the focus groups, this put our participants in a position where they “objectified their subjectivity” through the contrast in dialogue. This is an exercise (albeit one-off and planned), which Hayes et al. (2006) call “self as context.”. There were many diverse individual contributions, although no generational differences were found.

It was clear that, among all the generations, people were becoming aware of the job market conditions in Spain, although the way in which they approach this was as diverse as the people who made up the groups themselves.

The level of psychological flexibility among the participants across all generations can be improved. In all the groups, there is a lack of awareness in terms of being able to manage private events, a task which is difficult for everyone in this volatile and complex environment, something which all of the generations complain about. All the generations (including the youngest Gen Y and Gen Z) admit that they find the current uncertainty very challenging.

The biggest difference between the discussions took place again between BB and Gen X and Gen Y and Gen Z (younger people). BB and Gen X feel the need of taking charge of their lives, while for Gen Y and Gen Z, most of the discussion related to them being victims of a situation and a reality which moves them from one place to another and determines their current status.

The youngest generation, known as Gen Z, are the ones who most describe a situation linked to a crisis which defines them. This vital crisis or economic depression situation governs them and affects them even if they know they are very well prepared.

It was also seen among these youngsters (Gen Z) that they have had great success entering the job market, they are very critical and negative in terms of the learning and work environment they are experiencing, in which only their ability to innovate and distance themselves from situations will lead to success. This discourse on innovation and the autonomous search for resources was raised by a minority, and we understand that it has appeared as a result of having access to a sample of people who, by their special characteristics, have stood out and integrated into the job market successfully early on. Many of them even mentioned friends and family who had not as much “luck.” The general sentiment is that of complaint and regret, without delving any deeper.

Another difference which is evident, and which differentiates the two younger generations, is that in Gen Z there is a greater hunger for success and achievement, as well as more initiatives for developing alternative plans. They have seen how their older brothers and uncles, who despite having university degrees, have not been able to enter the job market, and as a result, they have always considered university to be insufficient and have sought complementary training.

In terms of accepting and confronting events faced by both young (Gen Y and Gen Z) and old (BB and Gen X), we can see differences which are clearly linked to people’s baggage and past experience. In general, there is a greater sense of accepting and confronting private events among people from BB and Gen X, without a doubt it is this experience which has taught them that it is better to take these on and confront them rather than avoid them. Gen Y and Gen Z see themselves as having more tools for avoiding these, and they even consider avoidance as being easier and more convenient due to the opportunities provided by new technology and networks. Due to the functional ubiquity of mobile devices, these youngsters have the option to never close down a line of action; they are involved in everything without giving up on anything, which seems like a way of avoiding confrontation. They complain that they do not have enough time or opportunities to deal with events in a reflective and profound manner.

It is important to understand that the young population (Gen Y and Gen Z) is entering the job market or has only recently entered. Furthermore, it is an extremely unstable and volatile market; the conditions are unfavorable for having an adequate self-perception within the context or associated defusion. They feel change and uncertainty.

There is also a difference again between BB and Gen X and Gen Y and Gen Z when it comes to articulating a coherent support between values and actions, something which is much more prevalent among older people (BB and Gen X), it seems that it is necessary to have a history of experiences which provide opportunities to reflect on the coherence and consistency between value and action. These experiences and learnings are evident among the older participants during the discussions and they are linked to values such as loyalty, commitment, and doing things properly.

Generational differences in the workplace have become a widely discussed topic in multiple publications in recent years, and there have also been countless experiences in human resources departments. It is also true that there is an open discussion on the suitability of this segmentation by generation ( Constanza et al., 2012 ; Lyons and Kuron, 2014 ). There are doubts as to whether this segmentation is explanatory or a significant enough source of behavioral diversity. It is not easy to distinguish the generational effects with the effects produced by age, maturity, and experience ( Twenge, 2000 ; Macky et al., 2008 ).

In this study, we have stated that it is these developmental elements which form the basis of the different discourses which have been expressed.

No differences have been found between the four proposed age groups, although clear differences have been found in the discussions with regard to psychological empowerment and psychological flexibility among employees born before 1982 (who as a result have more work and life experiences as is the case of BB and Gen X) and younger people who have few years of professional experience (Gen Y and Gen Z).

In terms of empowerment, both groups showed a positive self-image, although their empowerment was qualitatively different. Therefore, the role of their work within the wider population is determined by their responsibility for others and their work, and this responsibility has a sense of urgency. Among the younger population, work is important for achieving transformation and a different future.

Gen Y and Gen Z from our sample complain about the lack of self-determination as they consider themselves to be constrained by older people’s authority and the rules of bureaucratic structures, which they criticize heavily.

The differences in psychological flexibility are visible between older people (BB and Gen X) and younger people (Gen Y and Gen Z) who avoid confrontation, especially when it comes to interpersonal conflicts and giving up or not finding alternatives during decision-making. Therefore, youngsters have a greater ability for cognitive fusion between their thoughts and the reality in which they live, and they often feel like the victims.

Generational replacement is not a trivial topic in societies and organizations. Knowledge transfer is essential in order to secure and grow companies, and these should ensure that it takes place.

The focus groups carried out in this study have not shown clear differences between the four proposed generations, although there are many common themes as they all share the same cultural, economic and organizational situation. There have been more significant similarities and agreements than there have been differences. In many cases, these differences are a result of stereotypes which are more or less appropriate which have left a mark on society, and which tend to stereotype; as soon as the discussions became a bit longer and deeper, the differences once again become evident. There is, as has always been the case, a tension between the groups and people with experience (BB and Gen X) and those who want to get experience quickly (Gen Y and Gen Z). These two groups (young people and old people) have always existed and, although there are clear differences between them, the knowledge transfer between them remains as present as always, with the exception that in these “millennial times” this transfer is especially difficult and pressing.

The understanding of all these differences, based on age, may help companies to better use the psychological empowerment and psychological flexibility initiatives in order to facilitate the adaptation to the current VUCA environment. This understanding will be able to illuminate future strategic actions for Human Resources departments when facing the generational diversity challenges.

Data Availability

All datasets generated for this study are included in the manuscript and/or the supplementary files.

Ethics Statement

Ethical review process is not required as per the Spanish Law of Biomedical research 14/2007, July 3 since this is not a biomedical and clinic research. This study does not develop any clinical trials and does not involve patients; therefore, no written informed consents of the patients are required. This study is a qualitative research based on interviews and focus groups. The participants in these groups were informed according to the Spanish Law 5/1992, and all the information recorded in these groups was treated by anonymous form according to the previous referred law applicable to Spanish Universities. This study does not involve animal subjects.

Author Contributions

IS-D contributed to idea, redaction, and interviews. JL-F contributed to redaction, assessment, and conclusion. VD contributed to review, recommendations, and bibliography information.

This study has been founded by Comillas Pontifical University.

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

We are grateful to the focus groups for their encouragement of this study. Our thanks also go to experts in psychology for giving us the opportunity of working with them. Finally, we would like to thank the academic people for their contribution to this study.

1. In this article generations are named as BB, Gen X, Gen Y, and Gen Z.

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Keywords: psychological flexibility, psychological empowerment, generation, millennial, diversity

Citation: Sobrino-De Toro I, Labrador-Fernández J and De Nicolás VL (2019) Generational Diversity in the Workplace: Psychological Empowerment and Flexibility in Spanish Companies. Front. Psychol . 10:1953. doi: 10.3389/fpsyg.2019.01953

Received: 29 April 2019; Accepted: 08 August 2019; Published: 23 August 2019.

Reviewed by:

Copyright © 2019 Sobrino-De Toro, Labrador-Fernández and De Nicolás. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Víctor L. De Nicolás, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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Generational Differences at Work Are Small. Thinking They’re Big Affects Our Behavior

• Lisa Finkelstein,
• Courtney Thomas,
• Abby Corrington

Here’s what managers need to know.

Millennials only want to communicate with coworkers via text — and Baby Boomers don’t text, right? And you need to attract those techy Millennials with promises of flexible work schedules, but their older counterparts all want a traditional workday, correct? Actually, wrong. There’s very little evidence that people of different generations behave markedly differently at work, or want markedly different things. And yet because we have stereotypes about people of different ages — and because we have stereotypes about what we think people of different ages believe about us — our ability to collaborate and learn is negatively affected. To address this, managers need to talk openly about stereotypes; emphasize the commonalities and shared goals all employees have; and recognize that employees’ needs change over time, and in lots of different ways.

Look around your workplace and you are likely to see people from across the age span, particularly as more Americans are working past age 55 . In fact, the Society for Human Resource Management argues that there are a full five generations on the job today, from the Silent Generation to Gen Z.

• EK Eden King is the Lynette S. Autrey Professor of Industrial-Organizational Psychology at Rice University . She is pursuing a program of research that seeks to guide the equitable and effective management of diverse organizations. She has also partnered with organizations to improve diversity climate, increase fairness in selection systems, and to design and implement diversity training programs.
• LF Lisa Finkelstein is a professor in the social and industrial-organizational psychology area of the psychology department at Northern Illinois University and a fellow of the Society for Industrial and Organizational Psychology. She conducts research on diversity, stereotypes, and stigma at work, including age, disability, body weight, and gender, among others. She also studies mentoring relationships, high-potential designation, and humor at work.
• CT Courtney Thomas is a doctoral candidate in the Social-Industrial/Organizational program at Northern Illinois University. She conducts research on person perception related to topics like stereotyping, stigma, and diversity. While her research mainly focuses on the aging realm of diversity and inclusion, she also conducts research on other stigmatized identities like disability and obesity.
• AC Abby Corrington is a fifth-year graduate student who spent time in the corporate world prior to joining the Industrial/Organizational Ph.D. program at Rice University. She conducts research on the different ways that people express and remediate discrimination. She has received several grants for her work and has published in Journal of Vocational Behavior and Equality, Diversity, and Inclusion.

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Generations and Generational Differences: Debunking Myths in Organizational Science and Practice and Paving New Paths Forward

• Original Paper
• Published: 04 September 2020
• Volume 36 , pages 945–967, ( 2021 )

Cite this article

• Cort W. Rudolph   ORCID: orcid.org/0000-0002-0536-9638 1 ,
• Rachel S. Rauvola 2 ,
• David P. Costanza 3 &
• Hannes Zacher 4  

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Talk about generations is everywhere and particularly so in organizational science and practice. Recognizing and exploring the ubiquity of generations is important, especially because evidence for their existence is, at best, scant. In this article, we aim to achieve two goals that are targeted at answering the broad question: “What accounts for the ubiquity of generations despite a lack of evidence for their existence and impact?” First, we explore and “bust” ten common myths about the science and practice of generations and generational differences. Second, with these debunked myths as a backdrop, we focus on two alternative and complementary frameworks—the social constructionist perspective and the lifespan development perspective—with promise for changing the way we think about age, aging, and generations at work. We argue that the social constructionist perspective offers important opportunities for understanding the persistence and pervasiveness of generations and that, as an alternative to studying generations, the lifespan perspective represents a better model for understanding how age operates and development unfolds at work. Overall, we urge stakeholders in organizational science and practice (e.g., students, researchers, consultants, managers) to adopt more nuanced perspectives grounded in these models, rather than a generational perspective, to understand the influence of age and aging at work.

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People commonly talk about generations and like to make distinctions between them. Purported differences between generations have been blamed for everything from declining interest in baseball (Keeley, 2016 ) to changing patterns of processed cheese consumption (Mulvany & Patton, 2018 ). In the workplace, generations and generational differences have been credited for everything from declining levels of work ethic (e.g., Cenkus, 2017 ; cf. Zabel, Biermeier-Hanson, Baltes, Early, & Shepard, 2017 ), to higher rates of “job-hopping” (e.g., Adkins, 2016 ; cf. Costanza, Badger, Fraser, Severt, & Gade, 2012 ). Despite their ubiquity, a consensus is coalescing across multiple literatures that suggests that all the attention garnered by generations and generational differences (e.g., Lyons & Kuron, 2014 ; Twenge, 2010 ) has been “much ado about nothing” (see Rudolph, Rauvola, & Zacher, 2018 ; Rudolph & Zacher, 2017 ). That is to say, the theoretical assumptions upon which generational research is based have been questioned and there is little empirical evidence that generations exist, that people can be reliably classified into generational groups, and, importantly, that there are demonstrable differences between such groups that manifest and affect various work-related processes (Heyns, Eldermire, & Howard, 2019 ; Jauregui, Watsjold, Welsh, Ilgen, & Robins, 2020 ; Okros, 2020 ; Rudolph & Zacher, 2018 ; Stassen, Anseel, & Levecque, 2016 ). Indeed, a recent consensus study published by the National Academies of Sciences, Engineering, and Medicine (NASEM) concluded that “Categorizing workers with generational labels like ‘baby boomer’ or ‘millennial’ to define their needs and behaviors is not supported by research, and cannot adequately inform workforce management decisions…” (NASEM, 2020a ; see also NASEM, 2020b ).

Of equal importance to the theoretical limitations, common research methodologies used to study generations cannot unambiguously identify the unique effects of generations from other time-bound sources of variation (i.e., chronological age and contemporaneous period effects). Given all of this, some have argued that there has never actually been a study of generations (Rudolph & Zacher, 2018 ), and thus, the entire body of empirical evidence regarding generations is, to a large extent, wrong. Still, it is easy to find examples of empirical research that claim to find evidence in favor of generational differences (e.g., Dries, Pepermans, & De Kerpel, 2008 ; Twenge & Campbell, 2008 ; Twenge, 2000 ; see Costanza et al., 2012 , for a review) and theoretical advancements that aim to direct such empirical inquiries (e.g., Dencker, Joshi, & Martocchio, 2008 ). Moreover, some see generations as a useful heuristic in the process of social sensemaking: generations are recognized as social constructions, which help give meaning to the complexities and intricacies of aging and human development in the context of changing societies (e.g., Campbell, Twenge, & Campbell, 2017 ; Lyons, Urick, Kuron, & Schweitzer, 2015 ).

Considering all of this, we are faced with a variety of competing and contradictory issues when trying to sort out what bearing, if any, generations have on organizational science and practice. On the one hand, evidence for the existence of generations and generational differences is limited. On the other hand, the idea of generations is pervasive and is used to explain myriad patterns of thinking, feeling, and behaving that we observe day-to-day, especially in the workplace. Thus, there exists a tension between what science “says” about generations and what people “do” with the idea of generations. Given this, the continued popularity of generations as a means of understanding work-related processes is worthy of closer investigation. This popularity begs the question, “What accounts for the ubiquity of generations, despite a lack of evidence for their existence and impact?” This manuscript explores two answers to this question.

