Social exchange theory (SET) is one of the most prominent and well-known theories in the organizational sciences literature (Cropanzano & Mitchell, 2005). In the focal article, Chernyak-Hai and Rabenu (2018) asserted that SET needs to be adjusted to account for changes in the nature of work, workers, and workplace characteristics. Specifically, they identify that workplaces are now more volatile, complex, uncertain, and ambiguous (Bennett & Lemoine, 2014), and work itself has become more flexible, virtual, and technology dependent; accordingly, today's workers are sourced from a global talent pool and more frequently occupy nontraditional employment arrangements (e.g., freelancing). It is undisputable that the workplace landscape is vastly different from that of the workplace landscape during which early forms of SET were originally articulated (Gouldner, 1960; Homans, 1958).

The focal article by Miner et al. (2018) convincingly argues that industrial and organizational (I-O) psychology professionals share a responsibility to adopt a social-structural perspective in understanding why women are underrepresented in science, technology, engineering, and mathematics (STEM) fields. This approach offers the best path forward for explaining the disparity and improving the attraction and retention of women in these fields (Miner et al., 2018). In conjunction with the approach described, a deliberate effort to cast a spotlight on women of color is necessary, as they are the most marginalized, yet are often excluded from conversations about gender equality.

Jones, Arena, Nittrouer, Alonso, and Lindsey (2017) make the case that discrimination is multifaceted and can be identified along several continua. They also emphasize the role that every individual may play in the propagation of discrimination. As such, they make note of several interventions from bystanders and allies to combat subtle discrimination. Although we agree that subtle discrimination causes harm and that interventions targeted at such discrimination are necessary, we propose some additional considerations for the science and practice of subtle discrimination reduction. Specifically, we discuss the limitations of focusing on subtle discrimination at the individual level, the ambiguous nature of intentionality, the view of subtle discrimination as a manifestation of a hostile environment that falls under the broader umbrella of negative interpersonal treatment, and the emphasis placed by Jones et al. on the potential for organizational level interventions by proposing several considerations for tackling a climate of negative interpersonal treatment.

Gloss, Carr, Reichman, Abdul-Nasiru, and Oestereich (2017) present a compelling argument (or rallying call) for there being a “moral imperative for I-O psychology to overrepresent people living in the deepest forms of poverty in both science and practice” (p. 330). We agree. Our research has been dominated by a POSH perspective, and it is incumbent upon us to ensure that our science benefits those who are most affected by poverty. We believe the interest in engaging in humanitarian work psychology is growing among industrial and organizational (I-O) psychologists, yet many of us may not feel prepared to conduct such research and/or we may feel that we lack the skills to do so. Further, as Gloss et al. (2017) note, to the extent that we are unprepared to engage in research that benefits those living in poverty, in particular, we run the added risk of harming the very populations we are wanting to help. As such, the interest is there, but we may be daunted by the method. We argue that in order to heed that rallying call, without harm, we need to develop our own capabilities to engage in this important work.

Academics sometimes forget that the purpose of a university is to educate: our students, our local communities, each other, and the world. Although each university is unique in its constituency, all share the charge to generate knowledge for the protection and benefit of the public good. The goal of an academic should be to beneficially impact society, broadly defined, with scholarly activity. As editor and columnist for The Industrial-Organizational Psychologist, one publication highlighted by the focal article, we applaud the efforts of Aguinis et al. (2017) to put forth alternative approaches to defining impact. Like them, we are concerned that many of the measures of “impact” we currently use do not capture this charge.

Professor Russell proposes a decision model of turnover in which the attractiveness of the current job is compared with that of an alternative. In turn, an employee chooses the option with the highest judged attractiveness. For example, “Employees make decisions to quit based on the relative attractiveness of their current job compared to alternative jobs or activities” (2013, p. 163). The attractiveness of one's current job and alternative are estimated by a regression equation assessing various attributes of the two targets (i.e., current job and alternative). Evoking March and Simon (1958) for a theoretical foundation, Professor Russell offers a subjectively “rational model” for the choice to stay or leave based on expectancy and expected value type decision models. In his empirical work, he uses a “policy capturing” simulation to identify how new hires personally weigh various job attributes when deciding whether they would quit hypothetical jobs varying in those attributes (Russell & Van Sell, 2012). When these weights are applied to employees' actual survey perceptions of the levels of job attributes, the resulting “simulated turnover intention” score predicts turnover better than a survey measure of quit intentions or job attributes alone. The inference is that turnover scholars can make substantial progress toward the prediction of actual turnover by using this model.

In their article, Ree, Carretta, and Teachout (2015) argued that a dominant general factor (DGF) is present in most, if not all, psychological measures (e.g., personality, leadership, attitudes, skills). A DGF, according to Ree et al., is identified by two characteristics. First, the DGF accounts for the largest amount of a measure's systematic variance, and second, it influences every subdimension within the construct domain. They indicate that researchers ignore DGFs and pay inappropriate amounts of attention to the specific dimensions (DSs) even though the DGF provides most of the predictive power and the DS adds little predictive power.

Cucina, Walmsley, Gast, Martin, and Curtin (2017) started an important dialogue about survey key driver analysis (SKDA). We believe that promoting more useful and valid ways to understand survey data is critical not only for the organizations we serve, but also for advancing the relevancy of our field. We use the terms useful and valid quite intentionally. “Useful” is driven by our practitioner side, but “valid” is driven by our science side. It is the science that often sets industrial and organizational (I-O) psychology apart from other fields. But in some ways, it also holds us back from being timely and relevant. Overall, we believe that the focal article erred too much on the science side.

Cucina, Walmsley, Gast, Martin, and Curtin (2017) raise an important issue in evaluating whether our current approaches for key driver analysis on employee opinion survey data are indeed best practices. As has been argued elsewhere (Putka & Oswald, 2016; Scherbaum, Putka, Naidoo, & Youssefnia, 2010), there is and can be misalignment between current and best practices. We agree with Cucina et al. that our field should engage in larger discussion of these issues. That discussion is critical, as industrial and organizational (I-O) psychologists are competing with those outside our field who have either little knowledge of best practices in data analysis (but who have been empowered by technology that automates the analysis) or little knowledge of psychology (but a great deal of knowledge in big data analytical techniques). I-O psychologists are in the vanguard of survey data analysis (Ducey et al., 2015), and we have a responsibility to maintain the standards of our field as well as to wield our influence to guide other practitioners outside our field on sound theoretical and analytical approaches.