Scientific knowledge is driven by the research questions we ask. As Bergman and Jean (2016) argue, if wage earners, contract workers, and other workers are underrepresented in our research samples, we're likely to fail to investigate phenomena of importance to these populations. By focusing primarily on salaried and managerial workers, we limit the research questions we ask and fail to consider important caveats to industrial-organizational theories. As Bergman and Jean note, we cannot assume that the experiences observed in any group will generalize to other groups. We elaborate on this argument to discuss how failing to take into account the diverse perspectives and needs of the full labor market leads us to fail to ask relevant research questions. Specifically, we discuss how considering unexplored labor market perspectives opens new questions in the area of ethics management.

Bergman and Jean (2016) include freelancers as one of the categories of workers who are understudied in the industrial and organizational (I-O) psychology literature. This neglect is particularly striking given the attention paid by the popular media and by politicians to the rise of the “gig economy,” comprising primarily short-term independent freelance workers (e.g., Cook, 2015; Kessler, 2014; Scheiber, 2014; Warner, 2015). This may be due in part to challenges involved in accessing and researching this population, as discussed by Bergman and Jean, but it may also arise from complexities in defining and conceptualizing freelance work, as well as from misunderstandings about the nature of the work now performed by many people who are considered freelancers. Major topics of interest to I-O psychologists such as organizational attraction, job satisfaction, and turnover may seem at first glance to lack relevance to the study of workers who are officially classified as self-employed. But there is substantial opportunity for I-O psychologists and other behaviorally oriented organizational researchers to contribute to our understanding of the growing number of people who earn all or some of their income by freelancing.

Although we share Bergman and Jean's (2016) concerns about the representativeness of samples in the organizational sciences, we are mindful of the ever changing nature of the job market. New jobs are created from technological innovation while others become obsolete and disappear or are functionally transformed. These shifts in employment patterns produce both opportunities and challenges for organizational researchers addressing the problem of the representativeness in our working population samples. On one hand, it is understood that whatever we do, we will always be playing catch-up with the market. On the other hand, it is possible that we can leverage new technologies in order to react to such changes more quickly. As an example, in Bergman and Jean's commentary, they suggested making use of crowdsourcing websites or Internet panels in order to gain access to undersampled populations. Although we agree there is an opportunity to conduct much research of interest to organizational scholars in these settings, we also would point out that these types of samples come with their own sampling challenges. To illustrate these challenges, we examine sampling issues for Amazon's Mechanical Turk (MTurk), which is currently the most used portal for psychologists and organizational scholars collecting human subjects data online. Specifically, we examine whether MTurk workers are “workers” as defined by Bergman and Jean, whether MTurk samples are WEIRD (Western, educated, industrialized, rich, and democratic; Henrich, Heine, & Norenzayan, 2010), and how researchers may creatively utilize the sample characteristics.

We concur with Bergman and Jean (2016) that worker samples tend to be underrepresented in organizational research, which could have deleterious effects on the conclusions and practices derived from this research. However, we argue that the effects of underrepresentation could vary considerably by organizational domain (e.g., selection vs. leadership vs. satisfaction). Our focus with this commentary is to address its effects on organizational selection, particularly with employment interviews, including issues such as criterion-related validity, prevalence of student samples, and the four criteria outlined by the focal authors (overlooked phenomena, differences in construct meaning, worker status, and human capital patterns).

Bergman and Jean (2016) rightly argue that published research in industrial–organizational (I-O) psychology often underrepresents low-wage and frontline employees in favor of professional workers and management. One possible consequence of this bias is that I-O research may unintentionally marginalize workplace phenomena that impact employees professionally and personally. One example offered by Bergman and Jean is economic tenuousness, a work–life stressor that is more likely to be experienced by low-income and frontline employees. The recent growth in the proportion of individuals employed in low-wage jobs (Albelda & Carr, 2012) reinforces the need to explore the impact of the publication rift between the science and practice of I-O psychology.