One answer to this question is a lack of knowledge about what the science of generations tells us, leading to misunderstandings of the evidence about generations, their existence, and their purported impact. Thus, the first goal of this article will be to review and debunk ten common myths about generations and generational differences at work and beyond. A second answer to this question is a lack of knowledge regarding, and exposure to, alternative theoretical explanations for understanding (a) the role of age and aging at work and (b) the persistence of generations as a tool for social sensemaking. More specifically, we argue that, owing to a lack of knowledge about alternative explanations and supported by their ubiquity and popular acceptance (e.g., in the popular business and management press; see Howe & Strauss, 2007 ; Knight, 2014 ; Shaw, 2013 ), generations are more often than not the “default” mode of explanation for complex age-related phenomena observed in the workplace and beyond (e.g., because they are familiar and comfortable explanations, which are easy to adopt, and seem legitimate on their face).

Accordingly, the second goal of this paper is to further advance two alternative models for understanding age and aging at work that do not rely on generational explanations and that can explain their existence and popularity—the social constructionist perspective and the lifespan development perspective. This is an important contribution, because simply pointing out the obvious pitfalls of generations and generational explanations can only go so far toward changing the way that people think about, talk about, study, and enact practices that involve generations. Just advising people to drop the idea of generations without providing alternative models would be counterproductive to the goal of enhancing the credibility of organizational science and practice. Thus, our hope is that by providing workable alternatives to generations, researchers and practitioners will be encouraged to think more carefully about the role of age and the process of aging when enacting the work that they do.

The social constructionist perspective offers that generations and differences between them are constructed through both the ubiquity of generational stereotypes and the socially accepted nature of applying such labels to describe people of different ages (e.g., consider the recent “OK Boomer” meme; Hirsch, 2020 ). The social constructionist perspective helps address and explain the question of why generations are so ubiquitous. Complementing this, the lifespan perspective is a well-established alternative to thinking about the process of aging and development that does not require one to think in terms of generations. The lifespan perspective frames human development as a lifelong process which is affected by various influences—not including generations—that predict developmental outcomes. Despite its longstanding role in research on aging at work (e.g., Baltes, Rudolph, & Zacher, 2019 ), the lifespan perspective has been infrequently considered as an alternative model to generations, perhaps because it has not often been treated in accessible terms.

These complementary approaches—the social constructionist and the lifespan development perspective—offer alternative paths forward for studying age and age-related processes at work that do not require a reliance on generational explanations. Thus, as described further below, these perspectives by-and-large circumvent the logical and methodological deficiencies of the generations perspective. They also offer actionable theoretical and practical guidance for identifying the complexities involved in understanding age and aging at work.

First, we outline and “bust” ten myths about generations and generational differences (see Table 1 for a summary). These myths were chosen in particular, because we deemed them to be the most pressing for research and practice in the organizational sciences, broadly defined, in that they reflect commonly highlighted topics, and bear potential risks if not properly addressed. Then, we introduce and outline the core tenets of the social constructionist and lifespan development perspectives, giving examples of how their applications can complement each other in supplanting generational explanations in both science and practice. Finally, we conclude by drawing lines of integration between these two perspectives, in the hopes that these alternative ways of thinking will inspire researchers and practitioners to adopt alternatives to thinking about aging at work in generational terms.

Debunking Ten Myths About Generations in Organizational Science and Practice

Myth #1: generational “theory” was meant to be tested.

The sheer number of empirical studies purporting to test generational “theory” would suggest that such theory was intended for testing. However, this is far from the case. The concept of generations as we know it stems from early functionalist sociological thought experiments, derived from foundational work by Mannheim (1927/ 1952 ) and others (e.g., Ortega y Gasset, 1933 ; see also Kertzer, 1983 ). Adopting the term in a largely historical, rather than familial or genealogical, sense, these authors offered “generations” as social units that account for broad societal and cultural change. Generations were suggested to emerge through “shared consciousness,” which developed across individuals (e.g., those at similar life stages) after common exposure to formative events (e.g., political shifts, war, disaster; see Ryder, 1965 ). This consciousness, in turn, was theorized to shape unique values, attitudes, and behaviors that characterize a given generation’s members, especially to distinguish one generation from its predecessor. These attributes subsequently impact how these individuals interact with and influence society.

Here, a tautology emerges: culture begets generations and generations beget culture. This is a potentially useful perspective for describing macro-scale interactions between social groups and the social environments in which they live—that is, it is useful as a functionalist sociological mechanism, as the concept of generations was intended. However, this perspective also implies that culture, and the generational groups it forms and is formed by, cannot be disentangled. Generational “theory” is not falsifiable, nor was it intended to be. Attempts to empirically study generations have extended these ideas into positivist and deterministic practices for which they were not intended. Even life course research (e.g., Elder, 1994 ), which centers on the impact of social change and forces on individuals’ lives as opposed to societal change, does not directly “test” for generational differences, per se. Instead, it uses generations conceptually in explicating the roles that historical, biological, and social time play in life trajectories.

In fact, Mannheim’s (1927/ 1952 ) work was partly a critique of the overemphasis on absolutist/biological perspectives in the study of social and historical development, including the objective treatment of time (Pilcher, 1994 ). This makes it all the more puzzling and problematic that generational “theory” has been applied to discrete quantitative increments (i.e., age and year ranges to define cohorts), and in a fashion that ignores the “non-contemporaneity of the contemporaneous” (i.e., the fact that being alive at the same time, or even being alive and of a similar age at the same time, does not mean history is experienced uniformly; Troll, 1970 , p. 201). When considering the roots of “generations,” it is apparent that the concept has been re-characterized and misappropriated.

Myth #2: Generational Explanations Are Obvious

One appealing, if overstated, quality of generations is that there are unique characteristics that are (assumed to be) associated with various cohorts. Moreover, it is assumed that lines can be drawn between generations to distinguish them from one another on the basis of such characteristics. These characteristics, which are said to be influenced by the various events that supposedly give rise to generations in the first place, “make sense” in a way that give generations an air of face validity. For example, it seems very rational and indeed quite self-evident to many that living through the Great Depression made the Silent Generation more conservative and risk-avoidant and that helicopter parents and the rise of social media made Millennials narcissistic and cynical. These and other observed social phenomena such as job-hopping and materialism are frequently ascribed to generations. However, looking more deeply into the identification of these critical events, as well as the mechanisms by which generations supposedly emerge, reveals a far more complex, nuanced picture than a generational explanation would have us believe.

In order to understand why the events that created generations may, or may not, have been impactful, it is important to understand how the critical events purported to give rise to them are identified. As one example, in their popular book, Strauss and Howe ( 1991 ) offer a taxonomy of generations, developed by tracing historical records in search of what they called “age-determined participation in epochal events…” (p. 32). To Strauss and Howe, such events were deemed to be so critical that they contributed to the creation of a unique generation. This post hoc historical demographic approach benefits from the passage of time: it is far easier to identify critical events retrospectively, rather than when they are actually occurring. Although major events like economic depressions and wars likely qualify as epochal, dozens of other events have been proposed to be critical in the formation of generations, only to fade into historical oblivion within a matter of a few years.

For example, in defining supposedly seminal events in the development of the Millennial generation, Howe and Strauss ( 2000 ) cite the case of “Baby Jessica” (n.b. on October 14, 1987, 18-month-old Jessica McClure Morales fell into a well in her aunt’s backyard in Midland, Texas. After 56 h, rescue workers eventually freed her from the 8-in. well casing 22 ft below the ground; Helling, 2017 ). Why this event should help form a generation is uncertain, as is whether or not Millennials were or have been systematically impacted by her saga and subsequent rescue.

Rather than being obviously generational, explanations for many social phenomena are more likely to be associated with age or period effects, both of which are other time-based sources of variation that are often conflated with generational cohorts. Specifically, there are three sources of time-based variation that need to be accounted for to make claims about generations: age, period, and cohort effects (see Glenn, 1976 , 2005 ). Age effects refer to variability due to time since birth, in that chronological age is simply an index of “life lived” (e.g., Wohlwill, 1970 ). Period effects refer to variability due to contemporaneous time and refer to the effects of a specific time and place (i.e., the year 2020). Finally, cohort effects are those that are typically taken as evidence for generations, referring to the year of one’s birth. To make claims about generations, therefore, it is necessary to rule out the effect of age (i.e., developmental influences) and period (i.e., contemporaneous contextual influences).

There are numerous examples of how these sources of variability are conflated and confused with one another. Consider that popular press accounts of Millennials have until recently painted them to be dedicated urban dwellers who favored ride-sharing services and eschewed traditional families (e.g., Barroso, Parker, & Bennet, 2020 ; Godfrey, 2016 ). However, adults in this age range have more recently been observed moving to the suburbs, buying houses and cars, and having children (e.g., Adamczyk, 2019 ). This is not a generational effect but rather a phenomenon attributable to the fact that Millennials are reaching the normative age where people get married, start families, and purchase houses. This is a product of age and context, not generation or period. The picture becomes even more complex given other contextual factors not necessarily bound to time, for example, when considering that the average age of first conception is higher in urban, compared to rural, areas (Bui & Miller, 2018 ).

Another example comes from data showing that high school and college students are less likely to hold summer jobs today than 20 years ago (Desilver, 2019 ). This is not a generational effect, but rather is attributable to contemporaneous economic conditions. As a final example, after 9/11, there was a modest increase in the number of people enlisting in the United States Army, which is an example of a period effect (Dao, 2011 ). However, this change has also been misattributed in various ways to a generational effect (e.g., Graff, 2019 ). Notably, in ~ 2019 (i.e., when those born in ~ 2001 turned ~ 18 and were eligible to join the army), there were historically low rates of enlistment (Goodkind, 2020 ). If this rate had been particularly high, one might conclude evidence for a generational effect, such that people born in 2001 grew up in a time and place that demanded enlistment. However, this is not the case—growing up in a post 9/11 world did not make this cohort more likely than others to join the army.

In summary, whereas certain historical events might be easily identifiable as epochal, the extent to which recent events are defined as such might not be known for some time. Moreover, this idea assumes that epochal events actually matter for the formation of distinct generations, a key argument in generations theory that is by-and-large untested, and indeed untestable. Moreover, consider that “global” events (i.e., those that affect all members of a population regardless of age, not just those born in a particular time and place, like a global pandemic) almost certainly manifest as period, not generational cohort effects (Rudolph & Zacher, 2020a , 2020b ). Generations and the events that are purported to give rise to them are far from obvious and to attribute current individual characteristics to the occurrence of specific events is misguided. Furthermore, many of the “obvious” generational effects often attributed to such events are much more likely due to other factors associated with age and/or period.

Myth #3: Generational Labels and Associated Age Ranges Are Agreed Upon

Whereas generational labels are well-known and widely recognized, the specific birth year ranges that define each generational grouping and the consistency with which such groupings are applied across time, studies, and location, vary substantially. For example, Smola and Sutton ( 2002 , p. 364) identified a great deal of variation in the start and end years that define different generational groups and the names used to describe various generations, noting “generations…labels and the years those labels represent are often inconsistent” (p. 364).

In their meta-analysis, Costanza et al. ( 2012 ) found similar discrepancies with variations in start and end dates ranging from 3 to 9 years depending on the study, the variables of interest, and the source of the generational year ranges being used. Similar conclusions were reached by Rudolph et al. ( 2018 ) in their review of generations in the leadership literature.

Beyond these definitional inconsistencies, there are notable differences in the way researchers address cross-cultural variability in generational research. The ubiquity of the labels and their pervasiveness in the literature has led researchers from countries other than the USA to use labels (e.g., “Baby Boomers”) when doing so does not make sense, as the events that supposedly influenced individuals and gave rise to these generations in the first place clearly differ from country to country. Moreover, consider that the term “Millennials” is not meaningful in countries that use Chinese, Islamic, Jewish, Buddhist, Sakka, or Kolla Varsham calendars (Deal, Altman, & Rogelberg, 2010 ) and that generations are often labeled based on political or cultural events and epochs. For instance, members of the Greek workforce have been categorized into the Divided Generation, the Metapolitefsi Generation, and the Europeanized Generation (Papavasileiou, 2017 ). In Israel, generations are identified by wars and thus have shorter ranges (Deal et al., 2010 ). The German media has variously labeled younger people as Generation C64, Generation Golf, or Generation Merkel. In China, generations are pragmatically called the Post-50s generation, Post-60s Generation, and so on, whereas in India, the three main generational groups are labeled Conservatives, Integrators, and Y2K (Srinivasan, 2012 ).

One approach researchers have adopted for dealing with the complexities of cross-cultural variation in generational labeling is to ignore the issue and simply use US-based generational labels and years when studying individuals in other countries. For example, Yigit and Aksay ( 2015 ) looked at Turkish Gen X and Gen Y health professionals, roughly using US date ranges for these groups. A second approach has been to use the date ranges associated with US generations but assign country-specific labels to those same periods. Utilizing this approach, Weiss and Zhang ( 2020 ) picked birth year ranges and adopted or developed generational labels in three different countries. For example, for the years 1946–1965, they labeled the generations as the “68er Generation” in Germany, “Baby Boomer” in the USA, and the “New China Generation” in China. A third approach has been to develop country-specific generational groups based on local events that impacted people in that county, a strategy used by To and Tam ( 2014 ) who identified four distinct post-WWII generations in China.

Inconsistencies in labeling have significant conceptual and computational implications for the study and understanding of generations and especially so if one wishes to conduct comparative cross-national and/or cross-cultural research. Importantly, we would argue that the validity of the generations concept and its utility for understanding individual, group, and organizational phenomena is very limited due to a number of factors, including (a) researchers’ inability to agree on the start and end dates for different generations; (b) inconsistencies in the classification and labeling systems that characterize them; (c) disagreement on the specific significant influencing events that supposedly gives rise to them, such as the extent to which the timing of events plays a role, including the length of time that is associated with their influence, and the lag required to observe such influences; and (d) the issue of cross-cultural equivalencies. As such, defining generations represents a moving target, which is a significant liability for science and evidence-based practice.

Myth #4: Generations Are Easy To Study

Although there have been numerous attempts to study generations and generational differences, it is clear that these phenomena have not been studied very well. Indeed, it is not only difficult to study generations as they have been framed in the literature but also impossible. As noted above, research on generations is typically based upon birth year ranges, which is to say that they are derived from information about birth cohorts. A common problem emerges when one tries to study cohort effects in cross-sectional (i.e., single time point) research designs, which are the most commonly applied designs used to make inferences about generations (see Costanza et al., 2012 ). Namely, age, period, and cohort effects are confounded with each other in such designs.

