The U.S. labor market continues to grapple with a “skills gap” (Marshall & Craig, 2019): a disconnect between the skills employers need and the number of job-seekers with those skills. Compounded by historically low unemployment rates, this gap is leaving employers with unfilled jobs and narrow talent pipelines. Concurrently, there are lingering concerns regarding underrepresentation of women and minorities in certain sectors of the labor market—particularly occupations in science, technology, engineering, and mathematics (STEM). This article examines how the traditional interest-only career guidance tools used in education significantly influence the gender-based skills gaps that persist in high-demand careers and introduces YouScience, a company that is helping ameliorate the skills gap by combining measures of aptitudes and interests in a new career discovery platform. We close by presenting action steps for students, parents, educators, and counselors, as well as positing possible effects of COVID-19 on career exploration and counseling.

The focal article (Grand et al., 2018) addresses one of the most important issues across virtually all areas of science (Goldstein, 2010): the trustworthiness and credibility of a scientific discipline. Once these attributes are lost, it is difficult to regain them within a reasonable time frame, if ever. In contrast to previous articles on this topic (e.g., Kepes & McDaniel, 2013), the authors of the focal article provide a detailed review of the stakeholders surrounding industrial and organizational (I-O) psychology, including their potential effect on the robustness and trustworthiness of our scientific discipline. In essence, the focal article describes I-O psychology's ecosystem responsible for fostering robust and credible science. The authors should be commended for their comprehensive undertaking, and we have no substantive disagreements. However, implicitly, as with most articles on this vital topic, the focal article tends to take a bottom-up approach to decision making and change. The bottom-up approach is an emergent process where the individuals involved in the day-to-day activities are primarily responsible for the decision-making process and resulting change (Kindler, 1979). Thus, changes resulting from this process are incremental and typically involve making minor adjustments to existing processes (Bartunek & Moch, 1987).

In their focal article, Aguinis and Glavas (2013) emphasize that despite a growing body of research, the conditions facilitating positive effects of corporate social responsibility (CSR) remain unclear. In regard to this issue, empirical studies have thus far produced inconsistent and equivocal results (for an overview, see Margolis & Walsh, 2003). Aguinis and Glavas introduce the difference between embedded and peripheral CSR as a key differentiation to explain why and when CSR is more likely to lead to positive outcomes. Specifically, they argue that CSR is more likely to lead to positive outcomes if it is embedded and consequently related to an organization's core competencies and integrated within a firm's strategy, routines, and operations. The idea of embedded CSR being more likely to lead to positive outcomes is compatible with, for instance, the notion of “shared value” (Porter & Kramer, 2011).

The “big data” movement is forcing many fields to establish best practices for the collection, analysis, and application of big data, and the field of industrial–organizational (I-O) psychology is not exempt from this disruptive influence. Over the last several years, I-O scientists and practitioners have grappled with questions related to the definition, application, and interpretation of big data (e.g., Doverspike, 2013; Maurath, 2014; Morrison & Abraham, 2015; Poeppelman, Blacksmith, & Yang, 2013). The focal article by Guzzo, Fink, King, Tonidandel, and Landis (2015) continues this discussion and represents one of the first attempts to establish a formal set of recommendations for working with big data in ways that are consistent with I-O psychology's professional guidelines and ethics requirements.

Various solutions have been proposed to “fix” performance management (PM) over the last several decades. Pulakos, Mueller Hanson, Arad, and Moye (2015) have presented a holistic approach to improving PM in organizations. Although this approach addresses several key elements related to the social context of PM, namely the buy-in of organizational stakeholders, timely and regular feedback, and future-directed feedback, we believe that several robust findings from the PM research literature could further improve this process. Are Pulakos et al. looking at the forest but missing the trees? In the following commentary, we offer several reasons that performance judgments and perhaps even informal ratings are still operating and occurring in the proposed holistic system. Therefore, advancements in other areas of PM research may offer additional ways to fix PM.

Aguinis and Glavas (2013) ask, “When and why does CSR lead to positive outcomes for employees, organizations, and society?” Although the concept of corporate social responsibility (CSR) is elusive and comprises a wide range of definitions, there is convergence on the voluntary nature of these actions. But who implements these voluntary initiatives? CSR will take effect, as Aguinis and Glavas point out, at micro levels, through the actions of lower-level units and employees. Thus, there is a need to specify mechanisms by which macro-level CSR policies result in microlevel outcomes.

In their focal article, Tett, Hundley, and Christiansen (2017) stated in multiple places that if there are good reasons to expect moderating effect(s), the application of an overall validity generalization (VG) analysis (meta-analysis) is “moot,” “irrelevant,” “minimally useful,” and “a misrepresentation of the data.” They used multiple examples and, in particular, a hypothetical example about the relationship between agreeableness and job performance. Four noteworthy problems with the above statements, other similar statements elsewhere in Tett et al.’s article, and their underlying assumptions are discussed below along with alternative perspectives.