In the focal article, Britt, Shen, Sinclair, Grossman, and Klieger (2016) are rightfully concerned that the topic of resilience may become a “quicksand” term that is used by different audiences in different manners. However, we are more optimistic than the authors of the focal article, as several researchers at the team level of analysis have outlined frameworks that have attempted to tease apart team resilience from related constructs such as adaptation. Likewise, such work has also provided a deeper understanding of the factors that serve as antecedents to team resilience and adaptation, as well as how both constructs can shape subsequent team outcomes. Accordingly, the “sand” is starting to congeal at the team level of analysis, and we bring in these insights to extend the conversation about resilience.

Alderfer's (2013) piece on the Sandusky/Penn State tragedy reminds us that leader-centric analyses, the norm in leadership studies, often miss the mark. Alderfer joins a growing list of writers who increasingly recognize that leadership consists of three key elements in a triangle: leaders, followers, and environments. The Penn State scandal highlights how a conducive environment, typified by centralized power and an absence of checks and balances, coupled with flawed leaders and the actual assistance or quiet submission of certain followers, can lead to disastrous outcomes. As Alderfer observes, leadership is a social, or group, process. Leadership success or failure depends on group results, and group results involve more than just leaders and their characteristics and actions. Yet, over three-quarters of articles in scholarly journals consistently overlook the role of organizational environments and followers (Porter & McLaughlin, 2006), focusing instead on leader traits and behaviors (Kaiser, Hogan, & Craig, 2008; Thoroughgood, Padilla, Hunter, & Tate, 2012).

The commentaries to our focal article were both interesting and stimulating. As we generally agreed with the major points raised in the commentaries, we use this response to frame an on-going tension point or challenge regarding team definitions, highlight a few unifying themes that weave through our initial article and the commentaries, and discuss the transition from research to informed practice. The past few decades have been exciting times for team researchers and practitioners, and the time is ripe for new energies and approaches.

The current research literature on voluntary employee turnover exhibits at least four shortcomings: low predictive validity, excessive exuberance with predictors, low rigor, and little relevance. Existing theories of voluntary employee turnover research are under specified, as none contains the full range of variables originally hypothesized by March and Simon as contributing to individual employees' decisions to quit their jobs. March and Simon (1958) contributed to the current state of affairs by making at least one assumption that subsequent results do not support. Subsequent efforts to expand theory and to detect and integrate new explanatory constructs have led to a much deeper understanding of the same small portion of turnover variance explained in employee decisions to quit. Deficiencies in current approaches and what a nondeficient model of voluntary employee turnover might look like are described. Directions for future voluntary turnover research are identified, as are past trends that need to be discontinued.

Landers and Behrend (2015) present yet another attempt to limit reviewer and editor reliance on surface characteristics when evaluating the generalizability of study results (see also Campbell, 1986; Dipboye & Flanagan, 1979; Greenberg, 1987; Highhouse, 2009; Highhouse & Gillespie, 2009). Most of the earlier treatments of sample generalizability, however, have focused on the use of college students in (mostly) laboratory studies. Many industrial–organizational (I-O) scholars have experienced the hostility with which studies using students as participants receives. For instance, Jen Gillespie and I observed, “Reviewers and editors commonly assert that students should not be used to study workplace phenomena as though such a declaration requires no further explanation” (Highhouse & Gillespie, 2009, p. 247). The difference this time, however, is that Landers and Behrend (2015) are reacting to dismissals of research using Mechanical Turk (MTurk) workers to make inferences about behavior in organizations. Landers and Behrend (2015) make the important point that any research population is likely to be atypical on some dimensions and that all samples are samples of convenience (see also Oakes, 1972). We agree. Furthermore, we make two observations about MTurk: (a) We believe that it should be met with less resistance than student samples have historically faced, and (b) we suggest that it provides a unique opportunity to bring back randomized experimentation in I-O psychology.

The commentaries on my target article expand on it in many useful and enlightening ways, and some provide a glimpse at important new research. The commentaries also point to a few issues raised in the original article that require clarification or elaboration. In this response, I begin by recalling the “big idea” that initially led to interest in emotional intelligence (EI) as a concept, which is that success in life and work depends on more than just the basic cognitive abilities measured by IQ tests. I then clarify what I mean by emotional and social competence (ESC): It is not a single, unitary psychological construct but rather a very broad label for a large set of constructs. After considering whether we really need the ESC concept, I discuss whether the single, comprehensive definition of EI that I proposed in the target article is the best one in light of alternatives suggested in some of the commentaries. Next, I return to the issue of measurement and note new ideas and suggestions that emerge in the commentaries. I conclude by considering the question of how much EI or ESC adds conceptually or predictively to IQ or personality.

The basic income (BI) involves regular and unconditional cash payments to all members of a political community, without the requirement or expectation to work in return. Whereas the BI is increasingly discussed by political parties, organizational practitioners, and in other academic disciplines, the field of industrial, work, and organizational (IWO) psychology has so far remained silent on the concept. In this article, we first explain why there is a growing interest in the BI and outline potential reasons why the BI, despite its topical relevance, has not been discussed by IWO psychologists. Next, to initiate the needed discussion on the BI, we outline the most important background information on the concept, including its definition, history, financial aspects, main criticisms, and potential advantages. We further provide first answers to common questions about the BI from an IWO psychology perspective, such as “(Why) would people still work if they received a BI?” We conclude with a discussion of potential positive and negative consequences of the BI as well implications for future theory development, empirical research, and practical applications.

