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In response to the global COVID-19 pandemic, many businesses closed their offices and asked their employees to work from home. The transition to remote work has yielded performance gains for many companies; so much so that many firms are planning to continue to use remote work after the pandemic subsides. Nevertheless, such benefits may not be distributed equally throughout the workforce. Drawing on the sociocognitive theory of socioeconomic status (SES), we predict that one's home working environment features salient signals of their social status that affect their performance. Based on a sample of 304 remote workers from within the United States collected during the COVID-19 shutdown, we find that individuals whose home offices connote higher levels of SES report a greater sense of control over their environment, which ultimately is associated with higher levels of perceived job performance. Furthermore, we find that the more time an individual spends in their home office, the stronger the relationship between their environment-based SES and their personal sense of control. Taken as a whole, our findings suggest that because home working environments are arrayed along an SES gradient, they present another mechanism by which pre-existing inequalities may be made salient as a result of the COVID-19 pandemic.

Emergent states are team-level attributes that reflect team members' collective attitudes, values, cognitions, and motivations and influence team effectiveness. When measuring emergent states (e.g., cohesion, conflict, satisfaction), researchers frequently collect ratings from individual group members and aggregate them to the team level. After aggregating to the team level, researchers typically focus on mean differences across teams and ignore variability within teams. Rather than focusing on the mean level of emergent states, this study draws on recent advances in multilevel theory and describes an approach for examining the specific patterns of dispersion (i.e., disagreement) across five emergent states. Our findings suggest that teams reliably demonstrate different patterns of rating dispersion that are consistent with existing theoretical frameworks and typologies of dispersion, yet have not previously been empirically demonstrated. We also present evidence that the different patterns of dispersion in emergent states are significantly related to key team outcomes, even after controlling for the mean levels of those emergent states. These findings underscore the importance of exploring additional forms of team-level constructs and highlight ways of extending our understanding of group-level phenomena.

Meta-analyses are well known and widely implemented in almost every domain of research in management as well as the social, medical, and behavioral sciences. While this technique is useful for determining validity coefficients (i.e., effect sizes), meta-analyses are predicated on the assumption of independence of primary effect sizes, which might be routinely violated in the organizational sciences. Here, we discuss the implications of violating the independence assumption and demonstrate how meta-analysis could be cast as a multilevel, variance known (Vknown) model to account for such dependency in primary studies' effect sizes. We illustrate such techniques for meta-analytic data via the HLM 7.0 software as it remains the most widely used multilevel analyses software in management. In so doing, we draw on examples in educational psychology (where such techniques were first developed), organizational sciences, and a Monte Carlo simulation (Appendix). We conclude with a discussion of implications, caveats, and future extensions. Our Appendix details features of a newly developed application that is free (based on R), user-friendly, and provides an alternative to the HLM program.

Management researchers often use consensus-based composition models to examine the antecedents and effects of higher-level constructs. Typically, researchers present three indices, rwg, ICC(1), and ICC(2), to demonstrate agreement and consistency among lower-level units when justifying aggregation. Nevertheless, researchers debate what values for these indices are sufficient. This study examines the distributional characteristics of ICCs and rwg values from three sources: the multilevel literature, a large multinational sample of student teams, and a large sample of randomly generated "pseudo teams." Our results support existing cutoff criteria for ICCs but suggest that generally accepted values for rwg may, under certain circumstances, reflect pseudo-agreement (i.e., agreement observed among two raters not attributable to the same target). Thus, when there is minimal between-group variance (i.e., low ICCs), it is difficult to determine whether high rwg values reflect agreement or pseudo-agreement. Based on these findings, we provide recommendations to help researchers interpret aggregation indices.