Understanding how leadership functions within teams is critical to improving cohesion, communication, and overall performance. While prior research has focused on psychological and behavioral indicators of effective teams, less is known about how network structures of leadership evolve within teams and whether specific patterns correspond to stronger team dynamics and performance outcomes. This study applies social network analysis (SNA) to examine leadership structures in collegiate sports teams across multiple seasons.

Using a valued network approach, we analyze pre- and post-season leadership nominations alongside measures of team cohesion (GEQ) and communication effectiveness (SECTS) to explore whether network properties—such as density, centralization, reciprocity, and leadership stability—correlate with reported team dynamics. Leadership stability is assessed through the persistence of leadership nominations over time, identifying whether teams maintain consistent leadership structures or experience frequent shifts in who is perceived as a leader.

By tracking how leadership roles emerge and evolve over a season, this research investigates whether highly interconnected leadership structures promote stronger cohesion and adaptability or if certain network patterns, such as over-centralized leadership, present challenges for team function. The study contributes to ongoing discussions on network indicators of group effectiveness and offers insights into how leadership development can be assessed through structural rather than purely psychometric measures.

Findings from this research have implications for coaches, organizations, and scholars interested in optimizing leadership structures and fostering effective team environments.

Interprofessional teamwork is critical in healthcare. However, research to date neglects the dynamic aspect of teamwork from a network perspective. Through analysis of a data on 17 inter-professional teams, managing simulated medical emergencies, we describe and apply the Hierarchical Relational Event Model (HREM) to investigate: the network features of dynamic clinical teams; the similarities and differences in these features across teams; the impact of professional groups, and discuss ways to the relationship of network dynamics with externally assessed clinical performance. The results have implications for team training in healthcare and designing interventions for improving healthcare teams.

In recent years, the healthcare community has increasingly recognized the need for innovative approaches to enhance both clinical outcomes and the experiences of patients and employees. Among these, Social Network Analysis has emerged as a powerful tool for optimizing healthcare performance.

This study examines key factors influencing the performance of surgical teams, focusing on team composition within a large hospital setting. Analyzing data from more than 20,000 surgeries performed over the past five years, we build two distinct networks: one representing surgical team members and their connections based on the number of joint procedures, and another mapping individual surgeries as network nodes, connected through shared team members.

Our findings reveal significant associations between network centrality measures and surgical performance metrics, such as deviations from optimal procedure durations. In addition to traditional metrics like Betweenness and Closeness centralities, we analyzed the network positions of both employees and surgery nodes using innovative measures, including Distinctiveness Centrality and Rotating Leadership, to assess team efficiency and dynamics. Results provide valuable guidance for researchers and healthcare facility managers, demonstrating how network analysis techniques can inform more effective surgical scheduling and workforce management. By leveraging network-based strategies, hospitals can enhance patient care quality and efficiency.

Acknowledgments

This research project was funded under the agreement between the Department of Engineering of the University of Perugia and the USL Umbria 2 company, having as its object the “Development of efficiency models, both economic and in terms of service level in the management of production and organizational processes and in the management of patients in a complex health care facility”.

The funding organizations had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Context: Primary care teams play a critical role in providing high-quality value-added care to patients with cardiovascular disease (CVD). However, limited evidence exists on how social relatedness within primary care teams—defined as a sense of belonging and connection within the team beyond simple work interactions—contributes to the team’s ability to deliver high-quality CVD care at lower medical costs. This study fills this gap in the literature by investigating how social relatedness within care teams relates to CVD care delivery in US primary care settings.

Objective: To evaluate the association between social relatedness within primary care teams and healthcare utilization outcomes and medical costs for patients with cardiovascular disease.

Design: A total of 143 physicians and staff from five urban, suburban, and rural primary care clinics in Wisconsin completed a sociometric survey regarding how well they knew other team members. The survey asked, "Would you say that you and the other clinicians and clinic staff know about each other’s personal lives (e.g., family, hobbies, and interests outside of work)?" with a yes/no response. A dichotomous (0/1) social relatedness matrix was created for each team, and the density of social relatedness within each team’s social network was calculated based on the survey results. Cardiovascular disease diagnoses were identified by the presence of two validated ICD-9 codes (401.0–401.9, 428.00–428.02, 414.01, 430.0–438.9, 410.9, 427.89) on two separate occasions within the past three years. Health outcomes for patients with cardiovascular disease, including urgent care visits, emergency department visits, and hospital days in the past 12 months, were extracted from electronic health records (EHRs). Healthcare-related costs were estimated by applying average medical expenses from published reports to the utilization counts. A three-level generalized linear mixed model (GLMM) was used to assess the relationship between the density of social relatedness within team social networks and healthcare utilization and costs for the team’s patients with cardiovascular disease (N = 6,534). The model adjusted for patient-level covariates (age, gender, insurance status, diagnoses, and comorbidities including the Charlson Comorbidity Index) and clinic-level fixed effects.

Results: The density of social relatedness within team social networks ranged from 0.26 to 0.58, with a mean of 0.41 (SD = 0.09). A one standard deviation increase in team social relatedness density was associated with 23% fewer urgent care visits (OR=0.77, p=.034), 31% fewer emergency room visits (OR=0.69, p=.017), 39% fewer hospital days (OR=0.61, p=.001) and $390 lower health care costs (95%CI: [$159, $621], p<.001) for the care team’s patients with CVD in the past 12 months.

Conclusions: Interventions aimed at fostering stronger social relatedness and a sense of belonging within primary care teams may be a cost-effective strategy to improve the quality of care for patients with cardiovascular disease and reduce associated medical costs.