This confounding is best understood through an example. Let us assume that a hypothetical cross-sectional study is conducted in the year 2020 (i.e., the year constitutes the “period effect” in this case). If we reduce the logic of generations a bit and define a cohort effect in terms of a single birth year (e.g., those born in 1980), then the effect of age (i.e., time since birth; 40 years) is completely confounded with cohort. This is because:

In this example, any differences that researchers observe as a function of assumed cohort variability may instead be due to the age of the individuals when they were studied. This pattern would likewise be extrapolated to any age–cohort combinations studied in a single period. The linear dependency among the three effects means that unique effects of age cannot be separated from whatever cohort effect might exist and vice versa.

One common attempt to circumvent this confounding is to artificially group members of different cohorts together to form generational groups. However, this practice is likewise fraught with the same issues raised just above. Another hypothetical cross-sectional study helps to illustrate why: in this study, let us assume that we want to define two arbitrary groupings of birth cohorts, representing people born between 1981 and 1990 (“Generation A”) and those 1991–2000 (“Generation B”), to disentangle age and cohort effects from one another. The variability due to birth cohort in each generation is 10 years; however, as in our previous example, the age range within cohorts is likewise 10 years. Thus, this approach does little to solve the dependency other than shifting the scaling of age. As the rank order of cohort versus age has not changed (relatively older people are in “Generation A” and relatively younger people are in “Generation B”), there is still a correlation between age and generational groups in this study. Moreover, this approach has other limitations, including the loss of statistical power to detect age effects (see Rudolph, 2015 ) and a confusing logic of cohort versus age effect interpretations (e.g., the oldest members of “Generation A” are closer in age to the youngest members of “Generation B” than to the average age of their own generational group).

From a research design standpoint, this issue of confounding represents an unresolvable problem, which has long been known and lamented in the literature (e.g., Glenn, 1976 , 2005 ). Other research designs are unfortunately no better geared than cross-sectional designs to address this issue, or they do not address variability in cohort effects at all. For example, in typical longitudinal designs, cohort effects are held constant (i.e., from the first time point, people’s birth year does not vary) and period is allowed to vary (i.e., as data are collected from the same people across multiple time points). However, in such designs, period effects are conflated with age (i.e., as people “get older” across time). Expanded longitudinal approaches, such as cohort sequential designs (e.g., sampling 20-year-olds at each time point, T 1 − T k , adding successive cohorts of 20-year-olds at each time point) may be able to separate age/aging from period and cohort effects, depending on how “cohort” is defined. However, such studies require immense resources and time (e.g., 20+ years or more of data collection, including long-term data management and subject retention efforts; see Baltes & Mayer, 2001 ). As such, and perhaps not surprisingly, we are unaware of any applications of such designs to the study of generations at work.

An alternative that has been employed by some researchers (e.g., Twenge, Konrath, Foster, Campbell, & Bushman, 2008 ) is a cross-temporal approach, often employing time-lagged panels or cross-temporal meta-analyses (discussed further below). Cross-temporal approaches use data collections from members of different cohort groups, collected during different periods, holding age constant (e.g., data from panels of 25-year-olds and 50-year-olds collected in 2000, 2010, and 2020 or research done on college students every year from 1990 to the present). The logic of cross-temporal methods is to compare groups of similarly aged individuals (i.e., to “control” for age by holding its value constant) across time and then argue that cohort effects are more likely the cause of any observed differences than period effects. Among other issues, cross-temporal approaches have been criticized for their reliance on ecological correlations (i.e., correlations among variables that represent group means) and design assumptions (see Trzesniewski & Donnellan, 2010 ; Trzesniewski, Donnellan, & Robins, 2008 ) raising significant concerns about them as a way to study generations. Specifically, ecological correlations can misrepresent relationships when contrasted with correlations among individual observations (see Robinson, 1950 ).

Overall, the methodological and design challenges associated with studying generations are substantial and the conceptualization of generations as the intersection of age and period makes them impossible to study. Thus, studying generations is only “easy” to the extent that one is willing to ignore the issues raised here. Given these concerns, we echo the recommendations of Rudolph and Zacher ( 2017 ), who suggest that “…both research and practice would benefit from a moratorium on time-based operationalizations of generations as units for understanding complex dynamics in organizational behavior” (p. 125).

Myth #5: Statistical Models Can Help Disentangle Generational Differences

Given the design challenges noted above, it is perhaps not surprising that researchers have tried a variety of statistical techniques to resolve the age, period, and cohort confounding problem. Unfortunately, the great majority of generational studies to date have employed the least useful approach to doing so, pairing cross-sectional designs with comparisons of generational cohort means (e.g., typically via linear models, such as t tests or other variants of ANOVA-type models). As noted, cross-sectional approaches control for period effects but confound cohort and age effects with one another and this confounding cannot be resolved statistically through any means. To be clear, this is not a function of a lack of innovation regarding statistical modeling techniques. On the contrary, as long as age, period, and cohort are defined in time-related terms, they will be inextricably confounded with one another in cross-sectional research designs.

With respect to cross-temporal approaches, some researchers have implemented a specific technique referred to as “cross-temporal meta-analysis” (CTMA). CTMA shares certain features with traditional meta-analysis (e.g., studies assumed to be representative of a population of all possible studies on a given phenomenon are taken from the literature and synthesized). In a typical CTMA, age is more or less held constant by narrowing the sampling frame of studies included (e.g., by only considering studies of college age students). By holding age constant and looking at the effects of time on outcomes (i.e., by considering the relationship between year of publication and mean levels of a given phenomenon derived from contributing studies), CTMA models change over time in a phenomenon. However, although age is to some extent held constant, recall that cross-temporal methods inherently confound period and cohort effects with one another. Thus, any identified cohort effect cannot be unambiguously separated from period effects in CTMA. Although research employing CTMA has argued that generations are more likely than period effects to explain observed differences, such work also recognizes that period effects are equally likely explanations for any results derived therefrom (e.g., Twenge & Campbell, 2010 ). Furthermore, a recent paper by Rudolph, Costanza, Wright, and Zacher ( 2019 ) used Monte Carlo simulations to test the underlying assumptions of CTMA, finding that it may misestimate cohort effects by a factor of three to eight times, raising questions about both the source and magnitude of any differences identified.

A final analytic technique that has been occasionally employed to disentangle age, period, and cohort effects is cross-classified hierarchical linear modeling (CCHLM; Yang & Land, 2006 , 2013 ). Applying CCHLM to generational research, age is treated as a fixed effect and period and cohort are allowed to vary as random effects. Importantly, however, decisions about how such effects should be specified are somewhat arbitrary, because it is also possible that cohort and period could be fixed and age random in the population, resulting in different outcomes and conclusions from such models that are largely dependent on analytic decisions rather than reflecting “true” population effects. Thus, without generally unknowable insights into “what” to hold constant in estimating such models, CCHLM results in ambiguous parameter estimates for age, period, and cohort effects.

To this end, a series of simulation studies by Bell and colleagues (Bell & Jones, 2014 ; see also Bell & Jones, 2013 , for further commentaries) has shown that the Yang and Land methodology for separating age, period, and cohort effects simply does not “work.” Even ignoring this issue, CCHLM does little to solve the problem of age, period, and cohort confounding, because the three variables are still linearly dependent upon each other and hence computationally inseparable. Something (typically age) has to be held constant in such models to separate these variables from one another, and even then, ambiguities in how to interpret confounded effects of period and cohort still abound. In short, none of the statistical techniques that have been used to study generations can fully separate age, period, and cohort effects (see Costanza, Darrow, Yost, & Severt, 2017 , for a full discussion) and cannot solve the conceptual or design problems noted earlier. This known issue has befuddled social scientists for quite some time. For example, more than 40 years ago Glenn ( 1976 ) referred to this problem as “a futile quest.”

Myth #6: Generations Need To Be Managed at Work

Given the proliferation of research and popular press articles identifying generational differences, it is not surprising that practitioners and academics have suggested that people in different generations need to be managed differently at work (e.g., Baldonado, 2013 ; Lindquist, 2008 ). There are two main problems with these recommendations.

First, as has been noted, research generally does not and cannot support the existence of generational differences. Conceptual, theoretical, methodological, and statistical issues abound in this literature, and absent clear, convincing, and valid evidence for the existence of generational differences, there is no justification for managing individuals based on their supposed generational membership (NASEM, 2020a , 2020b ; Rudolph & Zacher, 2020c ). Eschewing the notion of generations does not mean that one must ignore that individuals change over the course of their lifespan or that their needs at different stages in their careers will vary. However, it is important to note that there is not a credible body of evidence to suggest that such changes are generational or that they should be managed as “generational differences” at work.

Indeed, as already noted, much of what lay people observe as “generational” at work is likely more accurately attributed to either age or career stage effects masquerading as generational differences. There is a broad and well-supported literature on best practices for HR, leadership, and management (e.g., Kulik, 2004 ) and customizing policies and practices based on those recommendations rather than generational stereotypes makes much more sense. Furthermore, there is a burgeoning literature on the positive influence that age-tailored policies (e.g., age-inclusive human resource practices that foster employees’ knowledge, skills, and abilities, motivation, effort, and opportunities to contribute, irrespective of age) for building positive climates for aging at work and supporting worker productivity and well-being (see Böhm, Kunze, & Bruch, 2014 ; Rudolph & Zacher, 2020d ). For example, research suggests that workers of all ages benefit from flexible work policies that allow for autonomy in choosing the time and place where work is conducted (see Rudolph & Baltes, 2017 ).

Second, as alluded to earlier, management strategies that are based on generations have the potential to raise legal risks for organizations. For example, in the USA, provisions of The Civil Rights Act of 1964, the Age Discrimination in Employment Act of 1967, and the Americans with Disabilities Act of 1991 disallow the mistreatment of individuals from certain groups based on a variety of characteristics. Although generational membership is not directly covered by such legislation, under the ADEA, age is a protected class for workers aged 40+. Given the conflation of generational effects with age, life, and career stage, employment-related decisions tied to generations could be interpreted as prima facie evidence of age-related discrimination (e.g., Swinick, 2019 ). Indeed, organizations that market themselves to and build personnel practices around generations and generational differences have been implicated in age discrimination lawsuits (e.g., Rabin vs. PriceWaterhouseCoopers, 2017 ). Combined with the absence of valid studies supporting generationally based differences, organizations open themselves up to an unnecessary liability if they manage individuals based on generational membership (Costanza & Finkelstein, 2015 ; for a related discussion of various policy implications of managing generations, see Rudolph, Rauvola, Costanza, & Zacher, 2020 ).

Recently, Costanza, Finkelstein, Imose, and Ravid ( 2020 ) reviewed the applied psychology, HR, and management literatures looking for studies about how organizations should manage generations in the workplace. They identified a range of inappropriate inferences and unsupported practical recommendations and systematically refuted them based on legal, conceptual, practical, and theoretical grounds. We echo their conclusion here, regarding advice from managing based on generational membership (p. 27): “Instead of customizing HR policies and practices based on such [generational] differences, organizations could use information about their overall workforce and its characteristics to train recruiters, develop and refine policies, and offer customizable benefits packages that appeal to a broad range of employees, regardless of generation.”

That said, we do not think that the idea of generations should be ignored altogether in the development of management strategies. Instead, the focus should be shifted away from managing assumed differences between members of different generations and toward managing perceptions of generations and generational differences. Considering evidence that people’s beliefs and expectations about age and generations feed into the establishment of stereotypes that interfere with work-relevant processes (e.g., King et al., 2019 ; Perry, Hanvongse, & Casoinic, 2013 ; Raymer, Reed, Spiegel, & Purvanova, 2017 ; Van Rossem, 2019 ), this is a particularly important consideration and is, in and of itself, a topic worthy of further study.

Myth #7: Members of Younger Generations Are Disrupting Work

While it may feel “new” to blame members of younger generations for changes in the work environment, this is a form of uniqueness bias: we think our beliefs and experiences are new, when in reality similar complaints have been levied against relatively younger and older people for millennia. Indeed, generationalized beliefs about the inflexibility and “out of touch” nature of older generations, or the laziness, self-centeredness, and entitlement of younger generations, have repeated with remarkable consistency across recorded history (Rauvola, Rudolph, & Zacher, 2019 ). One of the more obvious examples is in referring to generations with self-referent terminology: New York Magazine wrote about youth in the so-called “Me” Decade (Wolfe, 1976 ) over 30 years prior to Twenge’s ( 2006 ) work on “Generation Me,” Time Magazine’s (Stein, 2013 ) publication on the “Me Me Me” generation, and even the British Army’s recent use of the phrase “Me Me Me Millennials…Your Army needs you and your self belief” in recruitment ads (Nicholls, 2019 ).

Lamentations about young people “killing things” are far from radical as well. Modern claims are made about youth ending an absurd number of facets of life, ranging from institutions such as marriage and patriotism to household products like napkins, bar soap, and “light” yogurt (Bryan, 2017 ). Moreover, similar concerns have been voiced throughout the years regarding the rise and fall of consumer preferences, including concerns about young people upending and revolutionizing romantic relationships and transportation (e.g., Thompson, 2016 ), or being corrupted by new forms of popular media like the radio in the 1930s (Schwartz, 2015 ).

A more realistic explanation exists for both shifts in consumer preferences as well as changes and disruptions in the nature of work: the contemporaneous environment, and innovations and unexpected changes therein. To take a recent example, the global COVID-19 pandemic has tremendously impacted and transformed how and where work is conducted (Kniffin et al., 2020 ; Rudolph et al., 2020 ). While “non-essential” workers are conducting more work virtually and with more flexible hours, other workers deemed “essential” are working in environments with new health and safety protocols and often with different demands and resources in place (e.g., with respect to physical equipment, coworker and customer contact). Even more workers have been furloughed or laid off altogether, with the need to turn to alternative forms of work to maintain income or, when feasible, resorting to early retirement (see Bui, Button, & Picciotti, 2020 ; Kanfer, Lyndgaard, & Tatel, 2020 ; van Dalen & Henkens, 2020 ).

These changes have led to a dramatic pivot for many organization, managers, and individual workers, far surpassing the speed and degree to which more gradual, “generational” workplace changes have supposedly occurred. Not only this, but such changes have had outcomes for workers and society that contradict what generational hypotheses would predict. For example, generational stereotypes suggest that relatively older workers would struggle with technological changes at work while relatively younger workers would thrive. However, the move to work-from-home arrangements has resulted in positive benefits for some, including helpful and flexible accommodations, or health and safety protections, as well as new challenges for others, such as the need to balance childcare or eldercare with work while at home, while still others face newfound isolation and lack of in-person social support coupled with great uncertainty (Alon, Doepke, Olmstead-Rumsey, & Tertilt, 2020 ; Douglas, Katikireddi, Taulbut, McKee, & McCartney, 2020 ). These changes create a diverse set of advantages and disadvantages for individuals of all ages. Rather than blaming those of younger generations for disrupting work and life more generally, societal trends and events are a more appropriate, fitting, and ultimately addressable explanation (i.e., through non-ageist interventions and policies).

