The goal of focal articles in Industrial and Organizational Psychology: Perspectives on Science and Practice is to present new ideas or different takes on existing ideas and stimulate a conversation in the form of comment articles that extend the arguments in the focal article or that present new ideas stimulated by those articles. The two focal articles in this issue stimulated a wide range of reactions and a good deal of constructive input.

Stereotypes about generational differences in the workplace abound, and interventions for helping organizations and managers to deal with these supposed differences are increasing. In addition to popular press articles describing the differences and extolling the practices and strategies to deal with them, there are a growing number of researchers who are attempting to establish that there are such differences. Overall, however, there is little solid empirical evidence supporting generationally based differences and almost no theory behind why such differences should even exist. The purposes of this focal article are to explore the myths surrounding generations, to review the risks in using generations in organizational decisions, and to make recommendations for practitioners and researchers on how to proceed in this area.

We propose that generational differences are meaningful despite some theoretical and methodological challenges (cf. Costanza & Finkelstein, 2015). We will address five main issues: operationalizing generations, measuring generational differences, theoretical models of generations, mechanisms of generational change, and the importance of science versus stereotypes.

The focal article by Costanza and Finkelstein (2015) shed a light on the current status of generational research at work that often treats generation as an age grouping variable. Although the authors primarily focused on the applicability of research findings to the current workforce, I believe the benefits of studying and understanding generations should not be limited to this purpose. The goal of this commentary is twofold. First, I would like to revisit the concept of generation and the purposes of studying generations at work. Second, given the problems with generational research presented by Costanza and Finkelstein, I would like to propose potential actions that we can take.

Costanza and Finkelstein (2015) question whether there really are generational differences in the workplace. They argue that the generation construct is not properly operationalized (often confounding age, period, and cohort factors) and that generational research is atheoretical and plagued by methodological problems. We agree that generational research is largely guilty as charged. We also share their concern regarding the generationally based interventions that are being sold to, and adopted by, managers—interventions that are often little more than fads based on popular myths and folklore. We are also in full agreement with the potential significant harm that can be associated with stereotyping groups of individuals.

Belief in generational differences is nothing new. In ancient times, generational differences were seen as a threat to productivity, and efforts were made to change them. Bertman (1976) writes, “as early as the twenty-fifth century BC, the older generation in authoritarian Egypt had to ‘work at it’ to mold the younger generation in its own image.” Yet, Costanza and Finkelstein (2015) point out that “there is minimal empirical evidence actually supporting generationally based differences” and “no sufficient explanation for why such differences should even exist.” One explanation for the prevailing belief in generation-based differences is systematic biases in social perception.

I agree with Costanza and Finkelstein (2015) that it is futile to further invest in the study of generational differences in the work context due to a lack of appropriate theory and methods. The key problem with the generations concept is that splitting continuous variables such as age or time into a few discrete units involves arbitrary cutoffs and atheoretical groupings of individuals (e.g., stating that all people born between the early 1960s and early 1980s belong to Generation X). As noted by methodologists, this procedure leads to a loss of information about individuals and reduced statistical power (MacCallum, Zhang, Preacher, & Rucker, 2002). Due to these conceptual and methodological limitations, I regard it as very difficult if not impossible to develop a “comprehensive theory of generations” (Costanza & Finkelstein, p. 20) and to rigorously examine generational differences at work in empirical studies.

The topic of generational differences in the workplace has been immensely popular over the past decade, spawning a large number of academic publications and a far greater number of consulting reports, popular press books, magazine articles, media reports, blogs, and infographics. Indeed, a new industry of consultants and public speakers seems to have emerged primarily to capitalize on the popularity of this topic. As Costanza and Finkelstein (2015) note, the research on this “hot topic” has often seemed opportunistic, lacking rigor and depth. The relative ease of cutting existing cross-sectional data by age and calling it a generation study has tempted researchers to hop on the bandwagon, resulting in a large number of empirical studies with nearly identical literature reviews that overrely on popular press and opinion-based literature. There has been a lamentable tendency toward blind empiricism with little or no connection to theory, as has been stated elsewhere (Lyons & Kuron, 2014; Parry & Urwin, 2011).

