The lengthy and complex focal article by Tett, Hundley, and Christiansen (2017) is based on a fundamental misunderstanding of the nature of validity generalization (VG): It is based on the assumption that what is generalized in VG is the estimated value of mean rho ($\bar{\rho}$). This erroneous assumption is stated repeatedly throughout the article. A conclusion of validity generalization does not imply that $\bar{\rho}$ is identical across all situations. If VG is present, most, if not all, validities in the validity distribution are positive and useful even if there is some variation in that distribution. What is generalized is the entire distribution of rho ($\bar{\rho}$), not just the estimated $\bar{\rho}$ or any other specific value of validity included in the distribution. This distribution is described by its mean ($\bar{\rho}$) and standard deviation (SDρ). A helpful concept based on these parameters (assuming ρ is normally distributed) is the credibility interval, which reflects the range where most of the values of ρ can be found. The lower end of the 80% credibility interval (the 90% credibility value, CV = $\bar{\rho}$ – 1.28 × SDρ) is used to facilitate understanding of this distribution by indicating the statistical “worst case” for validity, for practitioners using VG. Validity has an estimated 90% chance of lying above this value. This concept has long been recognized in the literature (see Hunter & Hunter, 1984, for an example; see also Schmidt, Law, Hunter, Rothstein, Pearlman, & McDaniel, 1993, and hundreds of VG articles that have appeared in the literature over the past 40 years since the invention of psychometric meta-analysis as a means of examining VG [Schmidt & Hunter, 1977]). The $\bar{\rho}$ is the value in the distribution with the highest likelihood of occurring (although often by only a small amount), but it is the whole distribution that is generalized. Tett et al. (2017) state that some meta-analysis articles claim that they are generalizing only $\bar{\rho}$. If true, this is inappropriate. Because $\bar{\rho}$ has the highest likelihood in the ρ distribution, discussion often focuses on that value as a matter of convenience, but $\bar{\rho}$ is not what is generalized in VG. What is generalized is the conclusion that there is validity throughout the credibility interval. The false assumption that it is $\bar{\rho}$ and not the ρ distribution as a whole that is generalized in VG is the basis for the Tett et al. article and is its Achilles heel. In this commentary, we examine the target article's basic arguments and point out errors and omissions that led Tett et al. to falsely conclude that VG is a “myth.”

Morelli, Potosky, Arthur, and Tippins (2017) articulate a strong need for industrial and organizational (I-O) psychologists to develop a more theory-based understanding of the role of technology in employee selection and assessment. We agree with their concerns but argue that this issue should include examination of how technology impacts training also. Researchers have noted that training is increasingly important for firms, and technology-enhanced training can improve learning and transfer (Ford & Meyer, 2013). However, the arguments that the authors make about the need for a theory-driven approach for examining the impact of technology on selection applies to training outcomes as well. Although considerable evidence exists that workplace training is effective and that technology can impact the success of training, there has been less theory-driven research exploring how technology can enhance or detract from training success. Researchers have already identified several variables related to technology that promote learning, but one variable that remains consistently unexplored in the organizational literature is immersion. This research is particularly important given how increasingly accessible immersive technology, such as virtual reality (VR), is becoming. Thus, we argue that as virtual training environments become more widely available, the variable of “immersion” in training environments is a particularly important one that warrants additional research.

Although we support the primary embedded–peripheral categorization proposed by the focal article authors, (Aguinis & Glavas, 2013), in this commentary, we explore the implications of privileging embedded over peripheral corporate social responsibility (CSR) and identify benefits of the latter that we believe have been overlooked or underplayed. The benefits of peripheral CSR that we outline below can, in fact, contribute substantially to overall CSR efforts over and above those of embedded CSR.

To understand gender inequality in STEM, Miner et al. (2018) illustrate how an individual lens and a social-structural lens provide complementary perspectives. They indicate that gender inequality in STEM should not be simply understood from an individual lens concerning individual choices and responsibilities but also a social-structural lens concerning societal structures, processes, and meanings associated with gender. In this commentary, we would like to bring a cultural perspective to the consideration of the STEM field. Specifically, we focus on gender inequity in STEM in Japan and elaborate how Japanese culture, which emphasizes masculinity, collectivism, and a tight culture, imposes a stronger social-structural influence on gender inequality in STEM and at the same time strengthens the use of the individual lens to explain the phenomena, making the issue of gender inequality more prominent.

This response is being written primarily from the perspective of the Boston College Center for Corporate Citizenship and insights derived from extensive interactions with its members. This enables a different perspective on the focal article by Aguinis and Glavas (2013) than one primarily based on macro/micro distinctions in academic scholarship.

I agree with and expand on Pratt and Bonaccio's (2016) view that in order to facilitate the use of qualitative research in our field, we need more guidance for industrial–organizational (I-O) researchers in terms of training, best practice articles, and criteria for evaluating research quality. Fortunately, the interest in and openness to qualitative research is slowly but steadily growing in our field, something that is reflected in small but increasing numbers of publications and best practice articles (Bluhm, Harman, Lee, & Mitchell, 2011; Smith, Madden, & Ashmos Plowman, 2015). To give an example, the Journal of Applied Psychology (JAP), a journal not well known for publishing qualitative research, has recently published a qualitative article written by my colleagues and me (Wilhelmy, Kleinmann, König, Melchers, & Truxillo, 2016).

While nonspeech communication and “metaphorical” silence (in opposition to voice) have benefited from a considerable academic attention, less is known about quiet environments and the intentional practice of silence. We theorize these silences as potential catalysts of internal and collective reflection. Such silences can strongly impact individual and organizational processes and outcomes, notably in the workplace. The meaning, valence, and effects of these silences are highly context- and perspective-dependent. By characterizing and studying these silences and their effects, we show how they are functional or dysfunctional to individuals or organizations. These silences can notably serve as emotion regulators and generate an environment favorable to individual and collective decision making. Examining what is lost by individuals and organizations due to a lack of these silence and what can be gained with a better harnessing of their power is promising.