Exploring Social Care Network Structures

https://doi.org/10.61152/HDNZ4028

Jasmine Fernandez1, Michaela Bonnett1, Teri Garstka2 and Meaghan Kennedy1

1Orange Sparkle Ball, 2Social Innovation Labs, The University of Kansas

Series: Sunbelt 2024

Original Publication Date: June 25th, 2024

Publisher: Orange Sparkle Ball

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Abstract

Exploring Social Care Network Structures

This research is grounded in the theory that scale-free networks form between many organizations in a community when coordinating social care services and influential hubs in the network emerge (Barabási & Réka, 1999).We explore the variability in the structures of social care networks, focusing on how the diverse needs of community members and the array of providers influence these structures. We posit that the architecture of these networks may hold the key to discerning patterns in community health and social outcomes.

Our study examines the resilience of social care networks, defining them as systems designed to enhance interactions among all nodes to meet diverse community needs. We discuss community as a network and community resilience as a process, introducing three key properties—scale-free, small world, and hubness/information spreading scores, for understanding network resilience.

We analyzed 20 social care networks, which have been active over an 18-month period using the referral technology tool to send and receive service referrals, providing raw interaction data among organizational nodes. We focused on two primary objectives: 1) Social care networks are more likely to exhibit scale-free properties and contain influential hubs; and 2) There is significant variability among social care networks in terms of scale-free properties and centrality measures.

Using the three properties—small world, scale-free, and hubness/information spreading scores—we classified the 20 social care networks into different structural profiles. We analyzed node,edge radius, diameter, to understand the network structure characteristics. Our findings highlighted four distinct network structures, which we ranked from most to least resilient. We discussed the implications of these structures on community-level outcomes, including the potential centralized vulnerability when hubs and information spreaders overlap, creating efficiency during normal operations but also increasing vulnerability to disruptions.

Our findings offer insights into the emergent properties of complex systems, particularly in networks intentionally designed to enhance resilience and meet diverse community needs. We conclude by discussing the variability in centrality and structural metrics within the identified groups and propose future research directions to explore the long-term impact of these network structures.

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Citation List

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Norris, F. H., Stevens, S. P., Pfefferbaum, B., Wyche, K. F., & Pfefferbaum, R. L. (2008). Community Resilience as a Metaphor, Theory, Set of Capacities, and Strategy for Disaster Readiness. American Journal of Community Psychology, 41(1–2), 127–150. https://doi.org/10.1007/s10464-007-9156-6

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A Model for Technology-Enabled Community Resilience

https://doi.org/10.61152/PLCR9111

Meaghan Kennedy1, Michaela Bonnett1, and Teri Garstka2

1Orange Sparkle Ball, 2Social Innovation Labs, The University of Kansas

Series: Sunbelt 2024

Original Publication Date: June 25th, 2024

Publisher: Orange Sparkle Ball

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Abstract

A Model for Technology-Enabled Community Resilience

Introduction
Tech-Enabled Community Resilience is an innovative model designed to enhance resilience and optimize impact in complex systems such as communities and ecosystems. The model leverages social network analysis and technology to visualize network dynamics, measure interactions, and implement targeted interventions.
Model Structure
The approach consists of two key stages: a Startup Phase focused on assembling champions and co-creating a shared vision, and a Steady-state Phase involving iterative measurement and intervention. By utilizing technology platforms for data collection and visualization, the model provides near real-time understanding of network functioning.
Advantages Over Traditional Approaches
Traditional resource mapping approaches provide a limited understanding of the network based on a static understanding of resources and a lack of complexity about network function. The Tech-Enabled Community Resilience model provides for a more dynamic, systems-thinking perspective. The model allows for precision interventions based on network structure, potentially influencing community-level outcomes.
Case Studies and Research Findings
Case studies from social care networks and economic development initiatives demonstrate the model's applicability across various contexts. Research findings linking network cohesion to improved community outcomes during crises, and network structure to increased innovation in ecosystems, underscore the model's potential impact.
Future Directions
Further model refinement includes the development of a portfolio of network-based interventions, integration of real-time data sources, and strategies for adaptive governance structures. This model represents a significant advancement in how to understand and harness complex systems for community resilience and impact optimization.

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Citation List

Bonnett, M., Ladetto, A., Kennedy, M., Fernandez, J., & Garstka, T. (2024, June 25). Network Analysis of a Mobility Ecosystem in Detroit, MI [Conference Talk]. Sunbelt, Edinburgh, Scotland. https://www.insna.org/events/sunbelt-2024---edinburgh##

Cantner, U., & Graf, H. (2006). The network of innovators in Jena: An application of social network analysis. Research Policy, 35(4), 463–480. https://doi.org/10.1016/j.respol.2006.01.002

Garstka, T. (2024, June 25). The Relationship Between Community Networks and Population-Level Outcomes [Conference Talk]. Sunbelt, Edinburgh, Scotland. https://www.insna.org/events/sunbelt-2024---edinburgh##

Garstka, T., & Kennedy, M. (2023, May). Tech-Enabled Community Resilience [Conference Presentation]. Good Tech Fest, Washington, DC. https://www.goodtechfest.com/

