What is the role of the situation in situational judgment tests (SJTs)? Lievens and Motowidlo (2016) assert that SJTs are somewhat of a misnomer because they do not actually measure how individuals would behave in a given situation per se. According to these researchers, SJTs assess general domain knowledge—whether potential employees recognize the “utility of expressing certain traits” (p. 4). As a result, SJTs map onto personality measures, which are a summary of behavior across time and situations. SJTs provide predictive validity in part because they tap into personality. However, rather than renaming SJTs, it is possible to reintroduce the concept of a situation to provide even greater predictive power. Thus, the goals of this commentary are to (a) clarify what constitutes a situation, (b) describe what SJTs might actually measure, and (c) set forth a path for a taxonomy of workplace situations.

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).

In this response to Guzzo, Fink, King, Tonidandel, and Landis (2015), we suggest industrial–organizational (I-O) psychologists join business analysts, data scientists, statisticians, mathematicians, and economists in creating the vanguard of expertise as we acclimate to the reality of analytics in the world of big data. We enthusiastically accept their invitation to share our perspective that extends the discussion in three key areas of the focal article—that is, big data sources, logistic and analytic challenges, and data privacy and informed consent on a global scale. In the subsequent sections, we share our thoughts on these critical elements for advancing I-O psychology's role in leveraging and adding value from big data.

The goal of our focal article was to provide a current perspective on personality testing and its use in organizational research and to elicit constructive discussion and suggestions for future research and practice. The present article caps off the discussion by integrating the main ideas presented in the commentaries within our original framework of questions and topics, with the immodest hope of advancing our understanding of personality and its measurement in the context of industrial–organizational psychology. In short, we recommend continuing to take advantage of the organizing framework of the Big Five while also pursuing more “bottom-up” approaches that examine facet-level relationships with multidimensional performance outcomes, in addition to developing process models that include more proximal motivational and situational variables. Work along these lines is valuable to both organizational science and practice.

My focal article generated a good deal of both heat and light embodied in 13 commentaries. In this response, I address both the heat and the light by clarifying some of my original comments, responding to criticisms in the commentaries, and using the commentaries to suggest a research agenda for studying the possible influence of stereotypes on real-world personnel decisions.

Big data and its applicability to talent management (TM) as defined by Rotolo et al. (2018) has already been recognized by many outside the field of I-O psychology. The market is beginning to include offerings from vendors for products that use some combination of big data techniques to process vast amounts of data or previously unanalyzable data, which they claim will improve components of TM for organizations. Unfortunately, as noted in the focal article, this “frontier” issue makes it difficult for organizations to separate the wheat from the chaff. Further, with few exceptions, I-O psychology is just beginning to inform organizations about whether and how big data can be used for the purposes of TM.