Guzzo, Fink, King, Tonidandel, and Landis (2015) provide a clear overview of the implications of conducting research using big data. One element we believe was overlooked, however, was an individual-level perspective on big data; that is, what impact does this sort of data collection have on the individuals being studied? As psychologists, the ethics and impact of big data collection from workers should be at the forefront of our minds. In this reply, we use years of research on electronic monitoring and tracking to provide evidence that an individual-level perspective is an essential part of the discussion surrounding industrial–organizational psychology and big data. Specifically, we examine electronic performance monitoring (EPM) literature to identify how the widespread, pervasive collection of employee data affects employees’ attitudes and behaviors.

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 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.

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.

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.

Despite becoming increasingly represented in academic departments, women scholars face a critical lack of support as they navigate demands pertaining to pregnancy, motherhood, and child caregiving. In addition, cultural norms surrounding how faculty and academic leaders discuss and talk about tenure, promotion, and career success have created pressure for women who wish to grow their family and care for their children, leading to questions about whether it is possible for these women to have a family and an academic career. This paper is a call to action for academia to build structures that support professors who are women as they navigate the complexities of pregnancy, the postpartum period, and the caregiving demands of their children. We specifically call on those of us in I-O psychology, management, and related departments to lead the way. In making this call, we first present the realistic, moral, and financial cases for why this issue needs to be at the forefront of discussions surrounding success in the academy. We then discuss how, in the U.S. and elsewhere, an absence of policies supporting women places two groups of academics—department heads (as the leaders of departments who have discretion outside of formal policies to make work better for women) and other faculty members (as potential allies both in the department and within our professional organizations)—in a critical position to enact support and change. We conclude with our boldest call—to make a cultural shift that shatters the assumption that having a family is not compatible with academic success. Combined, we seek to launch a discussion that leads directly to necessary and overdue changes in how women scholars are supported in academia.

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.