Myth #8: Generations Explain the Changing Nature of Work (and Society)

Generations are an obvious and convenient explanation for the changing nature of work and societies. However, as discussed previously, convenience and breadth in applying generational explanations does not translate into validity. Because they can easily and generally be applied to explain age-related differences, generations give a convenient “wrapper” to the complexities of age and aging in dynamic environments (i.e., both within and outside of organizations). However, this wrapper restricts and obscures the complexities inherent to both individuals and the environments in which they operate. Generations are highly deterministic, suggesting that individuals “coming of age” at a particular time (i.e., members of the same cohort) all experience aging and development uniformly (i.e., cohort determinism; Walker, 1993 ). With so many other demonstrable age-related and person-specific factors (e.g., social identities, personality, socioeconomic status) that have bearing on individuals’ attitudes, values, and behaviors, as well as how these interact with contextual and environmental influences, the prospect of generations overriding all such explanations is implausible. Assuming otherwise wipes away a tremendous amount of potentially useful detail and heterogeneity.

Moreover, this perspective stipulates that events in a given time period impact younger people and not older people, such that historical context only influences individuals up to a certain (early) point in their development. This aligns with the idea that identity is “crystallized” or “ratified” at a certain age and development or change is more or less halted thereafter (Ryder, 1965 ). However, ample evidence suggests that this is far from the case, with age-graded dynamics in such areas as personality emerging across the breadth of the lifespan (e.g., Bianchi, 2014 ; Donnellan, Hill, & Roberts, 2015 ; Staudinger & Kunzmann, 2005 ) and alongside external forces (e.g., economic recessions). Our ability to dismiss crystallization claims is not merely empirical: although current methods and analyses used cannot fully disentangle age from cohort, lifespan development theory promotes the ideas of lifelong development, multiple intervening life influences, and individuals’ agency in shaping their identity and context (e.g., Baltes, 1987 ). Accordingly, it is more rational and defensible to suggest that individuals’ age, life stage, social context, and historical period intersect across the lifespan. These intersections, in turn, produce predictable as well as unique effects that translate into different attitudes, values, and behaviors, but not as a passive and predetermined function of an individual’s generation.

Myth # 9: Studying Age at Work Is the Antidote to the Problems with Studying Generations

Age and aging research are neither remedies for nor equivalent approaches to the study of generations. First, there are a broad range of phenomena encompassed in both research on “age at work” and “aging at work” (e.g., see discussion of “successful aging” research components in Zacher, 2015a ). These two areas are related but distinct, spanning the study of age as a discrete or sample-relative sociodemographic (i.e., age as a descriptive device, especially between person), age as a compositional unit property (e.g., age diversity in a team, organization), and age as a proxy for continuous processes and development over time (i.e., age representing the passage of time, especially within-person in longitudinal research). Each of these forms has a multitude of potential contributions to our understanding of the workplace, and these contributions should not (and cannot) be reduced to generational cohort-based generalizations. Second, and as noted earlier, although aging research is confounded by cohort effects, it draws on sound theories, research designs, and statistical modeling approaches (Bohlmann, Rudolph & Zacher, 2018 ). The study of generations at work, however, relies upon theories unintended for formal testing and flawed data collection methods and analyses (Costanza et al., 2017 ).

Moreover, whereas both age and aging research treat time continuously, generational research groups people into cohort categories. This results in a loss of important nuance and information about individuals, with results prone to either over- or underestimated age effects. The practice of cohort grouping also creates a “levels” issue in generational research to which age and aging research are not subject: studying aging focuses on the individual level of analysis, whereas (sociological) generational research “groups” individuals into aggregates and then incorrectly draws inferences about individual outcomes. This mismatch of levels can produce ecological or atomistic fallacies (i.e., assumptions that group-level phenomena apply to the individual level and vice versa), depending on whether group- or individual-level data are used to draw conclusions (Rudolph & Zacher, 2017 ). Thus, although age and aging research present robust opportunities for understanding how to support the age-diverse workforce, generational research provides incomplete conclusions about, and unclear implications for, understanding trends in the workplace. Studying age alone is not a substitute for generational research; rather, it transcends generational approaches and engenders more useful and tenable conclusions for researchers and practitioners alike.

Myth #10: Talking About Generations Is Largely Benign

Talking about generations is far from benign: it promotes the spread of generationalism, which can be considered “modern ageism.” Just as “modern racism” is characterized by more subtle and implicit, yet no less discriminatory or troubling, racist beliefs about black, indigenous, and people of color (BIPOC; e.g., McConahay, Hardee, & Batts, 1981 ), generationalism is defined by sanctioned ambivalence and socially acceptable prejudice toward people of particular ages. These beliefs are normalized and pervasive, reiterated across various forms of popular media and culture to the point that they seem innocuous. However, generationalism leads to decisions at a variety of levels (e.g., individual, organizational, institutional) that are harmful, divisive, and potentially illegal.

Media outlets play a large role in societal tolerance and acceptance of generationalism (Rauvola et al., 2019 ). New “generations” are frequently proposed in light of current events, and age stereotyping becomes further trivialized with each iteration. Adding to this, an abundance of generational labels “stick” while others do not—“iGen,” “Generation Wii,” “Generation Z,” and “Zoomers” all vie to define the “post-Millennial” generation (Raphelson, 2014 ), and “Generation Alpha” (a name inspired in part by naming conventions during the 2005 hurricane season; McCrindle & Wolfinger, 2009 ) now faces competition from “Gen C” to define the next generation. “Gen C” (or “Generation Corona;” see Rudolph & Zacher, 2020a , 2020b ) has gained traction in the media alongside the recent COVID-19 pandemic, with some suggesting that “coronavirus has the potential to create a generation of socially awkward, insecure, unemployed young people” (Patel, 2020 ). These labels differ markedly by country as well, as noted earlier, adding to the trivialization and confusion. More and more, these labels are also used to add levity, and/or to avoid blatant ageism, to deep-seated sociopolitical divides and conflicts portrayed in the media. Take, for example, the rise of “OK Boomer” alongside resentment toward conservatism (Romano, 2019 ), or the labeling of the “Karen Generation” to encapsulate white privilege and entitlement, especially among middle- to upper-class suburban women (Strapagiel, 2019 ).

Although often treated as harmless banter, this lexicon filters into influential research and policy-based organizations (e.g., “Gen C” in The Lancet, 2020 ), legitimizing the use of generational labels and associated age stereotypes in discourse and decision-making. As suggested above, in many countries, age is a protected class and the use of generations to inform differential practices and policies in organizations (e.g., hiring, development and training, benefits) poses great risk to the age inclusivity, and the legal standing, of workplaces (see also Costanza et al., 2020 ). Whether a generational label is new and catchy or accepted and seemingly mundane, it is built on the back of modern ageism, and generationalism—just like other “isms”—is far from benign.

Moving Beyond Generations: Two Alternative Models

With the preceding ten myths serving as a backdrop, we next introduce two models—the social constructionist perspective and the lifespan development perspective—that serve as alternative and complementary ways of thinking about, and understanding thinking about, generations and generational differences. Indeed, we propose that these are complementary models. Specifically, whereas the social constructionist perspective serves as a way of understanding why people tend to think about age and aging in generational terms , the lifespan development perspective serves as an alternative to thinking about age and aging in generational terms .

The Social Constructionist Perspective

Considering the ten myths reviewed above, it is clear that the evidence for the existence of generations and generational differences is lacking. Moreover, when applying a critical lens, what little evidence does exist does not hold up to theoretical and empirical scrutiny. What, then, are we left to do with the idea of generations? That is to say, how can we rationalize the continued emphasis that is placed on generations in research and practice despite the lack of a solid evidence base upon which these ideas rest? On the surface, this may seem to be a conceptually, rather than a practically, relevant question. However, there is a booming industry of advisors, gurus, and entire management consulting firms based around the idea of generations (e.g., Hughes, 2020 ). In whatever form it takes, generationally based practice is built upon the rather shaky foundations of this science, putting organizations and their constituents at risk—not only of wasted money, resources, and time, but of propagating misplaced ideas based on a weak, arguably non-existent evidence base (Costanza et al., 2020 ). As the organizational sciences move toward the ideals of evidence-based practice, generations and assumed differences between them are quickly becoming yet another example of a discredited management fad (see Abrahamson, 1991 , 1996 ; Røvik, 2011 ).

Borrowed from sociological theoretical traditions, the social constructionist perspective focuses on understanding the nature of various shared assumptions that people hold about reality, through understanding the ways in which meanings develop in coordination with others, and how such meanings are attached to various lived experiences, social structures, and entities (see Leeds-Hurwitz, 2009 )—including generations. Comprehensive treatments of the core ideas and tenets of the sociological notion of social constructionism can be found in Burr ( 2003 ) and Lock and Strong ( 2010 ). The social constructionist perspective on generations, which is based upon the idea that generations exist as social constructions, has been advanced as a means of understanding why people often think about age and aging in discrete generational, rather than continuous, terms (e.g., Rudolph & Zacher, 2015 , 2017 ; see also Lyons & Kuron, 2014 ; Lyons & Schweitzer, 2017 ; Weiss & Perry, 2020 ). The social constructionist perspective has utility as a model for understanding various processes that give rise to generations and for understanding the ubiquity and persistence of generations and generationally based explanations for human behavior. In an early conceptualization of this perspective, Zacher and Rudolph ( 2015 ) proposed that two processes reinforce each other to support the social construction of generations. Specifically, (1) the ubiquity and knowledge of generational stereotypes drive (2) the process of generational stereotyping, which is by-and-large socially sanctioned. These two processes fuel the social construction of generational differences, which have bearing on a variety of work-related processes, not least of which is the development of “generationalized” expectations for work specific attitudes, values, and behaviors. Such generationalized expectations set the stage for various forms of intergenerational conflicts and discrimination (i.e., generationalism; Rauvola et al., 2019 ) at work.

The social constructionist perspective on generations is grounded in three core principles: (1) generations are social constructs that are “willed into being”; (2) as social constructs, generations exist because they serve a sensemaking function; and (3) the existence and persistence of generations can be explained by various processes of social construction. The social constructionist perspective is gaining traction as a viable alternative to rather rigid, deterministic approaches of conceptualizing and studying generations, even among otherwise staunch proponents of these ideas. For example, Campbell et al. ( 2017 ) offer that “…generations might be best conceptualized as fuzzy social constructs” (p. 130) and Lyons et al. ( 2015 ) echo similar sentiments about the role and function of generations. To further clarify this perspective, we next expand upon these three core ideas that are advanced by the social constructionist perspective, providing more details and examples of each, and offering supporting evidence from research and theory.

First, the social constructionist perspective advances the idea that generations and generational differences do not exist objectively (see Berger & Luckman, 1966 , for a classic treatment of this idea of the “socially constructed” nature of reality). Rather, generations are “willed into being” as a way of giving meaning to the complex, multicausal, multidirectional, and multidimensional process of human development that we observe on a day-to-day basis, especially against the backdrop of rapidly changing societies. Adopting a social constructionist framework motivates an understanding of the various ways in which groups of individuals actively participate in the construction of social reality, including how socially constructed phenomena develop and become known to others, and how they are institutionalized with various norms and traditions. To say that generations are “social constructs,” or that generations reflect a process of “social construction,” implies that our understanding of their meanings (e.g., the “notion” of generations; the specific connotations of implying one generation versus another) exists as an artifact of a shared understanding of “what” generations “are,” and that this is accepted and agreed upon by members of a society.

Moreover, and to the second core principle, the social constructionist perspective suggests that generations serve as a powerful, albeit flawed, tool for social sensemaking. Generations provide a heuristic framework that greatly simplify people’s ability to quickly and efficiently make judgments in social situations, at the risk of doing so inaccurately. In other words, generations offer an easy, yet overgeneralized, way to give meaning to observations and perceptions of complex age-related differences that we witness via social interactions. This idea is borrowed from social psychological perspectives on the development, formation, and utility of stereotypes. When faced with uncertainty, humans have a natural tendency to seek out explanations of behavior (i.e., their own, but also others’; see Kramer, 1999 ). This process reflects an inherent need to makes sense of one’s world through a process of sensemaking. An efficient, albeit often flawed, strategy to facilitate sensemaking is the construction and adoption of stereotypes (Hogg, 2000 ). Stereotypes are understood in terms of cognitive–attitudinal structures that represent overgeneralizations of others—in the form of broadly applied beliefs about attitudes, ways of thinking, behavioral tendencies, values, beliefs, et cetera (Hilton & Von Hippel, 1996 ).

Applying these ideas, the adoption of generations, and the accompanying prescriptions that clearly lay out how members of such generations ought to think and behave, helps people to make sense of why relatively older versus younger people “are the way that they are.” Additionally, generational stereotypes can be enacted as an external sensemaking tool, as described, but also for internal sensemaking (i.e., making sense of one’s own behavior). Indeed, there is emerging evidence that people internalize various generational stereotypes and that they enact them in accordance with behavioral expectations (i.e., a so-called Pygmalion effect, see Eschleman, King, Mast, Ornellas, & Hunter, 2016 ).

Third, the social constructionist perspective offers that generations are constructed and supported through different mechanisms. The construction of generations can take various forms, for example, in media accounts of “new” generations that form as a result of major events (e.g., pandemics; Rudolph & Zacher, 2020a , 2020b ), political epochs (e.g., “Generation Merkel” Mailliet & Saltz, 2017 ; “Generation Obama,” Thompson, 2012 ), economic instability (e.g., “Generation Recession,” Sharf, 2014 ), and even rather benign phenomena, such as growing up in a particular time and place (e.g., “Generation Golf,” Illies, 2003 ).

A major source of generational construction can be traced to various “think tank”-type groups that purport to study generations. From time to time, such groups proclaim the end of one generation and the emergence of new generational groups (e.g., Dimock, 2019 ). These organizations legitimize the idea of generations in that they are often otherwise trusted and respected sources of information and their messaging conveys an associated air of scientific rigor. Relatedly, authors of popular press books likewise tout the emergence of new generations. For example, Twenge has identified “iGen” (Twenge, 2017 ) as the group that follows “Generation Me” (Twenge, 2006 ), although neither label has found widespread acceptance outside of these two texts. Importantly, all generational labels, including these, exist only in a descriptive sense, and it is not always clear if the emergence of the generation precedes their label, or vice versa. For example, consider that Twenge has suggested that the term “iGen” was inspired by taking a drive through Silicon Valley, during which she concluded that “…iGen would be a great name for a generation…” (Twenge, as quoted in Horovitz, 2012 ), a coining mechanism far from Mannheim’s original conceptualization of what constitutes a generation.