We agree with and expand on the points made by Costanza and Finkelstein (2015) regarding the definition of “generation” and its measurement, the lack of theory in this area; the harmful effects of propagating generationally based differences to organizations, society, and individuals; and the future directions to make this line of research more applicable to organizations and the workplace. Examining age differences at work through the lens of well-established within-person changes in physical ability (e.g., Maertens, Putter, Chen, Diehl, & Huang, 2012), cognition (e.g., Schaie, 1994), motivation (e.g., Kooij, De Lange, Jansen, Kanfer, & Dikkers, 2011), personality (e.g., Roberts, Walton, & Viechtbauer, 2006), and lifespan development theories (e.g., Baltes & Baltes, 1990; Carstensen, Isaacowitz, & Charles, 1999) would likely be more productive than using loose, relatively atheoretical generational stereotypes to understand age differences at work.

Costanza and Finkelstein (2015) have justly argued that cross-sectional operationalizations of generational groups represent a confound that constrains the ability to unequivocally separate the effects of age and period. A related statistical argument against this practice bears consideration as well. Namely, cross-sectional operationalizations of generations have the potential to unduly inflate type two-error rates when compared with the analysis of simple age effects. This is a problem because true age effects can be erroneously ignored in studies where age is artificially split into assumed generational groups. Indeed, the argument against artificially bifurcating continuous data is not new (e.g., Cohen, 1983), however past attempts to make inferences about generational effects in cross-sectional designs present an opportunity to investigate the particularly insidious nature of this practice and its implications. To demonstrate the problem at hand, let us consider a brief empirical example by virtue of a simulation study.

The notion of a “Millennial” generation, much like a “Generation X” or the “Baby Boom” generation, with a strong coherence in terms of values and norms that differ from previous cohorts, has been of dependable interest in the popular press. However, given what we know regarding the proportion of trait expression due to sources largely immune to cohort effects (e.g., large genetic contributions), how difficult it is for us to systematically influence their expression (e.g., small long-term parental effects), and the massive variation within groups, the meta-analytic work of Costanza, Fraser, Badger, Severt, and Gade (2012) underscores what should already be known from first principles; generation or cohorts are inevitably a poor predictor of anything. The literature on ingroup/outgroup bias (Hogg & Abrams, 1990), stereotype formation (Mackie, Hamilton, Susskind, & Rosselli, 1996), and reconstructive memory issues (Schacter, 1999) provides ample underlying evidence for how these generational overgeneralizations form.

Costanza and Finkelstein (2015) make a number of important observations in their exploration of how generation-based differences are understood by academics and practitioners. The absence of a unifying theory, the lack of a clear pattern of findings, and the conceptual ambiguity about generational membership have all limited the utility of generational membership to researchers, while the practice of describing large groups of individuals in generational terms has become enormously popular outside of academia. Certainly, there are several topics that are popular among the public and widely used by businesses despite being viewed negatively by academics; however, there is some danger in the widespread perception that group differences between individuals of different ages can be explained by generational membership. Although it might be tempting to consider these generational stereotypes as simply innocuous misperceptions supervisors may hold, they may, in fact, be quite harmful. The purpose of this article is to further expand on the potential for these stereotypes to be harmful for older and younger employees.

Although we agree with Costanza and Finkelstein (2015) that current research has failed to find consistent evidence of actual generational differences and that this research is limited methodologically, we suggest that at least some of these limitations could and should be addressed by future research before any firm conclusions are drawn. Further, the authors make an important but empirically untested assumption that generational stereotypes exist. We discuss why it is important to rigorously test this assumption. Finally, the authors assert that generational stereotypes are being “sold” as business strategy to organizations and managers and that that they should be neither sold nor used in the workplace. However, the authors’ recommendation is based on the premise that individuals acquire stereotypes from others and that they can easily suppress the use of their stereotypes. This advice runs counter to what research suggests about stereotypic processes. We more fully discuss each of these three points next.

