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The authors use proven cognitive and learning principles and recent developments in the field of educational psychology to teach the concept of the sampling distribution of the mean, which is arguably one of the most central concepts in inferential statistics. The proposed pedagogical approach relies on cognitive load, contiguity, and experiential learning theories and on the integration of new knowledge within previously formed knowledge structures. Thus, the proposed approach stimulates both visual and auditory learning, engages students in the process of learning through problem solving, and presents information so that it builds on existing knowledge. Results of an experiment including introductory statistics undergraduate students indicate that students exposed to the proposed theory-based pedagogical approach enhanced their learning by approximately 60%.

Workplace romance and sexual harassment are pervasive social-sexual phenomena in organizations. However, the processes through which dissolved workplace romances are most likely to foster sexually harassing behavior between former romantic partners are not known. The authors propose that the following factors play a critical role in influencing the likelihood that terminated workplace romances lead to sexually harassing behavior: (a) type of workplace romance as defined by pairing of each partner's primary romance motive, (b) partners' social power, (c) initiation of romantic relationship dissolution, (d) male partner's sexual harassment proclivity, (e) nature of each partner's residual affective state, and (f) organization's tolerance for sexual harassment. Based on the role of these factors, the authors provide a framework and propositions that serve to guide future research addressing the link between dissolved workplace romances and sexually harassing behavior. Methodological considerations and implications for management are also discussed.

Team research typically assumes that team performance is normally distributed: teams cluster around average performance, performance variability is not substantial, and few teams inhabit the upper range of the distribution. Ironically, although most team research and methodological practices rely on the normality assumption, many theories actually imply nonnormality (e.g., performance spirals, team composition, team learning, punctuated equilibrium). Accordingly, we investigated the nature and antecedents of team performance distributions by relying on 274 performance distributions including 200,825 teams (e.g., sports, politics, firefighters, information technology, customer service) and more than 500,000 workers. First, regarding their overall nature, only 11% of the distributions were normal, star teams are much more prevalent than predicted by normality, the power law with an exponential cutoff is the most dominant distribution among nonnormal distributions (i.e., 73%), and incremental differentiation (i.e., differential performance trajectories across teams) is the best explanation for the emergence of these distributions. Second, this conclusion remained unchanged after examining theory-based boundary conditions (i.e., tournament versus nontournament contexts, performance as aggregation of individual-level performance versus performance as a team-level construct, performance assessed with versus without a hard left-tail zero, and more versus less sample homogeneity). Third, we used the team learning curve literature as a conceptual framework to test hypotheses and found that authority differentiation and lower temporal stability are associated with distributions with larger performance variability (i.e., a greater proportion of star teams). We discuss implications for existing theory, future research directions, and methodological practices (e.g., need to check for nonnormality, Bayesian analysis, outlier management). Supplemental Material: The online appendix is available at https://doi.org/10.1287/orsc.2022.1619 .

The COVID-19 pandemic has resulted in significant talent management challenges including (a) hiring freezes and layoffs; (b) salary freezes, canceled bonuses, and pay reductions; (c) how work is done (i.e., teleworking); and (d) increased employee stress and burnout. Also, COVID-19 has made it evident that many organizations do not implement state-of-the-science performance management systems and, instead, have just performance appraisal systems that typically involve a once-a-year evaluation and review, little effort to provide feedback and coaching on an ongoing basis, little alignment between employee performance and the organization's strategic goals, a focus on past performance only, and ownership by the human resources function instead of those rating and being rated. Thus, the lack of value contributed by the usual performance evaluation and review process is the reason why so many organizations are postponing or even completely discontinuing performance evaluations. We argue that when implemented using evidence-based recommendations, performance management (rather than appraisal) serves important administrative, strategic and communication, developmental, organizational maintenance, and documentation purposes and therefore can be particularly useful in addressing contemporary talent management challenges. We offer five recommendations on how to adapt and improve performance management that will help organizations navigate crises like COVID-19 and, just as importantly, allow them to thrive after the crisis is over: (1) measure results in addition to behaviors, (2) measure adaptive performance, (3) conduct stay interviews to retain top performers, (4) implement a multisource performance management system, and (5) collect and use performance promoter scores. JEL CLASSIFICATION: J5 Labor–Management Relations, Trade Unions, and Collective Bargaining; M1 Business Administration; M5 Personnel Economics