In 2008, we saw an emerging business need to accurately identify and develop individuals early in their careers who have the potential to be effective leaders in later organizational positions. We decided early on to take a comprehensive and systematic approach to the challenge in order to build effective solutions with real organizational impact that are sustainable over time. We knew that this was a complex problem and that there was little agreement in industrial–organizational (I-O) psychology on how to approach this critical business need and what approaches would be most effective. Since then, we moved through the following process stages to arrive at effective solutions that are now being used in numerous organizations.

We can make better decisions when we have a better understanding of the different sources of variance that impact job performance ratings. A failure to do so cannot only lead to inaccurate conclusions when interpreting job performance ratings, but often misguided efforts aimed at improving our ability to explain and predict them. In this paper, we outline six recommendations relating to the interpretation of predictive validity coefficients and efforts aimed at predicting job performance ratings. The first three focus on the need to evaluate the effectiveness of selection instruments and systems based only on the variance they can possibly account for. When doing so, we find that it is not only possible to account for the majority of the variance in job performance ratings that most select systems can possibly predict, but that we’ve been able to account for this variance for years. Our last three recommendations focus on the need to incorporate components related to additional sources of variance in our predictive models. We conclude with a discussion of their implications for both research and practice.

To critically evaluate the relevance of social exchange theory (SET) to the contemporary workplace, Chernyak-Hai and Rabenu (2018) point out a number of factors that reshape work relationships and suggest how to apply and extend social exchange theory to understand the new era work relationships. However, in their discussion, they focus mainly on reciprocal exchange (RE) in dyadic relationships. The discussion completely overlooks another important form of social exchange, namely, generalized exchange (GE), which is increasingly relevant to contemporary organizations exactly because of the changes indicated by Chernyak-Hai and Rabenu. In this commentary, we briefly review prior investigations into GE across various social science disciplines and then point out its increasing relevance to organizations. Finally, we will discuss implications for future research in the industrial and organizational (I-O) psychology literature.

In their target article, Jones, Arena, Nittrouer, Alonso, and Lindsey (2017) make a compelling argument that discrimination may be best conceptualized continuously rather than categorically with respect to dimensions of subtlety, formality, and intentionality. We agree that such a framework can help capture the multifaceted nature of discrimination. The authors note that subtle and interpersonal discrimination, in particular, are difficult to address through formal organizational policy. In the workplace, subtle and often interpersonal discrimination can be overlooked or attributed to misunderstanding and, thus, may go unpunished (Dovidio & Gaertner, 2004).

Guzzo, Fink, King, Tonidandel, and Landis's (2015) focal article was intended to be not a set of standards but instead “stepping stones” to “raise awareness and provide direction” (p. 492) to our field for working with big data. We believe that the work done by the authors successfully achieved those objectives, and we encourage the Society for Industrial and Organizational Psychology (SIOP) Executive Committee to keep advancing this work toward greater clarity and guidance. Having clear alignment among members of our field and guidelines for handling nebulous issues such as big data is an important aspect of the scientist–practitioner model.

In this response, we address commentator concerns about the generalizability of the active ingredients of psychotherapy to the science and practice of executive coaching. We discuss four ingredient that may make a difference: (a) client characteristics, (b) goals or success criteria, (c) role of the organization, and (d) contextual knowledge of the executive coach. We explore how each of these differences is likely to affect the weighting of the four active ingredients in the equation for predicting executive coaching outcomes. From this analysis, we re-affirm our hypotheses that the active ingredients are generalizable to coaching and hold promise for strengthening research and practice. We conclude by highlighting the efforts of several commentators to extend and deepen our hypotheses to other areas of leadership development.