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Professional networks help employees accomplish work tasks, progress in their careers, and thrive personally. Decades of research suggest that achieving these outcomes requires more than simply amassing 'more' network contacts. Instead, networks with certain characteristics (e.g., networks that are open, diverse, and deep) enhance effectiveness. Network training teaches trainees about effective networks and helps trainees identify their network development needs by providing feedback on their current network. Once back on the job, trainees are assumed to take appropriate actions to develop their networks. However, our research and experience training MBA students, executives, and employees at all levels suggest that trainees often struggle to develop their networks after training. We studied 119 trainees engaged in network training and development to understand why. Our investigation revealed that many trainees fail to set network development goals that match their personalized feedback, identify strategies that match their goals, and take actions to develop networks that match their strategies. These mismatches create gaps in the bridge that trainees build to take themselves from network training to development. Further, even after building strong bridges by aligning their feedback, goals, strategies, and actions, many trainees encounter on-the-job barriers that prevent them from improving the effectiveness of their network. We offer a guide to help address translational gaps and mitigate on-the-job barriers, thereby enhancing the translation of network training insights into network development. 

Team membership in today’s open talent economy is more fluid and interchangeable than ever before. In light of these dynamics, we consider how team members’ signaling of human and social capital, in the form of challenging or supportive voice, informs our understanding of how individuals across an organizational network self-assemble into temporary work teams. We test our hypotheses in two separate multiwave studies and find support for our hypotheses above and beyond the effects of homophily. In Study 1, we find support for a human capital pathway in which challenging voice in a team fosters perceptions of quality work that enhance one’s personal reputation in the broader network. Personal reputation, in turn, predicts team assembly decisions. In Study 2, we consider a social capital pathway alongside the human capital pathway. We find that supportive voice in a team fosters friendship that enhances the extent to which one is trusted in the broader network, and trust subsequently influences team assembly decisions. Potential team members appear to prioritize the social capital signaled by supportive voice more so than the human capital signaled by challenging voice, although those who possess both human and social capital are also highly sought during team formation. We discuss the implications of these findings for the literatures on voice and team assembly. 

Substantial research has documented challenges women experience building and benefiting from networks to achieve career success. Yet fundamental questions remain regarding which aspects of men’s and women’s networks differ and how differences impact their careers. To spur future research to address these questions, we present an integrative framework to clarify how and why gender and networks—in concert—may explain career inequality. We delineate two distinct, complementary explanations: (1) unequal network characteristics (UNC) asserts that men and women have different network characteristics, which account for differences in career success; (2) unequal network returns (UNR) asserts that even when men and women have the same network characteristics, they yield different degrees of career success. Further, we explain why UNC and UNR emerge by identifying mechanisms related to professional contexts, actors, and contacts. Using this framework, we review evidence of UNC and UNR for specific network characteristics. We found that men’s and women’s networks are similar in structure (i.e., size, openness, closeness, contacts’ average and structural status) but differ in composition (i.e., proportion of men, same-gender, and kin contacts). Many differences mattered for career success. We identified evidence of UNC only (same-gender contacts), UNR only (actors’ and contacts’ network openness, contacts’ relative status), neither UNC nor UNR (size), and both UNC and UNR (proportion of men contacts). Based on these initial findings, we offer guidance to organizations aiming to address inequality resulting from gender differences in network creation and utilization, and we present a research agenda for scholars to advance these efforts. 

Driven by views of teams as dynamic systems with permeable boundaries, scholars are increasingly seeking to better understand how team membership changes (i.e., team members joining and/or leaving) shape the functioning and performance of organizational teams. However, empirical studies of team membership change appear to be progressing in three largely independent directions as researchers consider: (a) how newcomers impact and are impacted by the teams they join; (b) how teams adapt to member departures; or (c) how teams function under conditions of high membership fluidity, with little theoretical integration or consensus across these three areas. To accelerate an integrative stream of research on team membership change, we advance a conceptual framework which depicts each team membership change as a discrete team-level “event” which shapes team functioning to the extent to which it is “novel,” “disruptive,” and “critical” for the team. We use this framework to guide our review and synthesis of empirical studies of team membership change published over the past 20 years. Our review reveals numerous factors, across conceptual levels of the organization, that determine the strength (i.e., novelty, disruptiveness, criticality) of a team membership change event and, consequently, its impact on team functioning and performance. In closing, we provide propositions for future research that integrate a multilevel, event-based perspective of team membership change and demonstrate how team membership change events may impact organizational systems over time and across levels of observation. 

Constructs that reflect differences in variability are of interest to many researchers studying workplace phenomena. The aggregation methods typically used to investigate “variability-based” constructs suffer from several limitations, including the inability to include Level 1 predictors and a failure to account for uncertainty in the variability estimates. We demonstrate how mixed-effects location-scale (MELS) and heterogeneous variance models, which are direct extensions of traditional mixed-effects (or multilevel) models, can be used to test mean (location)- and variability (scale)-related hypotheses simultaneously. The aims of this article are to demonstrate (a) how the MELS and heterogeneous variance models can be estimated with both nested cross-sectional and longitudinal data to answer novel research questions about constructs of interest to organizational researchers, (b) how a Bayesian approach allows for the inclusion of random intercepts and slopes when predicting both variability and mean levels, and finally (c) how researchers can use a multilevel approach to predict between-group heterogeneous variances. In doing so, this article highlights the added value of viewing variability as more than a statistical nuisance in organizational research.