The debate over eliminating performance ratings addresses many important theoretical and practical issues. However, the academic debate on the topic is disconnected from the concerns of practitioners. Knowledge gained from theory-driven research is not leading practice on the use of performance ratings, despite the large volume of potentially relevant research findings. Many organizations are charging ahead with performance management solutions that seem sensible to them. They may be interested in academic research, but they are not waiting for it. We will argue that academic researchers who hope to influence practice need to better understand the concerns of practitioners and the research opportunities that are presented by contemporary practice.

Over the last few years, the concept of agility has become increasingly popular in organizations. Companies are hoping to foster speed, adaptability, and innovation by rolling out an agile strategy, implementing agile methods, and creating an agile mind-set among leaders and employees. There is, however, much ambiguity about what the concept of agility entails and how it can be successfully implemented in an organization. The purpose of this practice-focused paper is to organize the extant agility literature for readers by giving an overview of different definitions of agility; outlining evidence-based factors that contribute to agility at the individual, team, and organizational levels; and describing three practical examples at a large German car company. Finally, the authors suggest steps that organizations can take to increase agility in their workforce.

This article addresses the tensions between calls for civility and rights to free speech in public academic employment. We begin by summarizing relevant organizational science on workplace incivility. Next come critical perspectives from other fields, asserting that civility appeals infringe on rights to freedom of expression. Following this is a review of key court decisions within the jurisprudence of free speech in the workplace, especially as it applies to academics. We also address the protections afforded by tenure and (at some institutions) unions. Bringing these streams of scholarship together, we expose predicaments faced by public universities seeking to cultivate safe and civil work environments while, at the same time, respecting faculty rights to free speech. We conclude by suggesting compromises between these conflicting aims that would allow organizations (in academia and beyond) to protect workforce dignity without infringing on the rights of reasonable people.

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).

It is our contention that industrial-organizational (I-O) science can do many great things for the world of work, but we must first get it out there more readily and fully into the hands of decision makers, policy makers, and the public. This focal article addresses the following topics: (a) Why isn’t I-O science reaching the public? (b) What are good mechanisms to bring I-O science to the public? (c) What are some keys to translation and public consumption? Specific public-facing activities discussed include writing a trade book, writing for trade magazines (e.g., Harvard Business Review [HBR]) and online blogs (e.g., Fortune), leveraging social media (e.g., LinkedIn), submitting op-eds, doing podcasts as a producer and/or guest, and joining a speakers bureau. We also discuss barriers to these activities such as time, reward structures, and skill deficits.

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.

A dominant general factor (DGF) is present when a single factor accounts for the majority of reliable variance across a set of measures (Ree, Carretta, & Teachout, 2015). In the presence of a DGF, dimension scores necessarily reflect a blend of both general and specific factors. For some constructs, specific factors contain little unique reliable variance after controlling for the general factor (Reise, 2012), whereas for others, specific factors contribute a more substantial proportion of variance (e.g., Kinicki, McKee-Ryan, Schriesheim, & Carson, 2002). We agree with Ree et al. that the presence of a DGF has implications for interpreting scores. However, we argue that the conflation of general and specific factor variances has the strongest implications for understanding how constructs relate to external variables. When dimension scales contain substantial general and specific factor variance, traditional methods of data analysis will produce ambiguous or even misleading results. In this commentary, we show how several common data analytic methods, when used with data sets containing a DGF, will substantively alter conclusions.

The rapid pace at which technology changes creates a challenge for industrial-organizational (I-O) psychologists, who often conduct hypothetico-deductive research. In this article, we examine technology research in the I-O psychology community by asking three questions: Why should I-O psychologists study new technologies? How timely is I-O psychologists’ technology research? How can I-O psychologists produce timelier technology research? Using archival data from 23 years of SIOP conferences and a historical timeline of technology innovations, we find that I-O psychologists study technology milestones an average of 6.10 years after they first enter widespread awareness and adoption. We discuss the implications of this lag and conclude by urging I-O psychologists to study technology with an eye toward action, exploration, collaboration, dissemination, and creation.

The term resilience has grown in popularity among the general public and within the scientific community. Unfortunately, the rise in popularity has advanced neither our theoretical understanding of this construct nor the methodological approaches to study this topic. Britt, Shen, Sinclair, Grossman, and Klieger (2016) highlighted important conceptual, methodological, and practical advances and shortcomings within the literature on employee resilience. However, critical conceptual issues remain unaddressed within the focal article. Specifically, in our commentary, we differentiate between resiliency and resilience, identify key dimensions of each construct, and explicate the role of adversity and context to further advance our theoretical understanding of this construct. We conclude by discussing the implications of our proposed conceptualization and refinement of resilience for theory, research, and practice.