Top management teams (TMTs) of postmodern multinational corporations are increasingly confronted by new multiple context shifts defining our advancing global knowledge era (Grant, 2000). These include seven major context changes, not the least of which is knowledge becoming the new driver, not labor, machine, or money (Graen, in press). In addition, some complicating elements are the dysfunctions of human psychology under drastic changes in contexts (Snowden & Stanbridge, 2004). Clearly our 20th-century organizational designs and processing engineering systems are in need of continuous improvement to remain competitive. What TMTs seek are new practical ways to bring about needed changes in corporate structures and systems (Graen, 2007).

In their focal article “Gone fishing,” Ruggs et al. (2013) suggest that members of marginalized groups, such as ethnic/racial minorities other than Blacks, receive too little attention in top-tier industrial and organizational (I–O) psychology journals. Although we acknowledge the particularities each stigmatized group must face in the workplace and society, our commentary pleads for a better understanding of (a) general, underlying processes of discrimination across stigmatized groups while taking (b) complexity of discrimination into consideration. Whereas we concur with the authors regarding the need for more research, relevant studies on employment discrimination—even on the marginalized groups mentioned in the focal article—have been published, albeit many of them outside the focal I–O psychology journals mentioned and even outside the I–O psychology field. We believe that employment discrimination research might further benefit from (c) triangulation and (d) cross-cultural validation to advance insights, two other aspects that the focal article only slightly touched upon. We will illustrate these four challenges for future research on employment discrimination in I–O psychology with findings on hiring discrimination of Arab ethnics, one of the marginalized groups in the focal article, and one to which we have devoted considerable research effort.

In their focal article, Cucina, Walmsley, Gast, Martin, and Curtin (2017) raise an important and timely issue, but go too far in arguing that practitioners should abandon survey key driver analysis (SKDA) altogether. There are many valuable benefits to using SKDA, and rather than discontinuing the practice entirely, we suggest that a better approach is to focus on ways to make it even more valuable to organizations. It is true that this area is in need of further research, but instead of asking whether we are driving down the right road, perhaps the right question is, “Are we there yet?”

Guzzo, Fink, King, Tonidandel, and Landis (2015) review important issues—privacy, informed consent, and data/data analysis integrity—that are critical logistical considerations in any program of research with human subjects, including studies utilizing big data. Overall, we agree with the cautionary sentiment conveyed throughout the focal article; industrial and organizational (I-O) psychology researchers and practitioners should not assume that big data is a panacea, and many of our established disciplinary approaches for ensuring ethical and accurate research are applicable—or modifiable—in big data contexts. However, we believe that the conversation about big data in I-O psychology is broader than that reviewed by Guzzo et al., and we would like to further elaborate on the focal article. We present this commentary from our perspective as junior scholars entering the field at a critical time—a time when I-O psychology is becoming increasingly intertwined with big data science.

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.

Popular and influential social commentators have called organizations complicit in perpetuating weight-based bias and mistreatment. Although our field has advanced our understanding of the economic consequences of being fat at work (e.g., salary; job performance; and promotions), we urgently need more research on the interpersonal experiences of this swath of workers so that we can appropriately advise organizations. In this article, we describe how organizational psychology researchers can answer this call to do more research on weight at work (a) even while feeling uncomfortable with a topic that can feel personal, medicalized, and/or overly intertwined with other DEI-based topics; (b) by incorporating insightful research from outside disciplines that centers weight controllability and weight-based mistreatment deservedness; and, critically, (c) while approaching weight at work research with a respectfulness that conveys an understanding of the complexities intertwining weight, health, and personal agency. In culmination, this article offers to our field a flexible, living document entitled Best Practices for Weight-Based Research in Organizational Studies.

As discussed in the focal article, numerous research studies have supported the existence of automatic or implicit racial bias (Ruggs et al., 2016). In this commentary, we argue that examining implicit bias through the perspective of the motivation and opportunity as determinants (MODE) model (see Fazio & Olson, 2014, for a review) offers a framework for industrial–organizational (I-O) psychologists to design and implement strategies that reduce the number of violent interactions between police and communities. The MODE model has been applied to areas such as interpersonal relationships (McNulty, Olson, Meltzer, & Shaffer, 2013), effective treatment of mental disorders (Vasey, Harbaugh, Buffington, Jones, & Fazio, 2012), and crafting of media messages (Ewoldsen, Rhodes, & Fazio, 2015), as well as racial prejudice (Olson & Fazio, 2004). Below, we elaborate on how the I-O-related strategies and interventions described in the focal article can be captured by the components of the MODE model and highlight which interventions may be most efficacious in reducing discriminatory police officer behavior.

This response summarizes commentaries on the M. A. McDaniel, S. Kepes, and G. C. Banks (2011) article, which argued that the Uniform Guidelines on Employee Selection Procedures are a detriment to the field of personnel selection. Several themes were present in the commentaries. No compelling arguments were presented to dispute the assertion that mean racial differences in job-related attributes will be with us for a long time. However, compelling arguments were made that the disparate impact theory of discrimination is a more central issue for personnel selection than the Uniform Guidelines. Similarly, arguments were presented that the assessment of adverse impact is problematic and that expert witness testimony needs improvement. Areas in need of further investigation were also identified. Finally, the role of the Society of Industrial and Organizational Psychology (SIOP) in guiding regulatory, legislative, and court actions was considered.

Britt, Shen, Sinclair, Grossman, and Klieger (2016) recommend that our understanding of resilience would be advanced by making its temporal nature explicit. As a dynamic process, resilience is historical and temporal. The animating adverse event(s) and the resilient response(s) are dynamic and unfold over time; they often unfold as trajectories. This notion of trajectories implies a dynamic process, one progressing from one response or adaptation to another. These “temporal processes are a bit of a black box in I-O research” (Britt et al., p. 394). What would an extension of Britt et al.’s recommendation that researchers explicitly integrate time or temporality look like? Fully acknowledging the importance of time in this model may suggest worthwhile future research and potential interventions.