Gartska, T., Kennedy, M., & Bonnett, M. (2024, September). Tech-Enabled Community Resilience: Research on the Transformative Role of Inclusive Social Care Ecosystems. International Social Innovation Research Conference (ISIRC), Bern, Switzerland. https://www.nunify.com/events/isir1

Isada, F. (2021). The Partnership Network Structure of Automakers under Radical Technological Change. Business Systems Research Journal, 12(2), 95–113. https://doi.org/10.2478/bsrj-2021-0021

Norris, F. H., & Stevens, S. P. (2007). Community Resilience and the Principles of Mass Trauma Intervention. Psychiatry, 70(4), 320–328. https://doi.org/10.1521/psyc.2007.70.4.320

Norris, F. H., Stevens, S. P., Pfefferbaum, B., Wyche, K. F., & Pfefferbaum, R. L. (2008). Community Resilience as a Metaphor, Theory, Set of Capacities, and Strategy for Disaster Readiness. American Journal of Community Psychology, 41(1–2), 127–150. https://doi.org/10.1007/s10464-007-9156-6

Network Analysis of a Mobility Ecosystem in Detroit, MI

https://doi.org/10.61152/HEJW8941

Michaela Bonnett1, Meaghan Kennedy1, Angela Ladetto2, Jasmine Fernandez1 and Teri Garstka3

1Orange Sparkle Ball, 2Detroit Regional Partnership, 3Social Innovation Labs, The University of Kansas

Series: Sunbelt 2024

Original Publication Date: June 25th, 2024

Publisher: Orange Sparkle Ball

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Abstract

Network Analysis of a Mobility Ecosystem in Detroit, MI

Background
As part of a new initiative from the Global Epicenter of Mobility (GEM), organizations across many sectors in Detroit, MI, and surrounding counties are collaboratively investing in transforming the local legacy mobility industry into an inclusive advanced mobility cluster over the next 3 years. At the start of this initiative, in partnership with the research team at the Detroit Regional Partnership, a social network analysis was conducted to map the relationship between the foundational 24 organizations, the greater coalition, and their extended network to date. The organizations within this initiative were divided into 4 sectors that highlighted key differences in engagement This baseline map and relationship data, as well as key network analysis metrics, will be compared to future data collections over the coming years to track the initiative’s progress.

Methods
The original coalition (161 organizations) was identified by the local partner organization and data collection proceeded from September-December 2023 through survey completion. One or more representatives of coalition organizations were asked to identify their relationship to other members of the coalition using a 1-5 scale (Frey et al., 2006). Data were analyzed in R, and organization-level metrics, as well as centralized network-wide metrics, were produced for weighted betweenness, degree, and weighted degree centrality, as well as averages of connection strength. Maps were produced using KUMU software.

Findings
The mobility coalition consisted of 159 nodes and 7412 connections. Of those connections, 3763 (50.77%) had at least a level 1 connection strength, while 2319 (31.29%) had a connection strength of ≧ 3 (an active working relationship). The average connection strength for the network was 2.13. The coalition network was highly interconnected, with a clustering coefficient of 0.70 and a density of 0.59. Nonprofit and foundation organizations made up 47.5-50% of the top quartile by all centrality metrics while only making up 32.1% of the network. Corporate and private organizations made up 42.8% of the network and made up 68.42-82.50% of the bottom quartile across all metrics. The distribution of centrality scores of the corporate and private organizations was significantly lower than those of all other sectors within the network.

These results illuminate a network that is highly interconnected, but in which not all sectors are engaging equally. These results are being used to plan and implement strategic interventions to foster new relationships and growth within the network. In addition to the 159 coalition organizations, respondents to the survey identified an additional 244 organizations as active participants within the Detroit region mobility space. A select number of these organizations will be added to the coalition as it becomes established within the Detroit region. These provide directions for future growth of the GEM initiative and the mobility ecosystem network and are examples of turning research into action.

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Frey, B. B., Lohmeier, J. H., Lee, S. W., & Tollefson, N. (2006). Measuring Collaboration Among Grant Partners. American Journal of Evaluation, 27(3), 383–392. https://doi.org/10.1177/1098214006290356

Garstka, T., & Kennedy, M. (2023a, September). Convening of Mapping Our Social Fabric: The Tech-Enabled Community Resilience Summit [Virtual Summit]. Mapping Our Social Fabric: The Tech-Enabled Community Resilience Summit, Online. https://www.tecresilience.com/

Garstka, T., & Kennedy, M. (2023b, October). Establishing a Responsive and Inclusive Governance Structure for Multi-Sector Technology and Data Collaborations [Conference Presentation]. US Census Bureau Data Innovation Summit, San Juan, Puerto Rico. https://datasymposium23.splashthat.com/

Greenwald, H. P., & Zukoski, A. P. (2018). Assessing Collaboration: Alternative Measures and Issues for Evaluation. American Journal of Evaluation, 39(3), 322–335. https://doi.org/10.1177/1098214017743813

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