Focus of Sunbelt 2025 communication.

We plan to share our structural analysis of broker behavior within global virtual teams working simultaneously and independently on a standardized set of tasks over a set period in order to produce a standard output. The 4,135 team members, based in 88 countries, were assigned to 924 teams randomly at session start. 824 successfully produced a report.

For this prime exploration of the first dataset in this context designed from a network perspective, we will limit our empirical investigations to static structural multilevel network analysis, including node, structural, dyadic, and triadic traits (e.g. network constraint, density, mutual tie density, or asymmetric triad ratio) and their mutual interplay with team-level traits (such as cultural breadth, time zone range, report creativity, or report quality) and individual traits (such as negative affectivity, cultural intelligence, or English fluency), focusing specifically on brokerage potential.

Among the novel hypotheses to be explored are :

- cultural structural holes predict leadership roles;

- temporal structural holes moderate perceived leadership effectiveness;

- work relationship structural holes has a curvilinear effect on team task performance.

Empirical context.

Several times a year, X-Culture operates a two-sided platform where undergraduate and postgraduate students produce in global virtual teams an international business plan for an existing company to enter a new country with an existing or new offer. At session start, the X-Culture platform forms teams with 5 or 6 members, based in 5 or 6 countries. Participants fill an initial survey prior to team assignment. For the next 8 weeks, team members progress on intelligence gathering, analysis, and report writing, and fill surveys weekly within which they assess their peers. After the team files its report, a final survey with more peer assessment is undertaken to wrap up the session. An exclusion process also exists.

In order to study brokerage causes and effects, we collected 14,700 non null peer-to-peer data sets measuring, per our specifications, closeness of working relationship, frequency of communications, coordination and leadership roles, and conflict, as well individual traits such as Big5. Peer-perceived creativity, amiability, topical expertise, English fluency, and cultural intelligence, among others, were also measured during the session.

In Association Football, attacking players have traditionally been ranked according to the number of goals they score, and / or the number of goals they create for others. The disadvantage of this ranking system is clear: it does not account for the strengths of an opposing team. Here, we address by establishing the concept of a ‘par’ for each opponent: the average number of attacking contributions against that team. In this way, we are able to standardise measures of each player’s attacking contribution.

We use these standardised data to construct models of player influence in the teams of the 2011/12 English Premier League season. This allows us to assess not just a player’s level of contribution, but a duo’s level of contribution to the team’s attacking output. The resulting scores represent the combined strength of each attacking duo, allowing coaching staff to identify the strongest and weakest attacker combinations. Interestingly, the mean of these ‘duo scores’ appears to be a good predictor of a team’s position in the final Premier League standings. In fact, just one club recorded a top-half mean duo score, but a bottom-half final standing and we offer an explanation for this.

Viewing players as nodes in a network, with weighted, directed, edges between them illustrating influence, proves instructive for the team as a whole. For instance, algorithms grounded in Max-Plus Algebra can facilitate the identification of the strongest attacking duos, triumvirates and larger groups, all of which correspond to circuits in the network. The simplicity of this work makes it ripe for wider applicability and nuanced model development. Moreover, team structures are crucial to organisational success and, since our tools to identify key employees and groups are easily transferable to non-sporting organisations, we expect this work to be beneficial to management on a wider scale.

Collaboration is of fundamental importance to modern scientific and technological development, and expertise diversity has emerged as an important factor in predicting the success of collaboration. While expertise diversity has typically been seen as the knowledge attribute of a group (e.g., between collaborators), we provide an additional theoretical and empirical conceptualization that considers whether collaborators’ network knowledge is similar to the knowledge domain of their research output. Specifically, experience diversity or similarity can be assessed in at least two ways: between collaborators (dyadically) and between the researchers themselves and the substance of the research they engage in. We refer to these concepts as expertise diversity and divergent ideation. Expertise diversity measures the extent to which collaborators have distinct professional backgrounds, while divergent ideation represents how much the new research product diverges from what collaborators have previously worked on. While extensive research has explored the relationship between expertise diversity and innovation, few studies have examined how expertise diversity and the nature of the project (divergent ideation) jointly influence the success of first-time collaborations. This study addresses this gap by examining how these two factors—expertise diversity and divergent ideation—jointly influence both the impact of collaborative output and the likelihood of sustained collaboration.

To answer our research question we use data from 158,012 first-time partnerships recorded in the US Patent Office between the years of 1976 and 2012. We use natural language processing to estimate areas of expertise and to develop measures of expertise diversity based on inventors’ collaboration networks. Specifically, we use a Doc2Vec model on granted US utility patents to produce a 300-dimension embedding that represents the content of each patent. First, we use the embeddings of an inventor’s inventions to estimate their area of expertise (i.e. inventor’s topical focus). Topical focus is operationalized as the centroid of inventors’ prior work and is computed as the mean vector representation across all their outputs. Next, we operationalize expertise diversity as the cosine distance between a pair of inventors’ topical foci and divergent ideation as the average cosine distance between a pair of inventors’ prior expertise and the embedding location of their invention. Finally, we examine two distinct measures of partnership success—success in generating a high impact research output and success in establishing a lasting collaborative relationship.

Results show that collaborations exhibiting a high degree of expertise diversity produce more impactful products, while collaborations exhibiting a low degree of expertise diversity are more likely to collaborate again. Further, collaborations exhibiting a high degree of expertise diversity and a low-to-moderate degree of divergent ideation are most likely to create the highest impact inventions, but they are less likely to sustain their collaboration. We conclude our study by outlining the implications of our findings to the literature on diversity and technological innovation.