Organizing for Mars: A Task Management Perspective on Work within Spaceflight Multiteam Systems.

OBJECTIVE: The aim of this study was to examine how task, social, and situational factors shape work patterns, information networks, and performance in spaceflight multiteam systems (MTSs). BACKGROUND: Human factors research has explored the task and individual characteristics that affect decisions regarding when and in what order people complete tasks. We extend this work to understand how the social and situational factors that arise when working in MTSs affect individual work patterns. METHODS: We conducted a complex multi-site space analog simulation with NASA over the course of 3 years. The MTS task required participants from four teams (Geology, Robotics, Engineering, and Human Factors) to collaborate to design a well on Mars. We manipulated the one-way communication delay between the crew and mission support: no time lag, 60-second lag, and 180-second lag. RESULTS: The study revealed that team and situational factors exert strong effects: members whose teams have less similar mental models, those whose teams prioritize their team goal over the MTS goal, and those working in social isolation and/or under communication delay engage longer on tasks. Time-on-task positively predicts MTS information networks, which in turn positively predict MTS performance when communication occurs with a delay, but not when it occurs in real-time. CONCLUSION: Our findings contribute to research on task management in the context of working in teams and multiteam systems. Team and situational factors, along with task factors, shape task management behavior. APPLICATION: Social and situational factors are important predictors of task management in team contexts such as spaceflight MTSs.

Using Trellis software to enhance high-quality large-scale network data collection in the field.

Trellis is a mobile platform created by the Human Nature Lab at the Yale Institute for Network Science to collect high-quality, location-aware, off-line/online, multi-lingual, multi-relational social network and behavior data in hard-to-reach communities. Respondents use Trellis to identify their social contacts by name and photograph, a procedure especially useful in low-literacy populations or in contexts where names may be similar or confusing. We use social network data collected from 1,969 adult respondents in two villages in Kenya to demonstrate Trellis' ability to provide unprecedented metadata to monitor and report on the data collection process including artifactual variability based on surveyors, time of day, or location.

How Team Interlock Ecosystems Shape the Assembly of Scientific Teams: A Hypergraph Approach.

Today's most pressing scientific problems necessitate scientific teamwork; the increasing complexity and specialization of knowledge render "lone geniuses" ill-equipped to make high-impact scientific breakthroughs. Social network research has begun to explore the factors that promote the assembly of scientific teams. However, this work has been limited by network approaches centered conceptually and analytically on "nodes as people," or "nodes as teams." In this paper, we develop a ' team-interlock ecosystem' conceptualization of collaborative environments within which new scientific teams, or other creative team-based enterprises, assemble. Team interlock ecosystems comprise teams linked to one another through overlapping memberships and/or overlapping knowledge domains. They depict teams, people, and knowledge sets as nodes, and thus, present both conceptual advantages as well as methodological challenges. Conceptually, team interlock ecosystems invite novel questions about how the structural characteristics of embedding ecosystems serve as the primordial soup from which new teams assemble. Methodologically, however, studying ecosystems requires the use of more advanced analytics that correspond to the inherently multilevel phenomenon of scientists nested within multiple teams. To address these methodological challenges, we advance the use of hypergraph methodologies combined with bibliometric data and simulation-based approaches to test hypotheses related to the ecosystem drivers of team assembly.

Mapping the Growing Discipline of Dissemination and Implementation Science in Health.

BACKGROUND: The field of dissemination and implementation (D&I) research in health has grown considerably in the past decade. Despite the potential for advancing the science, limited research has focused on mapping the field. METHODS: We administered an online survey to individuals in the D&I field to assess participants' demographics and expertise, as well as engagement with journals and conferences, publications, and grants. A combined roster-nomination method was used to collect data on participants' advice networks and collaboration networks; participants' motivations for choosing collaborators was also assessed. Frequency and descriptive statistics were used to characterize the overall sample; network metrics were used to characterize both networks. Among a sub-sample of respondents who were researchers, regression analyses identified predictors of two metrics of academic performance (i.e., publications and funded grants). RESULTS: A total of 421 individuals completed the survey, representing a 30.75% response rate of eligible individuals. Most participants were White (n = 343), female (n = 284, 67.4%), and identified as a researcher (n = 340, 81%). Both the advice and the collaboration networks displayed characteristics of a small world network. The most important motivations for selecting collaborators were aligned with advancing the science (i.e., prior collaborators, strong reputation, and good collaborators) rather than relying on human proclivities for homophily, proximity, and friendship. Among a sub-sample of 295 researchers, expertise (individual predictor), status (advice network), and connectedness (collaboration network) were significant predictors of both metrics of academic performance. CONCLUSIONS: Network-based interventions can enhance collaboration and productivity; future research is needed to leverage these data to advance the field.

Neighborhood Isolation in Chicago: Violent Crime Effects on Structural Isolation and Homophily in Inter-Neighborhood Commuting Networks, 2002-2013.

Urban sociologists and criminologists have long been interested in the link between neighborhood isolation and crime. Yet studies have focused predominantly on the internal dimension of social isolation (i.e., increased social disorganization and insufficient jobs and opportunities). This study highlights the need to assess the external dimension of neighborhood isolation, the disconnectedness from other neighborhoods in the city. Analyses of Chicago's neighborhood commuting network over twelve years (2002-2013) showed that violence predicted network isolation. Moreover, pairwise similarity in neighborhood violence predicted commuting ties, supporting homophily expectations. Violence homophily affected tie formation most, while neighborhood violence was important in dissolving ties.

The effects of diversity and network ties on innovations: The emergence of a new scientific field.

This study examines the influence of different types of diversity, both observable and unobservable, on the creation of innovative ideas. Our framework draws upon theory and research on information processing, social categorization, coordination, and homophily to posit the influence of cognitive, gender, and country diversity on innovation. Our longitudinal model is based on a unique dataset of 1,354 researchers who helped create the new scientific field of Oncofertility, by collaborating on 469 publications over a four-year period. We capture the differences among researchers along cognitive, country and gender dimensions, as well as examine how the resulting diversity or homophily influences the formation of collaborative innovation networks. We find that innovation, operationalized as publishing in a new scientific discipline, benefits from both homophily and diversity. Homophily in country of residence and working with prior collaborators help reduce uncertainty in the interactions associated with innovation, while diversity in knowledge enables the recombinant knowledge required for innovation.

Understanding the assembly of interdisciplinary teams and its impact on performance.

Interdisciplinary teams are assembled in scientific research and are aimed at solving complex problems. Given their increasing importance, it is not surprising that considerable attention has been focused on processes of collaboration in interdisciplinary teams. Despite such efforts, we know less about the factors affecting the assembly of such teams in the first place. In this paper, we investigate the structure and the success of interdisciplinary scientific research teams. We examine the assembly factors using a sample of 1,103 grant proposals submitted to two National Science Foundation interdisciplinary initiatives during a 3-year period, including both awarded and non-awarded proposals. The results indicate that individuals' likelihood of collaboration on a proposal is higher among those with longer tenure, lower institutional tier, lower H-index, and with higher levels of prior co-authorship and citation relationships. However, successful proposals have a little bit different relational patterns: individuals' likelihood of collaboration is higher among those with lower institutional tier, lower H-index, (female) gender, higher levels of prior co-authorship, but with lower levels of prior citation relationships.