The contemporary practice of naming new generations has its own fascinating history (see Raphelson, 2014 ). Indeed, the social constructionist perspective recognizes that the idea of generations is not a contemporary phenomenon; there is a remarkable historical periodicity or “cycle” to their formation and to the narratives that emerge to describe members of older versus younger generations. As discussed earlier, members of older generations have tended to pan members of younger generations for being brash, egocentric, and lazy throughout history, whereas members of younger generations disparage members of older generations for being out of touch, rigid, and resource-draining (e.g., Protzko & Schooler, 2019 ; Rauvola et al., 2019 ). Likewise, the social constructionist perspective underlines that generations are supported through both the ubiquity of generational stereotypes and the socially accepted nature of applying such labels to describe people of different ages.

In summary, the social constructionist perspective offers a number of explanations for the continued existence of generations, especially in light of evidence which speaks to the contrary. Specifically, by recognizing that generations exist as social constructions, this perspective helps to clarify the continued emphasis that is placed on generations in research and practice, despite the lack of evidence that support their objective existence. Moreover, the social constructionist perspective offers a framework for guiding research into various processes that give rise to the construction of generations and for understanding the ubiquity and persistence of generations and generationally based explanations for human behavior. Next, we shift our attention to a complementary framework—the lifespan perspective—which likewise supports alternative theorizing about the role of age and the process of aging at work that does not require the adoption of generations and generational thinking. Then, we will focus on drawing lines of integration between these two perspectives.

The Lifespan Development Perspective

The lifespan development perspective is a meta-theoretical framework with a rich history of being applied for understanding age-related differences and changes in the work context (Baltes et al., 2019 ; Baltes & Dickson, 2001 ; Rudolph, 2016 ). More recently, the lifespan perspective has also been advanced as an alternative to generational explanations for work-related experiences and behaviors (see Rudolph et al., 2018 ; Rudolph & Zacher, 2017 ; Zacher, 2015b ). Contrary to generational thinking and traditional life stage models of human development (e.g., Erikson, 1950 ; Levinson, Darrow, Klein, Levinson, & McKee, 1978 ), the lifespan perspective focuses on continuous developmental trajectories in multiple domains (Baltes, Lindenberger, & Staudinger, 1998 ). For instance, over time, an individual’s abilities may increase (i.e., “gains,” such as accumulated job knowledge), remain stable, or decrease (i.e., “losses,” such as reduced psychomotor abilities).

Baltes ( 1987 ) outlined seven organizing tenets to guide thinking about individual development ( ontogenesis ) from a lifespan perspective. Specifically, human development is (1) a lifelong process that involves (2) stability or multidirectional changes, as well as (3) both gains and losses in experience and functioning. Moreover, development is (4) modifiable at any point in life (i.e., plasticity); (5) socially, culturally, and historically embedded (i.e., contextualism); and (6) determined by normative age- and history-graded influences and non-normative influences. Regarding the final tenet, normative age-graded influences include person and contextual determinants that most people encounter as they age (e.g., decline in physical strength, retirement), normative history-graded influences include person and contextual determinants that most people living during a certain historical period and place experience (e.g., malnutrition, recessions), and non-normative influences include determinants that are idiosyncratic and less “standard” to the aging process (e.g., accidents, natural disasters). Finally, Baltes ( 1987 ) argued that (7) understanding lifespan development requires a multidisciplinary (i.e., one that goes beyond psychological science) approach. In summary, the lifespan perspective recognizes that individuals’ development is continuous, malleable, and jointly influenced by both normative and non-normative internal (i.e., those that are genetically determined; specific decisions and behaviors that one engages in) and external factors (i.e., the sociocultural and historical context).

A generational researcher may ask research questions like (a) “How does generational membership influence employee attitudes, values, and behaviors?” or (b) “What differences exist between members of different generations in terms of their work attitudes, values, or behaviors?” Then, likely based on the results of a cross-sectional research design that collects information on age or birth year and work-related outcomes, a generational researcher would likely categorize employees into two or more generational groups and take mean-level differences in outcomes between these groups as evidence for the existence of generations and differences between them. Contrary to this, a lifespan researcher would be more apt to ask research questions like (a) “Are there age-related differences or changes in work attitudes, values, and behaviors?” or (b) “What factors serve to differentially modify employees’ continuous developmental trajectories?” They would seek out cross-sectional or longitudinal evidence for age-related differences or changes in attitudes, values, and behaviors, as well as evidence for multiple, co-occurring factors, including person characteristics (e.g., abilities, personality), idiosyncratic factors (e.g., job loss, health problems), and contextual factors (e.g., economic factors, organizational climate) that may predict these differences or changes.

The lifespan perspective generally does not operate with the generations concept, but does distinguish between chronological age, birth cohort, and contemporaneous period effects. As described earlier, generational groups are inevitably linked to group members’ chronological ages, as they are based on a range of adjacent birth years and typically examined at one point in time. Accordingly, tests of generational differences involve comparisons between two or more age groups (e.g., younger vs. older employees). In contrast to tenets #1, #2, and #3 of the lifespan perspective, generational thinking is static in that differences between generations are assumed to be stable over time. The possibility that members of younger generations may change with increasing age, or whether members of older generations have always shown certain attitudes, values, and behavior, are rarely investigated. Moreover, generational thinking typically adopts a simplistic view of differences between generational groups (e.g., “Generation A” has a lower work ethic than “Generation B”) as compared to the more nuanced lifespan perspective with its focus on stability or multidirectional changes, as well as the joint occurrence of both gains and losses across time.

With regard to the lifespan perspective’s tenet #4 (i.e., plasticity), generational researchers tend to treat generational groups as immutable (i.e., as they are a function of one’s birth year) and their influences as deterministic (i.e., all members of a certain generation are expected to think and act in a certain way; so-called cohort determinism). In contrast, the lifespan perspective recognizes that there is plasticity, or within-person modifiability, in individual development at any age. Changes to the developmental trajectory for a given outcome can be caused by person factors (e.g., knowledge gained by long-term practice), contextual factors (e.g., organizational change), or both. For instance, lifespan researchers assume that humans enact agency over their environment and the course of their development. Development is not only a product of the context in which it takes place (e.g., culture, historical period) but also a product of individuals’ decisions and actions. This notion underlies the principle of developmental contextualism (Lerner & Busch-Rossnagel, 1981 ), embodied within the idea that humans are both the products and the producers of their own developmental course.

Research on generations and intergenerational exchanges originated and still is considered an important topic in the field of sociology (Mayer, 2009 ), which emphasizes the role of the social, institutional, cultural, and historical contexts for human development (Settersten, 2017 ; Tomlinson, Baird, Berg, & Cooper, 2018 ). In contrast, the lifespan perspective, which originated in the field of psychology, places a stronger focus on individual differences and within-person variability. Nevertheless, the lifespan perspective’s tenet #5 (i.e., contextualism) suggests that individual development is not only influenced by biological factors but also embedded within the broader sociocultural and historical context. This context includes the historical period, economic conditions, as well as education and medical systems in which development unfolds. Even critics have acknowledged that these external factors are rather well-integrated within the lifespan perspective (Dannefer, 1984 ). That said, most empirical lifespan research has not distinguished between birth cohort and contemporaneous period effects.

For example, studies in the lifespan tradition have suggested that there are birth cohort effects on cognitive abilities and personality characteristics (Elder & Liker, 1982 ; Gerstorf, Ram, Hoppmann, Willis, & Schaie, 2011 ; Nesselroade & Baltes, 1974 ; Schaie, 2013 ). Possible explanations for these effects may be improvements in education, health and medical care, and the increasing complexity of work and home environments (Baltes, 1987 ). An important difference to generational research is that these analyses focus on individual development and outcomes and not on group-based differences.

In contrast to research in the field of sociology, the lifespan perspective generally does not make use of the generations concept and associated generational labels. Instead, in addition to people’s age, lifespan research sometimes focuses on birth year cohorts (Baltes, 1968 ). However, the lifespan perspective does not assume that all individuals born in the same birth year automatically share certain life experiences or have similar perceptions of historical events (Kosloski, 1986 ). According to Baltes, Cornelius, and Nesselroade ( 1979 ), researchers interested in basic developmental processes (e.g., child developmental psychologists) that were established during humans’ genetic and cultural evolution may treat potential cohort effects as error or as transitory, historical irregularities. In contrast, other researchers (e.g., social psychologists, sociologists) may focus less on developmental regularities and treat cohort effects as systematic differences in the levels of an outcome, with or without explicitly proposing a substantive theoretical mechanism or process variable that explains these cohort differences (e.g., poverty, access to high-quality education). Empirical research on generations is typically vague with regard to concrete theoretical mechanisms of assumed generational differences (i.e., beyond the notion of “shared life events and experiences,” such as the Vietnam war, 9/11, or the COVID-19 pandemic) and typically does not operationalize and test these mechanisms.

In proposing the general developmental model, Schaie ( 1986 ) suggested decoupling the “empty variables” of birth cohort and time period from chronological age and re-conceptualizing them as more meaningful variables. Specifically, he re-defined cohort as “the total population of individuals entering the specified environment at the same point in time” and period as “historical event time,” thereby uncoupling period effects from calendar time by identifying the timing and duration of the greatest influence of important historical events (Schaie & Hertzog, 1985 , p. 92). Thus, the time of entry for a cohort does not have to be birth year and can include biocultural time markers (e.g., puberty, parenthood) or societal markers (e.g., workforce entry, retirement; Schaie, 1986 ). Similarly, the more recent motivational theory of lifespan development has discussed cohort-defining events as age-graded opportunity structures (Heckhausen, Wrosch, & Schulz, 2010 ). Thus, from a lifespan perspective, cohorts are re-defined as an interindividual difference variable, whereas period is re-defined as an intraindividual change variable (Schaie, 1986 ).

Tenet #6 of the lifespan perspective suggests that individuals have to process, react to, and act upon normative age-graded, normative history-graded, and non-normative influences that co-determine developmental outcomes (Baltes, 1987 ). The interplay of these three influences leads to stability and change, as well as multidimensionality and multidirectionality in individual development (Baltes, 1987 ). Importantly, the use of the term “normative” is understood in a statistical–descriptive sense here, not in a value-based prescriptive sense; it is assumed that there are individual differences (e.g., due to gender, socioeconomic status) in the experience and effects of these influences (Baltes & Nesselroade, 1984 ). Moreover, the relative importance of these three influences can be assumed to change across the lifespan (Baltes, Reese, & Lipsitt, 1980 ). Specifically, normative age-graded influences are assumed to be more important in childhood and later adulthood than in adolescence and early adulthood (i.e., due to biological and evolutionary reasons). In contrast, normative history-graded determinants are assumed to be more important in adolescence and early adulthood than in childhood and old age (i.e., when biological and evolutionary factors are less important). Finally, non-normative influences are assumed to increase linearly in importance across the lifespan (Baltes et al., 1980 ; see also Rudolph & Zacher, 2017 ). Indeed, the assumed differential importance of these influences across the lifespan differs markedly from the cohort deterministic approach implied in generational theory and research.

According to Baltes et al. ( 1980 ), idiosyncratic life events become more important predictors of developmental outcomes with increasing age due to declines in biological and evolutionary-based genetic control over development and the increased heterogeneity and plasticity in developmental outcomes at higher ages. Despite the assumed relative strengths of these normative and non-normative influences across the lifespan, they are at no point completely irrelevant to individual development. For example, in the work context, the theoretical relevance of history-graded influences on work-related outcomes may be a factor that determines the strength of potential effects (Zacher, 2015b ). For instance, experiencing a global pandemic is more likely to influence the development of individuals’ attitudes—not an entire generations’ collective attitudes—toward universal health care than it is to influence their job satisfaction. Moreover, individuals’ level of job security may not only be influenced by the pandemic but also by their profession and levels of risk tolerance.

In summary, the lifespan development perspective offers a number of alternative explanations for the role of age and the process of aging at work that do not rely in generational explanations. Specifically, by recognizing that development is a lifelong process that is affected by multiple influences, this perspective helps to clarify the complexities of development, particularly the processes that lead to inter- and intraindividual changes over time. With a clearer sense of these two alternative perspectives, we next shift our attention to outlining various points of integration between them.

Integrating the Social Constructionist and Lifespan Development Perspectives

With a clearer sense of the core tenets of the social constructionist and lifespan development perspectives, we now turn our attention to clarifying lines of integration between these two approaches. While seemingly addressing different “corners” of the ideas presented here, there are a number of complementary features of the social constructionist and lifespan development perspectives to be noted. First, both perspectives generally eschew the idea that generations exist objectively and are meaningful units of study for explaining individual and group differences. Second, both perspectives offer that the complexities that underlie the understanding of age and the process of aging at work cannot be reduced to rather simple mean-level comparisons. Third, both perspectives are generative, in that they encourage research questions that go beyond common ways of thinking. Fourth, and relatedly, both perspectives provide frameworks for more “directly” studying aging and development—whether in the form of how we collectively understand and conceptualize these processes (the social constructionist perspective), or how individuals continuously and interactively shape their own life trajectory (the lifespan development perspective). Together, rather than relying on determinism, these perspectives capitalize on the subjective, dynamic, and agentic aspects of life in organizations and society, allowing for more rigorous and representative research into meaning, creation, stability, and change in context.

Commonalities Between Social Constructionist and Lifespan Development Perspectives

Beyond these complementary features, we propose six additional commonalities that serve as the basis for a more formal integration of these two perspectives with one another (see Table 2 for a summary). First, both perspectives recognize the role of context, in that both development (the lifespan development perspective) and sensemaking (the social constructionist perspective) occur within social contexts. Second, both perspectives describe processes of action, creation, negotiation, and/or codification. Whereas the lifespan perspective focuses on how these processes create identity, beliefs, and habits or behaviors that emerge over time through active self-regulatory, motivational processes, discovery, and (self)acceptance/selectivity, the social constructionist perspective focuses more so on the development of truths and meaning that emerge from collective dialogues, understandings, and traditions through acceptance and institutionalization. Third, both perspectives acknowledge the fundamental roles of internal and external comparisons. For example, the lifespan perspective offers that successful development is judged both externally (e.g., in comparison with important others, normative age expectations, or timetables) and internally (e.g., in comparison with younger or desired state selves). Similarly, social constructions can be focused externally (e.g., in the form of stereotypes) as well as internally (i.e., to make sense of one’s own behavior or identity).