The purpose of our commentary is to expand the conversation started by the focal article (Bergman & Jean, 2016) by providing a practitioner perspective, especially focusing on practitioners who work in a government or military context. We believe this perspective is valuable because (a) interventions and research projects involving nonprofessional/nonmanagement workers are already regularly being conducted in this context; (b) there are likely to be other causes of a lack of focus on broader populations of workers in this context than those highlighted by the focal article, some of which may differ between practitioners and academics; and (c) we believe practitioners in this context have much to contribute to solving the potential problems that the focal article highlights.

Bergman and Jean (2016) suggest that an underrepresentation of nonmanagerial workers in industrial and organizational (I-O) research is hindering our contribution to practice and ultimately our legitimacy as a discipline. I agree with the target article authors’ overarching message insomuch as it serves as a reminder that we, as I-O psychologists, have responsibilities to understand and improve the work experience of a diverse range of populations. However, I also believe the authors’ claim that nonmanagerial workers are underrepresented may be overstated. Moreover, I offer a perspective suggesting that the current supply of research being produced in the name of I-O psychology is driven by real world demand, that this naturally occurring phenomenon is what maximizes our impact on practice, and that ignoring this phenomenon is actually what hinders the legitimacy of applied disciplines.

Bergman and Jean (2016) skillfully summarize how the industrial and organizational (I-O) psychology literature generally overrepresents salaried, core, managerial, professional, and executive employees. We concur that that the underrepresentation of traditional workers (i.e., wage earners, laborers, first-line personnel, freelancers, contract workers, and other workers outside managerial, professional, and executive positions) can negatively affect our science. In our commentary we extend the arguments of Bergman and Jean by (a) discussing the appropriate use of samples, which are determined by study goals and hypotheses, and (b) further examining samples in occupational health psychology (OHP) and related journals, which generally require worker samples.

The focal article written by Bergman and Jean (2016) draws attention to a critical void in the industrial and organizational (I-O) psychology domain: the study of low- and medium-skill workers. Although segmenting employees based on their job status may provide new conceptualizations of employee work experiences, this approach may not provide the nuanced view necessary to fully comprehend the many ways in which employees differentially experience the workplace. Within this category of workers, experiences may vary based on employees’ race, gender, socioeconomic status (SES), or other identity-defining characteristics, and these person-specific identities may interact with one another. An intersectional research approach provides a foundation on which researchers can more fully understand how individuals’ multiple social identities interact to affect their workplace experiences. In the commentary that follows, we provide an overview of intersectional research and describe how such a perspective would lead to meaningful developments within I-O psychology.

Bergman and Jean (2016) compare published industrial and organizational (I-O) literature with labor statistics, demonstrating an underrepresentation of “workers” (i.e., “wage earners, laborers, first-line personnel, freelancers, contract workers”) relative to managerial, professional, and executive positions. They note that one of four ways in which worker underrepresentation undermines the utility of I-O psychology research is that we could miss the role of worker status as a main effect on important variables and/or a moderator of key relationships, which could hinder understanding of important phenomena as they relate to workers. We applaud the emphasis on workers and agree with this basic premise.

In Industrial and Organizational Psychology volume 8, issue 3, a commentary to the focal article Ree et al. was unfortunately omitted. This commentary, “I've Found It, but What Does It Mean? On the Importance of Theory in Identifying Dominant General Factors,” by Zhenyu Yuan, is reproduced in the following pages.

In their focal article, Ree, Carretta, and Teachout (2015) based their definition of a dominant general factor (DGF) on two criteria: (a) A DGF should be the largest source of reliable variance; (b) it is influencing every variable measuring the construct. Although detailed attention has been paid to the statistical properties of a DGF, I believe another criterion of equal if not greater importance is the theoretical justification to expect a DGF in the measurement of a construct. In the following commentary, I will highlight the importance of theory as another important criterion when determining the meaningfulness and usefulness of DGFs, discuss the risks of creating a DGF without any theoretical guidance, and elaborate on the complexities surrounding job performance as a detailed example to illustrate why theory is important before extracting a DGF from performance ratings.