Most organizations require some level of emphasis on sustained performance to survive. This is particularly true of publically traded organizations or those concerned with profit-and-loss accountability. In short, performance needs to be managed. As a result, performance management (PM) is a key practice in business and is often one of the primary areas of responsibility for industrial and organizational (I-O) practitioners in organizational settings. It is also a key lever for change in organization development (OD) interventions (e.g., Church, Rotolo, Shull, & Tuller, 2014), particularly when linked to specific behaviors that are being introduced and/or reinforced for the future success of the organization. Unfortunately, as Pulakos, Mueller Hanson, Arad, and Moye (2015) have noted, leaders and employees in most organizations fundamentally dislike PM. In fact, this dislike is so intense that it has resulted in professional conferences, workshops, and popular business books focused on the simplification, replacement, or even demise of the field (e.g., Culbert, 2010; Effron & Ort, 2010). There is even a PM book for dummies (Lloyd, 2009).

The scientist–practitioner model of training in industrial and organizational psychology provides the foundation for the education of industrial and organizational psychologists across the world. This approach is important because, as industrial and organizational psychologists, we are responsible for both the creation and discovery of knowledge and the use or application of that knowledge. In multiple articles recently published in this journal, Pulakos and her colleagues (Pulakos, Mueller Hanson, Arad, & Moye, 2015; Pulakos & O’Leary, 2011) have argued that performance management (PM), as applied and implemented in organizations, is broken. This is not a unique take on the state of PM in organizations, as others have been arguing for many years that PM is no longer working in organizations the way that we would like it to work (Banks & Murphy, 1985; Bretz, Milkovich, & Read, 1992). Further, for many years and in many Society for Industrial and Organizational Psychology conference panels and debates in the literature, we have been inundated with discussions and conversations around the science–practice gap and around the gap being especially evident in PM.

We appreciate the authors of the target article (Britt, Shen, Sinclair, Grossman, & Klieger, 2016) shining a spotlight on the important work being done in the study of resilience and providing a historical overview of how resilience has been conceptualized and assessed in the past. To add to this review, we would briefly like to comment on current developments in the field of resilience and related fields that may have broader implications for both the study of resilience and for many other topics in the industrial–organizational literature as well.

We wholeheartedly agree with Miner et al. (2018) that industrial and organizational (I-O) psychologists should take a lead in addressing gender inequity in science, technology, engineering, and mathematics (STEM) fields. The focal article is particularly timely in light of the recent controversial “Google memo” (Damore, 2017), in which a senior software engineer endorsed the same individual-level myths regarding the gender gap in STEM that were critiqued by Miner et al. (2018). However, we caution against painting all STEM fields with the same broad brush. We argue that it is critical for I-O psychologists to be aware of important differences between STEM subfields, as these distinctions suggest that a “one-size-fits-all” approach may be inadequate for addressing existing gender disparities in STEM. In order to be maximally effective, interventions may need to emphasize distinct issues and target different points in the career pipeline depending on the specific STEM subfield in question.

As Hyland, Lee, and Mills (2015) note, many conceptualizations of mindfulness include three characteristics. In particular, mindfulness is often defined as a state of consciousness in which an individual (a) focuses on the present moment, (b) attends to phenomena occurring both externally and internally, and (c) remains open to and accepting of observed stimuli—and thus avoids making judgments. Together, these characteristics grant insight into how mindfulness stands to improve performance in work settings. Just as directing attention to the work environment and the tasks and events associated with it can equip workers with key information for making decisions and help them identify and circumvent risks and threats, so too can attending to internal phenomena (e.g., emotions and intuitions) provide workers with useful inputs to the decisions they face (Dane, 2011). Furthermore, in refraining from making judgments and evaluations, workers are more likely to view unfolding events through a variety of lenses and are less likely to view their thoughts, feelings, and reactions as manifestations or indictments of who they are (Glomb, Duffy, Bono, & Yang, 2011). In line with these observations, researchers have hypothesized and provided evidence for positive relationships between mindfulness and the well-being and performance of workers (e.g., Allen & Kiburz, 2012; Dane & Brummel, 2014; Hülsheger, Alberts, Feinholdt, & Lang, 2013).

In their focal article, Chamorro-Premuzic, Winsborough, Sherman, and Hogan (2016) provide an overview of a number of new technologies with potentially significant implications for talent management related practices of industrial–organizational (I-O) psychology (both challenges and opportunities) that they label as “new talent signals.” These signals, they argue, are part of a revolution, due to increasing levels of social activity online as well as data-collection and mining techniques that have overtaken the conventional practice of talent identification in organizations. Their position is that these trends are leaving I-O psychologists in the dust in terms of our existing traditional theory, research, and methodologies. Their optimistic tone, however, also seems to suggest that while these approaches “have not yet demonstrated validity comparable with old school methods, they tend to disregard theory, and they pay little attention to the constructs being assessed (Chamorro-Premuzic et al., p. 634)” they may, in fact, have some legitimate basis in the identification of talent. Their point, of course, is that momentum in this area has surpassed human resources (HR), let alone I-O psychology, so that any concerns one might have about these trends are essentially “irrelevant.” This reminds us of a reference to lemmings going off a cliff together.

In this article, I summarize and respond to key comments and criticisms offered in commentaries on Kraiger (2008). At issue is whether the proposed third-generation instructional model is fundamentally sound, represents a new or better approach, overemphasizes the importance of learner–learner interaction, and underemphasizes the role of the instructor. Responses to these concerns are offered, along with clarification that the major contribution of the third-generation model is at the point of training design.

Ethical decision making has long been recognized as critical for industrial-organizational (I-O) psychologists in the variety of roles they fill in education, research, and practice. Decisions with ethical implications are not always readily apparent and often require consideration of competing concerns. The American Psychological Association (APA) Ethical Principles of Psychologists and Code of Conduct are the principles and standards to which all Society for Industrial and Organizational Psychology (SIOP) members are held accountable, and these principles serve to aid in decision making. To this end, the primary focus of this article is the presentation and application of an integrative ethical decision-making framework rooted in and inspired by empirical, philosophical, and practical considerations of professional ethics. The purpose of this framework is to provide a generalizable model that can be used to identify, evaluate, resolve, and engage in discourse about topics involving ethical issues. To demonstrate the efficacy of this general framework to contexts germane to I-O psychologists, we subsequently present and apply this framework to five scenarios, each involving an ethical situation relevant to academia, practice, or graduate education in I-O psychology. With this article, we hope to stimulate the refinement of this ethical decision-making model, illustrate its application in our profession, and, most importantly, advance conversations about ethical decision making in I-O psychology.