Neubert, Mainert, Kretzschmar, and Greiff (2015) rightly argue that today's business world requires employees to frequently engage in nonroutine, creative, and interactive tasks. The authors go further to describe two potentially important skills—complex problem solving and collaborative problem solving—which they believe can address gaps in our current understanding of employee skill assessment. I contend however that the authors might be reinventing the wheel with this framework, given that the already popular practice of competency modeling satisfies the very deficiencies that the authors argue exist. To expand on this argument, I will first provide a brief history and discussion of what competency modeling is, followed by an explanation of several key benefits of this approach in terms of addressing the authors’ concerns. Then, on the basis of my applied experience as an external consultant, I will discuss how I might use competency modeling to address one of the authors’ own example scenarios, which should help identify ways in which competency modeling subsumes Neubert and colleagues’ approach.

This article responds to and extends the commentaries offered in response to our focal article on learning agility. After summarizing the basic themes in the commentaries, we use this response to clarify points that were unclear in our original article and push back on certain points raised in a few of the responses. In particular, we reframe the rigor–relevance debate from an “either–or” to a “both–and” discussion, clarify the relationship between learning agility and ability to learn, explain how learning agility in organizations moves beyond cognition, and describe how exchanges such as the one we have collectively engaged in here are central to progressing the scientific study on learning agility and its effective use in practice.

Colquitt, Murphy, and Ollander-Krane (Adler et al., 2016) argue that performance ratings are problematic in part because of the problems associated with feedback: Ratees dislike and dismiss performance feedback, raters are reluctant to provide tough feedback, and organizations do not enact research findings about improving feedback processes (Adler et al.). Discarding performance ratings on these grounds is effectively “throwing out the baby with the bath water,” given that we know quite a lot about how to improve the delivery and receptivity of feedback. Our commentary is intended to briefly illustrate ways to leverage research on feedback receptivity to improve performance management systems. Specifically, we focus on (a) cultivating supportive feedback environments, (b) integrating employee coaching into performance management systems, and (c) attending to the characteristics of feedback recipients to understand how they process feedback.

The paper proposes that industrial-organizational (I-O) psychology will benefit greatly from expanding our research focus from predominantly individual differences to studying organizational differences. We argue here that an increased organizational frame of reference on variables of interest to I-O psychology (e.g., selection, job design, performance management (PM), work motivation) is important because it will enhance our understanding of organizational behavior and make I-O research more effective in practice. After noting some organizational-level research already being done, several examples are provided for how an organizational mindset and methods can provide new insights into traditional areas of I-O effort. Also discussed is how methodological issues that may have constrained the study of organizational differences in the past and the potential new issues such research may yield can be addressed. We conclude that the future maintenance and enhancement of the I-O psychology brand as a science–practice profession requires enhanced attention to the organization level of analysis as our frame of reference for research.

There doesn't seem to be much disagreement that experience should be at the heart of leadership development. The energy in the commentaries was around building on the existing foundation of knowledge about experience-based leadership development, bolstering certain areas to make it more complete and usable. I examine more closely several of the issues raised in the commentaries: the role of training and education in leadership development, increasing the probability that the desired learning from experience actually happens, and the transfer of learning.

Science is a complex task. It involves the creation and dissemination of knowledge. The creation of knowledge requires identifying and abstracting patterns (i.e., identifying phenomena and theorizing about the processes that bring it about), as well as systematically observing to better see and quantify the patterns (e.g., effect size estimating) or assess the validity of the abstractions used to explain the patterns (i.e., theory testing). To help (a) hone in on what observations would be useful and (b) communicate what the patterns mean, we are encouraged to develop and report hypotheses. That is, strategically, hypotheses facilitate the planning of data collection by helping the researcher understand what patterns need to be observed to assess the merit of an explanation. Meanwhile, tactically, hypotheses help focus the audience on the crucial patterns needed to answer a question or test a theory. When the strategic hypotheses are not supported, it raises a question regarding what to do tactically. Depending on the result (i.e., different direction; null), one might construct a hypothesis to facilitate dissemination without reporting this post hoc construction or remove mention of a hypothesis altogether. This practice is called HARKing (i.e., hypothesizing after results are known). HARKing has been so disparaged as to be considered a “detrimental research practice” (Grand et al., 2018, p. 6). As such, the Society for Industrial and Organizational Psychology's (SIOP) Robust and Reliability Science task force appears to be recommending that HARKing not be taught by educators, encouraged by reviewers or editors, or practiced by authors. I do not agree with those recommendations, and I elaborate on my position below.

Costanza and Finkelstein (2015) have justly argued that cross-sectional operationalizations of generational groups represent a confound that constrains the ability to unequivocally separate the effects of age and period. A related statistical argument against this practice bears consideration as well. Namely, cross-sectional operationalizations of generations have the potential to unduly inflate type two-error rates when compared with the analysis of simple age effects. This is a problem because true age effects can be erroneously ignored in studies where age is artificially split into assumed generational groups. Indeed, the argument against artificially bifurcating continuous data is not new (e.g., Cohen, 1983), however past attempts to make inferences about generational effects in cross-sectional designs present an opportunity to investigate the particularly insidious nature of this practice and its implications. To demonstrate the problem at hand, let us consider a brief empirical example by virtue of a simulation study.

Mindfulness—present moment attention and awareness (Brown & Ryan, 2003)—is commonly proposed as a productive state of consciousness in the workplace. Unfortunately, being mindful at every moment of the workday is fairly uncommon. Research suggests that people engage in mind wandering—a lack of attention to and awareness of the present (Smallwood & Schooler, 2006)—for the majority of their day (in every task except making love; Killingsworth & Gilbert, 2010). Further, there is another state of consciousness called flow—an intense sense of concentration and control over activities—that has also been linked to workplace performance (Nakamura & Csikszentmihalyi, 2002). Interestingly, whereas mindfulness facilitates higher performance by being aware of external stimuli, flow enables higher performance by doing the opposite—blocking out external stimuli. These findings suggest that mindfulness is neither the most common psychological state nor the only productive psychological state for the workplace.

Ruggs et al. (2013) argued that industrial–organizational (I–O) psychologists have “gone fishing” insofar as we (as a field) have neglected conducting research on minority groups in the workplace. They offer interesting and insightful suggestions for researching the seven groups named in their paper. We believe that many of these ideas can be extended to other minority groups as well. It is our sincere hope that the focal article will attract the attention of both new and established researchers interested in studying minority group members' experiences in the workplace. We also hope that the article will validate and motivate researchers who already study these issues and confirm the importance of including minority perspectives in the I–O literature.