Over the years, employee opinion surveys have evolved in their use and how they are conducted. A major advancement has been the use of linkage analyses, whereby employee attitudes at a unit level are statistically related to other important organizational outcomes. A more recent development has been linkage analyses at the individual level and over time. In order to carry out these types of analyses, “identified surveys” must be used—surveys that retain identifying information on each survey respondent in order to link with other individual-level variables over time. The purpose of this article is to open up a discussion on identified surveys, describe under what circumstances they may be uniquely beneficial, and highlight potential concerns with them. We close with proposed guidelines for professional practice and recommend that our profession have a point of view on identified surveys for ourselves and to advise others.

Hyland, Lee, and Mills (2015) specified the two most popular scales for mindfulness: the Freiburg Mindfulness Inventory (FMI; 30 items, Buchheld, Grossman, & Walach, 2001; 14 items, Walach, Buchheld, Buttenmüller, Kleinknecht, & Schmidt, 2006) and the Mindfulness Attention and Awareness Scale (MAAS; Brown & Ryan, 2003). However, the popularity of these measures does not necessarily mean that they are of high quality. Especially considering the complex epistemology and ontology of the mindfulness construct, we should apply mindfulness assessments with caution. More specially, according to item development theorists in the industrial and organizational (I-O) area (see Hinkin, 1998; Hinkin & Tracey, 1999; Kerlinger & Lee, 2000; Schriesheim, Powers, Scandura, Gardiner, & Lankau, 1993), scholars must select measures that have (a) a clear operational definition; (b) alignment between definition and measure (content validity); (c) high reliability and (d) high construct validity; and (e) high criterion-related validity. However, it is not clear which of the available mindfulness assessments satisfies these criteria and to what extent. In this commentary, we assess currently used measures based on these criteria and provide directions for future research.

Attrition modeling is a direct application of extant turnover research that can favorably impact workforce planning and action planning. However, while academic research enables practitioners insights into understanding turnover phenomena, there is no single document that comprehensively translates this work to give guidance as to the many practical decisions that must be made when modeling turnover, as well as how to apply psychological research to messier operational data. This focal article introduces and provides guidance on attrition modeling by outlining early considerations when planning a study, describing how to mesh theory with operational considerations when identifying turnover predictors within organizational settings, highlighting analytical strategies to model turnover, and considering how to appropriately share results. Collectively, this article serves as a guide to conducting attrition modeling within organizations and offers suggestions for future research to inform best practices.

The commentaries on our focal article agreed with its main premise that work–family research should follow new strategies to improve its practical impact, and made suggestions clustering into three main themes. The first theme built on our suggestion to improve the research focus, terminology, and framing of work-family research. These essays offered additional ideas such as decoupling work-family from work-life research, and examining contextual factors more deeply. The second theme focused on how to better apply the findings from work family research. These commentaries provided social change approaches for making work-family issues more central to key stakeholders and to organizations. The third theme focused on broadening our scope to the societal level. These editorials advocated tactics supporting the development of basic rights of work–life balance within and across nations.

Although we share Bergman and Jean's (2016) concerns about the representativeness of samples in the organizational sciences, we are mindful of the ever changing nature of the job market. New jobs are created from technological innovation while others become obsolete and disappear or are functionally transformed. These shifts in employment patterns produce both opportunities and challenges for organizational researchers addressing the problem of the representativeness in our working population samples. On one hand, it is understood that whatever we do, we will always be playing catch-up with the market. On the other hand, it is possible that we can leverage new technologies in order to react to such changes more quickly. As an example, in Bergman and Jean's commentary, they suggested making use of crowdsourcing websites or Internet panels in order to gain access to undersampled populations. Although we agree there is an opportunity to conduct much research of interest to organizational scholars in these settings, we also would point out that these types of samples come with their own sampling challenges. To illustrate these challenges, we examine sampling issues for Amazon's Mechanical Turk (MTurk), which is currently the most used portal for psychologists and organizational scholars collecting human subjects data online. Specifically, we examine whether MTurk workers are “workers” as defined by Bergman and Jean, whether MTurk samples are WEIRD (Western, educated, industrialized, rich, and democratic; Henrich, Heine, & Norenzayan, 2010), and how researchers may creatively utilize the sample characteristics.

Although synthetic validation has long been suggested as a practical and defensible approach to establishing validity evidence, synthetic validation techniques are infrequently used and not well understood by the practitioners and researchers they could most benefit. Therefore, we describe the assumptions, origins, and methods for establishing validity evidence of the two primary types of synthetic validation techniques: (a) job component validity and (b) job requirements matrix. We then present the case for synthetic validation as the best approach for many situations and address the potential limitations of synthetic validation. We conclude by proposing the development of a comprehensive database to build prediction equations for use in synthetic validation of jobs across the U.S. economy and reviewing potential obstacles to the creation of such a database. We maintain that synthetic validation is a practically useful methodology that has great potential to advance the science and practice of industrial and organizational psychology.