As a workforce evolves, it is important for employers to consider how trends and changes in the workforce have implications for organizational processes such as recruiting, selecting, training, and rewarding a cohort of workers (Costanza & Finkelstein, 2015). This article examines the practical aspects of workforce changes on organizations’ recruitment efforts. Particularly in the face of recruiting shortages, it is important to understand how to best to develop a large and qualified pool of applicants.

Costanza and Finkelstein (2015) are correct to highlight the dangers of using generationally based stereotypes in organizations. Although popular, these stereotypes are related to a stigmatization based on group membership that can be pernicious and discriminatory. Costanza and Finkelstein are also correct in their assessment of the state of the literature on generational effects: theory and research is woefully lacking. Indeed, a recent review of research on generations at work characterized this research as descriptive and neither theoretical nor empirical (Lyons & Kuron, 2014). Yet, as pointed out by Costanza and Finkelstein, the idea of a generational identity is salient and even appealing to many people. Why would this be if it were completely devoid of psychological import? People seem to resonate with the idea that, to some extent at least, they are a product of their generation.

According to Costanza and Finkelstein (2015), the definition and measurement of generational membership could be two major problems inherent in the literature on generational differences. So far, researchers have defined generation in terms of groups of people belonging to the same cohorts, age groups, and experience of certain common events (Joshi, Dencker, & Franz, 2011). In this vein, generational difference is operationalized mostly as a categorical variable, and most researchers assign participants into generational categories based on date of birth. For instance, people born in 1958–1959 and 1973–1974 are typically divided into Boomers and Gen X, respectively (Twenge, Campbell, Hoffman, & Lance, 2010).

The purpose of this rebuttal to Costanza and Finkelstein (2015) is (a) to examine the findings surrounding the new generations at work, (b) to suggest the risks of not responding to change in generations with new talent strategies, and (c) to propose a new theory about the development of a new millennial culture and how practitioners and researchers may capitalize on the promise of a more positive and joyful workplace culture. We propose to accomplish these points by (a) citing recent global findings showing that Millennials (born after 1980) are turning away from companies with cultures that were designed for 20th century workers, (b) presenting a theory on the development of an emerging millennial culture that is based on positive industrial and organizational psychology, and (c) recommending that the method of innovation design teams be used to render workplaces compatible with the emerging culture.

General factors are found in the measurement of many human traits. The concept of dominant general factors (DGFs) is introduced to represent the magnitude of general factors within numerous content domains. DGFs are defined as coming from the largest sources of reliable variance and influencing every variable measuring the construct. Although these factors are most frequently found in measures of cognitive ability, they are not limited to cognitive abilities. Examples are provided for a variety of construct and content domains along with estimates of their DGF percentages, ranging from 38% to 92%. Several reasons for these results are offered, and a call for concerted research is made. Research that ignores DGFs by treating specific factors or constructs within a domain as if they were distinct and uncorrelated can lead to errors in interpretation.

In their focal article, Ree, Carretta, and Teachout (2015) argue that a large general factor (DGF), defined as the first component of an unrotated principal components solution, is characteristic of many different domains. In their view, ignoring the DGF in assessment and prediction in industrial and organizational (I-O) psychology is counterproductive. They readily acknowledge that the existence of a DGF does not preclude the existence of distinguishable specific factors. Their message is simply that the general factor (unrotated) frequently accounts for over half the reliable variance, and rather than ignore it, the reasons for it and the usefulness of it should be investigated. Further, the general factor is a construct, and all constructs must be supported by the various kinds of evidence that demonstrate construct validity. The DGF is no exception.

Relying on work described by Jackson (2003), Ree, Carretta, and Teachout (2015) recommended researchers use the first unrotated principal component associated with a principal components analysis (PCA) to estimate the strength of a general factor. Arguably, such a recommendation is based on rather old work. Furthermore, it is not a method that can be relied on to yield an accurate solution. For example, it is well known that the first component extracted from a correlation matrix of the Wechsler intelligence subtests is biased toward the verbal comprehension subtests (Ashton, Lee, & Vernon, 2001).