Adler et al. (2016) provide a discussion of the pros and cons surrounding the issue of “Getting Rid of Performance Ratings.” Yet neither the pro nor the con side of the debate appears to fully consider the central role of performance ratings outside the realm of performance management.

Being familiar with their earlier work investigating the factor structures of the Armed Services Vocational Aptitude Battery and the Air Force Officer Qualifying Test, we read with interest Ree, Carretta, and Teachout's (2015) proposal to extend the idea of a dominant general factor (DGF) beyond the realm of cognitive abilities to other areas of research and practice in industrial–organizational (I-O) psychology. We found their ideas intriguing and their arguments compelling, but we stumbled on a reference to an article of one of the present authors (Lance, Teachout, & Donnelly, 1992) and Ree et al.’s claim that Lance et al. (1992) had found a DGF that accounted for 59% of the variance in job performance ratings in a military job. They did not. Rather, Lance et al. reported a hierarchical confirmatory factor analysis (CFA) model that explained parsimoniously the correlations among 15 job performance first-order factors in terms of four Job Proficiency and four other Measurement Source second-order factors. We reasoned that Ree et al. must have conducted some secondary analysis on the results presented by Lance et al., and indeed we replicated their claim by finding that the first unrotated principal component accounted for 59% of the variance in correlations among the four Proficiency second-order factors reported in Lance et al.’s Table 6.

Central to the argument of Ryan and Ford (2010) is that the identity of “industrial-organizational (I-O) psychologist” is at a number of tipping points that could lead to major changes in the field. The two tipping points that we will focus on are (a) the increasingly blurry distinctions between current I-O psychologists and HR/OB professionals and (b) the growing number of new I-O psychologists who do not have a deep-structure I-O identity because they are ultimately employed in fields outside of I-O psychology.

After reviewing the article by Johnson et al. (2010), I began to ask myself and other industrial and organizational (I-O) psychologists what interest groups might be impacted by a Society for Industrial and Organizational Psychology (SIOP)-sponsored synthetic validity database and its resulting mechanical behavior and performance predictions. Who are the various stakeholders and what might be the positive or negative outcomes caused by this “disruptive technology” that could cause “creative destruction” in the I-O psychology profession? Among the consumers and producers of performance prediction, who might gain and who might be creatively destroyed? From these questions and the subsequent conversations, I identified the following categories and possibilities, but I am sure there are others that I have not anticipated.

The Society for Industrial and Organizational Psychology (SIOP) and its Foundation have increasingly focused on linking science to practice, and to that end have partnered with Society of Human Resource Management (SHRM) and its Foundation to create the Human Resource Management (HRM) Impact Award for firms demonstrating great practices in evidence-based human capital and management practices. Jack in the Box, the 2016 winner of this award, was so honored because of its integration of surveys and analytic models with leadership practices to understand, predict, and manage the right people drivers of important business outcomes. Instead of a research paper, we have chosen to share their story through conversational interview questions with the various stakeholders who contributed to the journey leading to the award and, in so doing, provide key lessons for others wishing to increase the impact of human capital in their organizations.

Gloss, Carr, Reichman, Abdul-Nasiru, and Oestereich (2017) neglected the “how-to” component of their proposal for increasing humanitarian efforts in the field of industrial and organizational (I-O) psychology. Implementing change at an earlier point in the education process, through social justice awareness and applied service learning, is likely to lead to a more robust and fruitful shift away from a POSH bias compared to an attempt at persuading those currently in the field to adopt this change after their training (although I would never argue that the latter is utterly irrelevant). I hold an unwavering conviction that a natural route toward change would be to ensure that I-O psychology educators are explicitly focusing on the humanitarian workplace movement within our courses and that I-O professionals in organizations aid this cause by encouraging a humanitarian perspective as they train their newly hired, fresh-faced I-O graduates. The purposeful threading of humanitarian goals into I-O psychology education, in large part through service learning or civic education, is the key to our effectiveness. Without such a focus in the beginning stratum of I-O psychologists’ training, our other well-intentioned attempts at shifting the field's focus toward humanitarian concerns may be in vain. Below, I offer suggestions to reach this goal.

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.