Fourth, both perspectives highlight learning and reinforcement processes that are derived from environmental sources. The lifespan perspective offers that adaptiveness (e.g., how successfully someone is developing/aging) and the self (as well as identities, values, behaviors, etc.) are learned from and reinforced by feedback from various aspects of the environment. Similarly, social constructions are derived from and reinforced by multiple environmental sources, including those with perceived status, “weight,” and legitimacy. Fifth, by offering that development is a modifiable, discontinuous process (the lifespan development perspective) and that social constructions are constantly re-defined and re-emerge into public consciousness (the social constructionist perspective), both perspectives focus on continuous evolution, revision, and change. The final commonality to be drawn across these two perspectives is that they both focus on predictable influences that characterize certain spans of time, especially around significant events or “turning points.” The lifespan perspective offers that, although complex and plastic, development does have some predictable aspects and influences due to their significance in the life course (e.g., age-graded events). Complimenting this, many social constructions, although in constant flux and redefinition, fall back on the same key concepts due to their pervasiveness in public consciousness (e.g., the laziness of youth) at certain “key moments” in history (e.g., to explain or cope with societal change).

Limitations of These Alternative Perspectives

Beyond the benefits of considering alternative models to generations, and integrations thereof, it is important to mention the limitations of these alternative perspectives. For example, it could be argued that, because it does not provide formalized predictions, the social constructionist perspective is “hard to study.” Additionally, the lifespan perspective can be criticized, just as it is lauded, for its focus on individual agency: as noted earlier, psychological perspectives often place a premium on studying individual-level mechanisms rather than other levels of influence (Rauvola & Rudolph, 2020 ). Thus, without directed efforts on the part of researchers to attend to these aspects of lifespan development theory in their work, it can be easy to fall into the “trap” of ignoring structural factors (e.g., socioeconomic status, governmental policy, institutionalized discrimination) that have bearing on and may constrain individuals’ agentic influence on their life trajectory (for an integration of the psychological lifespan perspective and the sociological life course perspective in the context of vocational behavior and career development, see Zacher & Froidevaux, 2020 ). Still, and for the many reasons noted throughout this manuscript, we do not contend that generational cohort membership is one of these structural factors, and a generational approach ignores these other forces even more flagrantly.

Recommendations for Adopting Alternative Theoretical Perspectives on Generations

Overall, we argue that organizational researchers and practitioners should move beyond the notion of generations for understanding the complexities of age at work. To do so, we urge the adoption of the alternative theoretical models we have outlined here, as well as considerations of their integration. To this end, those interested in studying the role of age at work should adopt a lifespan, rather than a generational, perspective, whereas those interested in studying the persistence of generational thinking would be well served to consider the adoption of a social constructionist perspective. Moreover, to understand more holistically the role of age and the construction of aging at work, it may be useful to adopt an integrative view on these two perspectives, embodied within the six commonalities between them that we have outlined above (see also Table 2 ).

Generational thinking is problematic because it assumes that aggregate social phenomena can explain individual-level attitudes, values, and behavior. In contrast, adopting a lifespan perspective means taking a multidisciplinary lens to understanding age-related differences and changes at work by specifically focusing on how the interplay between person characteristics and contextual variables serve to modify individual development. Moreover, the social constructionist perspective offers guidance for unpacking the meanings people attach to assumptions that are made about these aggregate social phenomena, further aiding in understanding the complexities at play here. We consider recommendations for research and practice adopting these perspectives, next.

Recommendations for Adopting the Social Constructionist Perspective

The social constructionist perspective on generations highlights a number of potential areas for research and practice. Given their longstanding and culturally/historically embedded nature, the social constructionist perspective recognizes that the idea of generations is not likely to go away, even with a lack of empirical methods or evidence to support their existence. Instead, this perspective calls for a paradigm shift in generational research and practice, away from the rather positivist notion of “seeking out” generational differences and instead toward a focus on studying and understanding those processes that support the social construction of generations to begin with. Considering research, the focus could be on those antecedents (e.g., intergroup competition and discrimination; North & Fiske, 2012 ; i.e., to address the question, “Why do these social constructions emerge?”) and outcomes (e.g., self-fulfilling prophecies—i.e., to address the question, “What are the consequences of willing generations into being?”) of socially constructed generations.

Conducting research from a social constructionist perspective requires adopting methodologies that may not be common in organizational researchers’ “tool kits.” For example, Rudolph and Zacher ( 2015 ) used sentiment analysis, a natural language processing methodology, to analyze the content of Twitter dialogues concerning various generational groups to understand the relative sentiment associated with each. Indeed, it would arguably be difficult to study generations from this perspective by adopting a typical frequentist approach to hypothesis testing. This perspective is less about gathering evidence “against the null hypothesis” that generations or differences between them exist in a more or less “objective” (i.e., measurable) way. Instead, it is more about understanding, phenomenologically, the various processes that give rise to people’s subjective construction of generations, the systems that facilitate attaching meaning to generational labels, and the structures that support our continued reliance on generations as a sensemaking tool in spite of logical and empirical arguments against doing so.

More practically, understanding why people think in terms of generations can help us to develop interventions that are targeted at helping people think less in terms of generations and more in terms of individuating people on the basis of the various processes outlined in our description of the lifespan perspective (i.e., personal characteristics; idiosyncratic and contextual factors). The social constructionist perspective also encourages changing the discourse among practitioners, shifting the focus away from managing generations as discrete groups and toward developing more age-conscious personnel practices, policies, and procedures that support workers across the entirety of their working lifespans (e.g., Rudolph & Zacher, 2020c ). We thus urge practitioners to adopt a social constructionist perspective and shift focus away from promoting processes to manage members of different generations to a focus on managing the perceptions of generations and their differences. By recognizing the constructed nature of generations, the social constructionist perspective decouples beliefs about generations from these broad and overgeneralized assumptions about their influence on individuals.

Recommendations for Adopting the Lifespan Perspective

Just as the social constructionist perspective highlights a number of potential areas for research and practice, so too does the lifespan perspective. To this end, and to move research on the lifespan perspective on generations forward, Rudolph and Zacher ( 2017 ) argued that, at the individual level of analysis, the influence of age-graded and historical/contextual influences are inherently codetermined and inseparable. Accordingly, in their lifespan perspective on generations, they proposed that the influence of historically graded and sociocultural context variables occurs at the individual level of analysis only, and not as a manifestation of shared generational effects (proposition 1). They suggested that future research should focus on individual-level indicators of historical and sociocultural influences. Furthermore, they argued that age, period, and cohort effects are both theoretically and empirically confounded and, thus, inseparable (proposition 2). Finally, consistent with Schaie’s ( 1986 ) general developmental model, they suggested that cohorts should be operationalized as interindividual differences, whereas period effects should be defined in terms of intraindividual changes (proposition 3).

In terms of more practical implications of the lifespan perspective, we urge practitioners to adopt principles of lifespan development in the design of age-conscious work processes, interventions, and policies that do not rely on generations as a means of representing age. Indeed, researchers and practitioners alike should take steps to avoid the pitfalls of “generational thinking,” which yields several dangers that can be overcome by lifespan thinking (Rauvola et al., 2019 ; Rudolph et al., 2018 ; Rudolph & Zacher, 2020c ). First, generational thinking categorizes individuals into arbitrary generational groups based on a single criterion (i.e., birth year) and is therefore socially exclusive rather than inclusive; in contrast, the lifespan perspective conceptualizes and operationalizes age directly as a continuous variable (Baltes, 1987 ). Second, generational thinking reduces complex age-related processes into a simplistic dichotomy at a single point in time; the lifespan perspective adopts a multidimensional, multidirectional, and multilevel approach to represent the complexities of aging more appropriately. Third, generational thinking overemphasizes the role of (ranges of) birth cohorts in influencing work outcomes; in contrast, the lifespan perspective emphasizes interindividual differences and intraindividual development (as well as interindividual differences in intraindividual development). Finally, generational thinking is dangerous because it assumes that generational group membership determines individual attitudes, values, and behavior. In contrast to this, the lifespan perspective, which entails the notion of plasticity, suggests that intraindividual changes in developmental paths are possible at any age and that individuals can enact control and influence their own development.

Conclusions

This manuscript sought to achieve two goals, related to helping various constituents better understand the complexities of age and aging at work, and dissuade the use of generations and generational differences as a means of understanding and simplifying such complexities. First, we aimed to “bust” ten common myths about generations and generational differences that permeate various discussions in organizational sciences research and practice and beyond. Then, with these debunked myths as a backdrop, we offered two complementary alternative models—the social constructionist perspective and the lifespan perspective—with promise for helping organizational scientists and practitioners better understand and manage age and the process of aging in the workplace and comprehend the pervasive nature of generations as a means of social sensemaking. The social constructionist perspective calls for a shift in thinking about generations as tangible and demonstrable units of study, to socially constructed entities, the existence of which is in-and-of-itself worthy of study. Supplementing these ideas, the lifespan perspective offers that rather than focusing on simplified, rather deterministic groupings of people into generations, development occurs in a continuous, multicausal, multidirectional, and multidimensional process. Our hope is that this manuscript helps to “redirect” talk about generations away from their colloquial use to a more critical and informed perspective on age and aging at work.

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Rudolph, C.W., Rauvola, R.S., Costanza, D.P. et al. Generations and Generational Differences: Debunking Myths in Organizational Science and Practice and Paving New Paths Forward. J Bus Psychol 36 , 945–967 (2021). https://doi.org/10.1007/s10869-020-09715-2

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Published : 04 September 2020

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DOI : https://doi.org/10.1007/s10869-020-09715-2

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Article publication date: 7 November 2008

The purpose of this paper is to review data from 1.4 million people who completed personality, attitude, psychopathology, or behavior scales between the 1930s and the present and to discuss how those differences may impact today's workplace.

Design/methodology/approach

The data are gathered from research reports using psychological scales over the last eight decades, primarily those using college student samples.

Generation Me (sometimes called Gen Y or Millennials) demonstrates higher self‐esteem, narcissism, anxiety, and depression; lower need for social approval; more external locus of control; and women with more agentic traits.

Practical implications

Managers should expect to see more employees with unrealistically high expectations, a high need for praise, difficulty with criticism, an increase in creativity demands, job‐hopping, ethics scandals, casual dress, and shifting workplace norms for women. Organizations can respond to these changes with accommodations (e.g. praise programs) or with counter pressure (e.g. dress codes), and it is imperative that managers consider the best reaction for their workforce.

Originality/value

Most studies of generations interview workers at one time; thus any differences could be due to age or generation. Many of these reports are also based on subjective opinions and perceptions. In contrast, the paper reviews quantitative data on generational differences controlling for age. This empirically based look at generations in the workplace should be useful to managers and workers.

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Twenge, J.M. and Campbell, S.M. (2008), "Generational differences in psychological traits and their impact on the workplace", Journal of Managerial Psychology , Vol. 23 No. 8, pp. 862-877. https://doi.org/10.1108/02683940810904367

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How Pew Research Center will report on generations moving forward

Journalists, researchers and the public often look at society through the lens of generation, using terms like Millennial or Gen Z to describe groups of similarly aged people. This approach can help readers see themselves in the data and assess where we are and where we’re headed as a country.

Pew Research Center has been at the forefront of generational research over the years, telling the story of Millennials as they came of age politically and as they moved more firmly into adult life . In recent years, we’ve also been eager to learn about Gen Z as the leading edge of this generation moves into adulthood.

But generational research has become a crowded arena. The field has been flooded with content that’s often sold as research but is more like clickbait or marketing mythology. There’s also been a growing chorus of criticism about generational research and generational labels in particular.

Recently, as we were preparing to embark on a major research project related to Gen Z, we decided to take a step back and consider how we can study generations in a way that aligns with our values of accuracy, rigor and providing a foundation of facts that enriches the public dialogue.

A typical generation spans 15 to 18 years. As many critics of generational research point out, there is great diversity of thought, experience and behavior within generations.

We set out on a yearlong process of assessing the landscape of generational research. We spoke with experts from outside Pew Research Center, including those who have been publicly critical of our generational analysis, to get their take on the pros and cons of this type of work. We invested in methodological testing to determine whether we could compare findings from our earlier telephone surveys to the online ones we’re conducting now. And we experimented with higher-level statistical analyses that would allow us to isolate the effect of generation.

What emerged from this process was a set of clear guidelines that will help frame our approach going forward. Many of these are principles we’ve always adhered to , but others will require us to change the way we’ve been doing things in recent years.

Here’s a short overview of how we’ll approach generational research in the future:

We’ll only do generational analysis when we have historical data that allows us to compare generations at similar stages of life. When comparing generations, it’s crucial to control for age. In other words, researchers need to look at each generation or age cohort at a similar point in the life cycle. (“Age cohort” is a fancy way of referring to a group of people who were born around the same time.)

When doing this kind of research, the question isn’t whether young adults today are different from middle-aged or older adults today. The question is whether young adults today are different from young adults at some specific point in the past.

To answer this question, it’s necessary to have data that’s been collected over a considerable amount of time – think decades. Standard surveys don’t allow for this type of analysis. We can look at differences across age groups, but we can’t compare age groups over time.

Another complication is that the surveys we conducted 20 or 30 years ago aren’t usually comparable enough to the surveys we’re doing today. Our earlier surveys were done over the phone, and we’ve since transitioned to our nationally representative online survey panel , the American Trends Panel . Our internal testing showed that on many topics, respondents answer questions differently depending on the way they’re being interviewed. So we can’t use most of our surveys from the late 1980s and early 2000s to compare Gen Z with Millennials and Gen Xers at a similar stage of life.

This means that most generational analysis we do will use datasets that have employed similar methodologies over a long period of time, such as surveys from the U.S. Census Bureau. A good example is our 2020 report on Millennial families , which used census data going back to the late 1960s. The report showed that Millennials are marrying and forming families at a much different pace than the generations that came before them.

Even when we have historical data, we will attempt to control for other factors beyond age in making generational comparisons. If we accept that there are real differences across generations, we’re basically saying that people who were born around the same time share certain attitudes or beliefs – and that their views have been influenced by external forces that uniquely shaped them during their formative years. Those forces may have been social changes, economic circumstances, technological advances or political movements.

When we see that younger adults have different views than their older counterparts, it may be driven by their demographic traits rather than the fact that they belong to a particular generation.

The tricky part is isolating those forces from events or circumstances that have affected all age groups, not just one generation. These are often called “period effects.” An example of a period effect is the Watergate scandal, which drove down trust in government among all age groups. Differences in trust across age groups in the wake of Watergate shouldn’t be attributed to the outsize impact that event had on one age group or another, because the change occurred across the board.

Changing demographics also may play a role in patterns that might at first seem like generational differences. We know that the United States has become more racially and ethnically diverse in recent decades, and that race and ethnicity are linked with certain key social and political views. When we see that younger adults have different views than their older counterparts, it may be driven by their demographic traits rather than the fact that they belong to a particular generation.

Controlling for these factors can involve complicated statistical analysis that helps determine whether the differences we see across age groups are indeed due to generation or not. This additional step adds rigor to the process. Unfortunately, it’s often absent from current discussions about Gen Z, Millennials and other generations.

When we can’t do generational analysis, we still see value in looking at differences by age and will do so where it makes sense. Age is one of the most common predictors of differences in attitudes and behaviors. And even if age gaps aren’t rooted in generational differences, they can still be illuminating. They help us understand how people across the age spectrum are responding to key trends, technological breakthroughs and historical events.

Each stage of life comes with a unique set of experiences. Young adults are often at the leading edge of changing attitudes on emerging social trends. Take views on same-sex marriage , for example, or attitudes about gender identity .

Many middle-aged adults, in turn, face the challenge of raising children while also providing care and support to their aging parents. And older adults have their own obstacles and opportunities. All of these stories – rooted in the life cycle, not in generations – are important and compelling, and we can tell them by analyzing our surveys at any given point in time.

When we do have the data to study groups of similarly aged people over time, we won’t always default to using the standard generational definitions and labels. While generational labels are simple and catchy, there are other ways to analyze age cohorts. For example, some observers have suggested grouping people by the decade in which they were born. This would create narrower cohorts in which the members may share more in common. People could also be grouped relative to their age during key historical events (such as the Great Recession or the COVID-19 pandemic) or technological innovations (like the invention of the iPhone).

By choosing not to use the standard generational labels when they’re not appropriate, we can avoid reinforcing harmful stereotypes or oversimplifying people’s complex lived experiences.

Existing generational definitions also may be too broad and arbitrary to capture differences that exist among narrower cohorts. A typical generation spans 15 to 18 years. As many critics of generational research point out, there is great diversity of thought, experience and behavior within generations. The key is to pick a lens that’s most appropriate for the research question that’s being studied. If we’re looking at political views and how they’ve shifted over time, for example, we might group people together according to the first presidential election in which they were eligible to vote.

With these considerations in mind, our audiences should not expect to see a lot of new research coming out of Pew Research Center that uses the generational lens. We’ll only talk about generations when it adds value, advances important national debates and highlights meaningful societal trends.

• Age & Generations
• Demographic Research
• Generation X
• Generation Z
• Generations
• Greatest Generation
• Methodological Research
• Millennials
• Silent Generation

Kim Parker is director of social trends research at Pew Research Center .

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ABOUT PEW RESEARCH CENTER  Pew Research Center is a nonpartisan fact tank that informs the public about the issues, attitudes and trends shaping the world. It conducts public opinion polling, demographic research, media content analysis and other empirical social science research. Pew Research Center does not take policy positions. It is a subsidiary of  The Pew Charitable Trusts .

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Generational Differences at Work Are Marketing Hype

Understanding engagement through universal needs, not generational stereotypes..

Posted May 17, 2024 | Reviewed by Tyler Woods

• What Is a Career
• Find a career counselor near me
• Generational differences are minimal and often exaggerated.
• Focus on managing perceptions, not supposed generational gaps.
• Misleading stereotypes harm workplace dynamics and employee attitudes.

For the first time in history, five different generations are working together . Over the next decade, Millennials will become the majority of the workforce. What are their aspirations? Their behaviors? How should they be managed?

Unfortunately, these discussions are rarely backed 2by empirical data; most arguments are based on intuitive and generalized observations. Additionally, the concept of "generations" seems to mainly benefit the interests of numerous consultants.

Limited scientific support

Millennials want flexibility and transparency, Generation Z needs more security due to economic, environmental, and health crises, and Generation X values salary over a company's innovation capabilities. These stereotypes, naively shared by reputable institutions, are proven wrong when confronted with scientific realities. The differences between generations are much smaller than popular belief suggests, and academic research generally fails to demonstrate significant differences . In short, generational gaps are more of a myth than a substantiated theory. Studies and meta-analyses show nearly non-existent differences between generations in (1) work attitudes , (2) personality , (3) career mobility , conformity to norms, or overtime work, (4) reasons for resignation or motivations to accept a new position.

Similarly, contrary to the myth , there is no increase in narcissistic tendencies across generations. Narcissistic traits are more closely linked to life stages and age rather than generational affiliation. Younger generations appear more narcissistic not because they are different from previous ones, but because narcissism is more pronounced during youth. Some differences are thus attributed to supposed generational effects when they are actually part of our natural evolution and maturation. Indeed, while personality is not easily malleable, it is not set in stone.

A meta-analysis of longitudinal studies shows that young adulthood is the most critical life stage for personality development, and throughout adolescence , traits become more stable, peaking at age 25. Additionally, potential future changes, particularly between ages 20 and 40 , mostly see people becoming more agreeable, emotionally stable, conscientious , and dominant.

Worse, when studies do identify differences , the individual variability within a single generation is greater than that between generations. In other words, individuals within the same cohort are more different from each other than from those in different generations. The proliferation of blind empiricism and credulous opinions has contributed to misleading management practices , potentially leading to harmful consequences and contradicting legal, conceptual, practical, and theoretical foundations.

In light of these findings, it is imperative to shift the focus from managing generational differences to managing perceptions related to generations. This shift is crucial to avoid reinforcing stereotypes and modern ageism.

Negative effects

With each new event, new generations are proposed, further normalizing age-based stereotyping: recently, some have attempted to conceptualize a Covid-19 generation . Research highlights the emergence of individual perceptions, including both what an individual believes about members of other generations (stereotypes) and what they think other groups believe about their own group (meta-stereotypes).

These studies show that both older and younger workers believe others perceive them more negatively than they actually do, and these stereotypes and meta-stereotypes are not accurate. However, they have critical implications for the workplace:

• Age-related biases negatively affect the quality of training and performance evaluations for older individuals, particularly in the context of new technologies.
• Being labeled as a Baby Boomer leads to more negative judgments in recruitment, training, and conflict management scenarios.
• Individuals react with defiance or threat to meta-stereotypes, which can create conflicts or avoidance behaviors.
• These stereotypes are internalized , causing individuals to conform to behavioral expectations.

Consequently , perceiving oneself as belonging to the same generation as colleagues positively impacts work-related attitudes and behaviors.

Conversely, employees who work with colleagues perceived as belonging to different generations report more negative stereotypes, an increased perception of an age-discriminatory climate, and more negative work attitudes and behaviors.

These conclusions, however, are a direct result of artificial and arbitrary segmentation into distinct generations. While most individuals do not identify with a specific generation, these classifications are often promoted by pseudo-experts seeking recognition. These individuals fuel the fire of stereotypes, obscuring the fact that, intrinsically, generational differences do not exist. Additionally, people tend to notice differences rather than similarities, especially when those differences pose problems. Thus, if we believe in generational disparities, we're likely to find evidence to support them, driven by confirmation bias . It would be wiser to minimize the existence of these differences rather than exacerbate them through coarse categorizations and hasty generalizations.

Everyone wants the same thing

Instead of perpetuating a misleading view of generational gaps in hopes of managing them more effectively, it's better to focus on a fundamental understanding of work engagement, rooted in historical and scientific perspectives.

At its core, engagement manifests as a process of psychological identification with work, where individuals find their identity in their activities. It involves how individuals interpret, value, and appropriate their work, find meaning in it, and how their work meets their needs.

Understanding engagement, therefore, requires grasping the universal needs of everyone. From an anthropological standpoint, human beings share three universal needs intrinsic to our nature, society, and evolution. The first is the need for community and social connection, reflecting our social nature. The second need is for personal progression: a desire for advancement and distinction within our hierarchical structures. Finally, the third need is to make sense of the world, to find a cause, and to have an impact. This need is the lens through which we analyze and interpret the world.

These three needs (community, career, and cause) constitute the core values that drive everyone at work. Regardless of their supposed generational affiliation, everyone seeks to identify the what, who, and why of their professional activity.

Emeric Kubiak is a researcher specializing in personality and Head of Science at AssessFirst.

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Generational differences

ABSTRACT Generational differences are the subject of much popular speculation but relatively little substantive research. Among the speculations are suggestions that instructional designers should take generational differences into account when developing instruction and that games and simulations will be more effective learning environments with today's younger generation than they have been with earlier ones.

Related Papers

Thomas C Reeves

In the Spring of 2006, I conducted a literature review with my graduate research assistant, Ms. Eunjung Oh, to address questions related to generational differences in the contemporary workforce that might justify the specification of a new instructional design model and/or the development of innovative instructional methods and technologies to accommodate the generational differences found to exist.

Jerónimo Francisco

Celia Russell

Intergenerational design is a child-centred process in which children act as co-designers for their own educational software. One of its foremost methodologies, participatory design, stresses direct input by the child in the same way an expert in any target knowledge field may be consulted when software is produced. To date, research on the use of thesis technique has concentrated on scientific subject domains. In this thesis, we examine the proposition that intergenerational design can be used to produce a viable resource based on a museum collection. In line with the methodology, the site was co-designed by 29 ten-year olds. The children developed scenarios and storyboards for the site using low technology materials. The designs were then programmed up by the author and returned to the children for re-iterative prototyping. The final result is a vivid and peppy site which was rated highly by its child users.

Interactive Media Use and Youth: Learning, Knowledge Exchange and Behavior

Regina Kaplan-Rakowski

The purpose of this chapter is to provide educators, researchers, and policy-makers with an overview on how modern technology has been influencing the learning styles of the “neomillennial” generation. The authors begin by describing the demographic and cultural characteristics of the neomillennial generation and how they differ from preceding generations. They follow with a discussion of how neomillennial learning styles have changed as a result of new technology. The authors then take a detailed look at two examples of how modern technology can be used to design novel learning approaches: digital game-based learning and learning in virtual worlds. Disadvantages, difficulties, and barriers to acceptance of these approaches are then examined. They conclude by summarizing the characteristics of the neomillennial generation and why technological changes are likely to influence educational practices for them, as well as how these changes fit in the broader context of educational theory.

Linda Kaiser

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Education and Information Technologies

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• Published: 06 May 2024

APOE4 homozygozity represents a distinct genetic form of Alzheimer’s disease

• Juan Fortea   ORCID: orcid.org/0000-0002-1340-638X 1 , 2 , 3   na1 ,
• Jordi Pegueroles   ORCID: orcid.org/0000-0002-3554-2446 1 , 2 ,
• Daniel Alcolea   ORCID: orcid.org/0000-0002-3819-3245 1 , 2 ,
• Olivia Belbin   ORCID: orcid.org/0000-0002-6109-6371 1 , 2 ,
• Oriol Dols-Icardo   ORCID: orcid.org/0000-0003-2656-8748 1 , 2 ,
• Lídia Vaqué-Alcázar 1 , 4 ,
• Laura Videla   ORCID: orcid.org/0000-0002-9748-8465 1 , 2 , 3 ,
• Juan Domingo Gispert 5 , 6 , 7 , 8 , 9 ,
• Marc Suárez-Calvet   ORCID: orcid.org/0000-0002-2993-569X 5 , 6 , 7 , 8 , 9 ,
• Sterling C. Johnson   ORCID: orcid.org/0000-0002-8501-545X 10 ,
• Reisa Sperling   ORCID: orcid.org/0000-0003-1535-6133 11 ,
• Alexandre Bejanin   ORCID: orcid.org/0000-0002-9958-0951 1 , 2 ,
• Alberto Lleó   ORCID: orcid.org/0000-0002-2568-5478 1 , 2 &
• Víctor Montal   ORCID: orcid.org/0000-0002-5714-9282 1 , 2 , 12   na1  

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• Alzheimer's disease
• Predictive markers

This study aimed to evaluate the impact of APOE4 homozygosity on Alzheimer’s disease (AD) by examining its clinical, pathological and biomarker changes to see whether APOE4 homozygotes constitute a distinct, genetically determined form of AD. Data from the National Alzheimer’s Coordinating Center and five large cohorts with AD biomarkers were analyzed. The analysis included 3,297 individuals for the pathological study and 10,039 for the clinical study. Findings revealed that almost all APOE4 homozygotes exhibited AD pathology and had significantly higher levels of AD biomarkers from age 55 compared to APOE3 homozygotes. By age 65, nearly all had abnormal amyloid levels in cerebrospinal fluid, and 75% had positive amyloid scans, with the prevalence of these markers increasing with age, indicating near-full penetrance of AD biology in APOE4 homozygotes. The age of symptom onset was earlier in APOE4 homozygotes at 65.1, with a narrower 95% prediction interval than APOE3 homozygotes. The predictability of symptom onset and the sequence of biomarker changes in APOE4 homozygotes mirrored those in autosomal dominant AD and Down syndrome. However, in the dementia stage, there were no differences in amyloid or tau positron emission tomography across haplotypes, despite earlier clinical and biomarker changes. The study concludes that APOE4 homozygotes represent a genetic form of AD, suggesting the need for individualized prevention strategies, clinical trials and treatments.

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APOE4/4 is linked to damaging lipid droplets in Alzheimer’s disease microglia

The Amyloid-β Pathway in Alzheimer’s Disease

Dynamics of cognitive variability with age and its genetic underpinning in NIHR BioResource Genes and Cognition cohort participants

Data availability.

Access to tabular data from ADNI ( https://adni.loni.usc.edu/ ), OASIS ( https://oasis-brains.org/ ), A4 ( https://ida.loni.usc.edu/collaboration/access/appLicense.jsp ) and NACC ( https://naccdata.org/ ) can be requested online, as publicly available databases. All requests will be reviewed by each studyʼs scientific board. Concrete inquiries to access the WRAP ( https://wrap.wisc.edu/data-requests-2/ ) and ALFA + ( https://www.barcelonabeta.org/en/alfa-study/about-the-alfa-study ) cohort data can be directed to each study team for concept approval and feasibility consultation. Requests will be reviewed to verify whether the request is subject to any intellectual property.

Code availability

All statistical analyses and raw figures were generated using R (v.4.2.2). We used the open-sourced R packages of ggplot2 (v.3.4.3), dplyr (v.1.1.3), ggstream (v.0.1.0), ggpubr (v.0.6), ggstatsplot (v.0.12), Rmisc (v.1.5.1), survival (v.3.5), survminer (v.0.4.9), gtsummary (v.1.7), epitools (v.0.5) and statsExpression (v.1.5.1). Rscripts to replicate our findings can be found at https://gitlab.com/vmontalb/apoe4-asdad (ref. 32 ). For neuroimaging analyses, we used Free Surfer (v.6.0) and ANTs (v.2.4.0).

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Acknowledgements

We acknowledge the contributions of several consortia that provided data for this study. We extend our appreciation to the NACC, the Alzheimer’s Disease Neuroimaging Initiative, The A4 Study, the ALFA Study, the Wisconsin Register for Alzheimer’s Prevention and the OASIS3 Project. Without their dedication to advancing Alzheimer’s disease research and their commitment to data sharing, this study would not have been possible. We also thank all the participants and investigators involved in these consortia for their tireless efforts and invaluable contributions to the field. We also thank the institutions that funded this study, the Fondo de Investigaciones Sanitario, Carlos III Health Institute, the Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas and the Generalitat de Catalunya and La Caixa Foundation, as well as the NIH, Horizon 2020 and the Alzheimer’s Association, which was crucial for this research. Funding: National Institute on Aging. This study was supported by the Fondo de Investigaciones Sanitario, Carlos III Health Institute (INT21/00073, PI20/01473 and PI23/01786 to J.F., CP20/00038, PI22/00307 to A.B., PI22/00456 to M.S.-C., PI18/00435 to D.A., PI20/01330 to A.L.) and the Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas Program 1, partly jointly funded by Fondo Europeo de Desarrollo Regional, Unión Europea, Una Manera de Hacer Europa. This work was also supported by the National Institutes of Health grants (R01 AG056850; R21 AG056974, R01 AG061566, R01 AG081394 and R61AG066543 to J.F., S10 OD025245, P30 AG062715, U54 HD090256, UL1 TR002373, P01 AG036694 and P50 AG005134 to R.S.; R01 AG027161, R01 AG021155, R01 AG037639, R01 AG054059; P50 AG033514 and P30 AG062715 to S.J.) and ADNI (U01 AG024904), the Department de Salut de la Generalitat de Catalunya, Pla Estratègic de Recerca I Innovació en Salut (SLT006/17/00119 to J.F.; SLT002/16/00408 to A.L.) and the A4 Study (R01 AG063689, U24 AG057437 to R.A.S). It was also supported by Fundación Tatiana Pérez de Guzmán el Bueno (IIBSP-DOW-2020-151 o J.F.) and Horizon 2020–Research and Innovation Framework Programme from the European Union (H2020-SC1-BHC-2018-2020 to J.F.; 948677 and 847648 to M.S.-C.). La Caixa Foundation (LCF/PR/GN17/50300004 to M.S.-C.) and EIT Digital (Grant 2021 to J.D.G.) also supported this work. The Alzheimer Association also participated in the funding of this work (AARG-22-923680 to A.B.) and A4/LEARN Study AA15-338729 to R.A.S.). O.D.-I. receives funding from the Alzheimer’s Association (AARF-22-924456) and the Jerome Lejeune Foundation postdoctoral fellowship.

Author information

These authors contributed equally: Juan Fortea, Víctor Montal.

Authors and Affiliations

Sant Pau Memory Unit, Hospital de la Santa Creu i Sant Pau - Biomedical Research Institute Sant Pau, Barcelona, Spain

Juan Fortea, Jordi Pegueroles, Daniel Alcolea, Olivia Belbin, Oriol Dols-Icardo, Lídia Vaqué-Alcázar, Laura Videla, Alexandre Bejanin, Alberto Lleó & Víctor Montal

Centro de Investigación Biomédica en Red de Enfermedades Neurodegenerativas. CIBERNED, Barcelona, Spain

Juan Fortea, Jordi Pegueroles, Daniel Alcolea, Olivia Belbin, Oriol Dols-Icardo, Laura Videla, Alexandre Bejanin, Alberto Lleó & Víctor Montal

Barcelona Down Medical Center, Fundació Catalana Síndrome de Down, Barcelona, Spain

Juan Fortea & Laura Videla

Department of Medicine, Faculty of Medicine and Health Sciences, Institute of Neurosciences, University of Barcelona, Barcelona, Spain

Lídia Vaqué-Alcázar

Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain

Juan Domingo Gispert & Marc Suárez-Calvet

Neurosciences Programme, IMIM - Hospital del Mar Medical Research Institute, Barcelona, Spain

Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Barcelona, Spain

Centro de Investigación Biomédica en Red Bioingeniería, Biomateriales y Nanomedicina. Instituto de Salud carlos III, Madrid, Spain

Centro Nacional de Investigaciones Cardiovasculares (CNIC), Madrid, Spain

Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI, USA

Sterling C. Johnson

Brigham and Women’s Hospital Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA

Reisa Sperling

Barcelona Supercomputing Center, Barcelona, Spain

Víctor Montal

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Contributions

J.F. and V.M. conceptualized the research project and drafted the initial manuscript. V.M., J.P. and J.F. conducted data analysis, interpreted statistical findings and created visual representations of the data. O.B. and O.D.-I. provided valuable insights into the genetics of APOE. L.V., A.B. and L.V.-A. meticulously reviewed and edited the manuscript for clarity, accuracy and coherence. J.D.G., M.S.-C., S.J. and R.S. played pivotal roles in data acquisition and securing funding. A.L. and D.A. contributed to the study design, offering guidance and feedback on statistical analyses, and provided critical review of the paper. All authors carefully reviewed the manuscript, offering pertinent feedback that enhanced the study’s quality, and ultimately approved the final version.

Corresponding authors

Correspondence to Juan Fortea or Víctor Montal .

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Competing interests.

S.C.J. has served at scientific advisory boards for ALZPath, Enigma and Roche Diagnostics. M.S.-C. has given lectures in symposia sponsored by Almirall, Eli Lilly, Novo Nordisk, Roche Diagnostics and Roche Farma, received consultancy fees (paid to the institution) from Roche Diagnostics and served on advisory boards of Roche Diagnostics and Grifols. He was granted a project and is a site investigator of a clinical trial (funded to the institution) by Roche Diagnostics. In-kind support for research (to the institution) was received from ADx Neurosciences, Alamar Biosciences, Avid Radiopharmaceuticals, Eli Lilly, Fujirebio, Janssen Research & Development and Roche Diagnostics. J.D.G. has served as consultant for Roche Diagnostics, receives research funding from Hoffmann–La Roche, Roche Diagnostics and GE Healthcare, has given lectures in symposia sponsored by Biogen, Philips Nederlands, Esteve and Life Molecular Imaging and serves on an advisory board for Prothena Biosciences. R.S. has received personal consulting fees from Abbvie, AC Immune, Acumen, Alector, Bristol Myers Squibb, Janssen, Genentech, Ionis and Vaxxinity outside the submitted work. O.B. reported receiving personal fees from Adx NeuroSciences outside the submitted work. D.A. reported receiving personal fees for advisory board services and/or speaker honoraria from Fujirebio-Europe, Roche, Nutricia, Krka Farmacéutica and Esteve, outside the submitted work. A.L. has served as a consultant or on advisory boards for Almirall, Fujirebio-Europe, Grifols, Eisai, Lilly, Novartis, Roche, Biogen and Nutricia, outside the submitted work. J.F. reported receiving personal fees for service on the advisory boards, adjudication committees or speaker honoraria from AC Immune, Adamed, Alzheon, Biogen, Eisai, Esteve, Fujirebio, Ionis, Laboratorios Carnot, Life Molecular Imaging, Lilly, Lundbeck, Perha, Roche and outside the submitted work. O.B., D.A., A.L. and J.F. report holding a patent for markers of synaptopathy in neurodegenerative disease (licensed to Adx, EPI8382175.0). The remaining authors declare no competing interests.

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Fortea, J., Pegueroles, J., Alcolea, D. et al. APOE4 homozygozity represents a distinct genetic form of Alzheimer’s disease. Nat Med (2024). https://doi.org/10.1038/s41591-024-02931-w

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Imagine you and a friend are playing a game where your goal is to communicate secret messages to each other using only cryptic sentences. Your friend's job is to guess the secret message behind your sentences. Sometimes, you give clues directly, and other times, your friend has to guess the message by asking yes-or-no questions about the clues you've given. The challenge is that both of you want to make sure you're understanding each other correctly and agreeing on the secret message.

MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) researchers have created a similar "game" to help improve how AI understands and generates text. It is known as a “consensus game” and it involves two parts of an AI system — one part tries to generate sentences (like giving clues), and the other part tries to understand and evaluate those sentences (like guessing the secret message).

The researchers discovered that by treating this interaction as a game, where both parts of the AI work together under specific rules to agree on the right message, they could significantly improve the AI's ability to give correct and coherent answers to questions. They tested this new game-like approach on a variety of tasks, such as reading comprehension, solving math problems, and carrying on conversations, and found that it helped the AI perform better across the board.

Traditionally, large language models answer one of two ways: generating answers directly from the model (generative querying) or using the model to score a set of predefined answers (discriminative querying), which can lead to differing and sometimes incompatible results. With the generative approach, "Who is the president of the United States?" might yield a straightforward answer like "Joe Biden." However, a discriminative query could incorrectly dispute this fact when evaluating the same answer, such as "Barack Obama."

So, how do we reconcile mutually incompatible scoring procedures to achieve coherent, efficient predictions? 

"Imagine a new way to help language models understand and generate text, like a game. We've developed a training-free, game-theoretic method that treats the whole process as a complex game of clues and signals, where a generator tries to send the right message to a discriminator using natural language. Instead of chess pieces, they're using words and sentences," says Athul Jacob, an MIT PhD student in electrical engineering and computer science and CSAIL affiliate. "Our way to navigate this game is finding the 'approximate equilibria,' leading to a new decoding algorithm called 'equilibrium ranking.' It's a pretty exciting demonstration of how bringing game-theoretic strategies into the mix can tackle some big challenges in making language models more reliable and consistent."

When tested across many tasks, like reading comprehension, commonsense reasoning, math problem-solving, and dialogue, the team's algorithm consistently improved how well these models performed. Using the ER algorithm with the LLaMA-7B model even outshone the results from much larger models. "Given that they are already competitive, that people have been working on it for a while, but the level of improvements we saw being able to outperform a model that's 10 times the size was a pleasant surprise," says Jacob. 

"Diplomacy," a strategic board game set in pre-World War I Europe, where players negotiate alliances, betray friends, and conquer territories without the use of dice — relying purely on skill, strategy, and interpersonal manipulation — recently had a second coming. In November 2022, computer scientists, including Jacob, developed “Cicero,” an AI agent that achieves human-level capabilities in the mixed-motive seven-player game, which requires the same aforementioned skills, but with natural language. The math behind this partially inspired the Consensus Game. 

While the history of AI agents long predates when OpenAI's software entered the chat in November 2022, it's well documented that they can still cosplay as your well-meaning, yet pathological friend. 

The consensus game system reaches equilibrium as an agreement, ensuring accuracy and fidelity to the model's original insights. To achieve this, the method iteratively adjusts the interactions between the generative and discriminative components until they reach a consensus on an answer that accurately reflects reality and aligns with their initial beliefs. This approach effectively bridges the gap between the two querying methods. 

In practice, implementing the consensus game approach to language model querying, especially for question-answering tasks, does involve significant computational challenges. For example, when using datasets like MMLU, which have thousands of questions and multiple-choice answers, the model must apply the mechanism to each query. Then, it must reach a consensus between the generative and discriminative components for every question and its possible answers. 

The system did struggle with a grade school right of passage: math word problems. It couldn't generate wrong answers, which is a critical component of understanding the process of coming up with the right one. 

“The last few years have seen really impressive progress in both strategic decision-making and language generation from AI systems, but we’re just starting to figure out how to put the two together. Equilibrium ranking is a first step in this direction, but I think there’s a lot we’ll be able to do to scale this up to more complex problems,” says Jacob.   

An avenue of future work involves enhancing the base model by integrating the outputs of the current method. This is particularly promising since it can yield more factual and consistent answers across various tasks, including factuality and open-ended generation. The potential for such a method to significantly improve the base model's performance is high, which could result in more reliable and factual outputs from ChatGPT and similar language models that people use daily. 

"Even though modern language models, such as ChatGPT and Gemini, have led to solving various tasks through chat interfaces, the statistical decoding process that generates a response from such models has remained unchanged for decades," says Google Research Scientist Ahmad Beirami, who was not involved in the work. "The proposal by the MIT researchers is an innovative game-theoretic framework for decoding from language models through solving the equilibrium of a consensus game. The significant performance gains reported in the research paper are promising, opening the door to a potential paradigm shift in language model decoding that may fuel a flurry of new applications."

Jacob wrote the paper with MIT-IBM Watson Lab researcher Yikang Shen and MIT Department of Electrical Engineering and Computer Science assistant professors Gabriele Farina and Jacob Andreas, who is also a CSAIL member. They presented their work at the International Conference on Learning Representations (ICLR) earlier this month, where it was highlighted as a "spotlight paper." The research also received a “best paper award” at the NeurIPS R0-FoMo Workshop in December 2023.

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Press mentions, quanta magazine.

MIT researchers have developed a new procedure that uses game theory to improve the accuracy and consistency of large language models (LLMs), reports Steve Nadis for Quanta Magazine . “The new work, which uses games to improve AI, stands in contrast to past approaches, which measured an AI program’s success via its mastery of games,” explains Nadis. 

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Related Links

• Article: "Game Theory Can Make AI More Correct and Efficient"
• Jacob Andreas
• Athul Paul Jacob
• Language & Intelligence @ MIT
• Computer Science and Artificial Intelligence Laboratory (CSAIL)
• Department of Electrical Engineering and Computer Science
• MIT-IBM Watson AI Lab

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• Computer science and technology
• Artificial intelligence
• Human-computer interaction
• Natural language processing
• Game theory
• Electrical Engineering & Computer Science (eecs)

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Haifeng Xu Wins Best Paper Award at Leading AI Conference for Pioneering Research on Mechanism Design for LLMs

As this year’s Web Conference is under way, pioneering research work by Assistant Professor of Computer Science and Data Science Haifeng Xu and his collaborators has been announced as the winner for their prestigious Best Paper Award.

Xu’s paper, entitled “ Mechanism Design for Large Language Models ,” was selected from amongst 2008 submissions.

This paper lays out a newly developed method to aggregate language generations from multiple self-interested LLM agents into a single text generation. It does so by accounting for these LLM agents’ self-interests in an incentive-compatible way. As summarized in the meta review, “the review team unanimously finds the paper novel, well-executed, and … has potential to be a landmark paper sparking a new line of research linking LLMs and mechanism design.”

This paper is a joint work with Google Researchers. The technology Xu and his team developed has been tested on Google’s LLM model Bard and Xu reports that it performs very well. According to Xu, the nice (and often very rare) combination of both strong theoretical development and real-world implementation on Bard is probably a key reason for the paper to be named the Best Paper.

Congratulations, Haifeng!

This article was originally published by the Data Science Institute.

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