How can nonprofit networks be rewired for maximum social impact?
This is the main question that the Network for Nonprofit and Social Impact is dedicated to answer.
Located at Northwestern University, the research team includes undergraduates, graduate students, post-doctoral fellows, and faculty members. Since its establishment in 2012, NNSI uniquely focuses on research that emphasizes collaborative efforts and network structures that include nonprofit organizations and their many organizational and community partners.
Meet the Team
Michelle Shumate is a Professor in Communication Studies and Associate Faculty at the Institute for Policy Research at Northwestern University. Her research focuses on how to design interorganizational networks to make the most social impact, which has been funded by the National Institutes of Health, Bill and Melinda Gates Foundation, and Army Research Office. In addition to leading NNSI, she offers workshops, consulting, and coaching through the Social Impact Network Consulting. Professor Shumate holds a Ph.D. from the Annenberg School for Communication at the University of Southern California and a Bachelor’s Degree from Pepperdine University.
Graduate Research Assistant
Graduate Research Assistant
Graduate Research Assistant
Data Research Analyst Associate
Research Assistant
Research Assistant
Strategic Communications Intern
Strategic Communications Intern
Strategic Communications Intern
Current Projects
Help Seeking and Navigation Support
Technology and human-centered navigation solutions have emerged in the last decade as ways to support individual help-seeking. The key idea behind these interventions is that it is confusing for individuals to identify which organizations offer benefits, programs, and services that they are eligible for and that the enrollment processes for these benefits, programs, and services may be a barrier. This pilot project examines the experiences of individuals who have received help or sought help but didn’t receive help. We will interview individuals about their experiences with help-seeking during the COVID-19 pandemic.
Examining the Outcomes of Repeated Client Referrals Based on the Trajectories of Care
The proposed study aims to introduce and validate a system-derived measure of the quality of client outcomes based upon trajectories of care. The focus will be on network performance, defined as “the longitudinal improvement of client well-being across multiple service episodes.” We will analyze data from Combined Arms, a collaborative network that provides support for veteran transition. This study will then aim to answer three questions: 1) What is the effectiveness of different pathways in the network over time? 2) Which referrals should be made first? 3) Which providers are most effective at each stage along the trajectory of care?
Predictive Analytics with the Veterans Services of the Carolinas
Veterans Services of the Carolinas (VSC) provides supportive services to veterans and military-connected individuals across North Carolina. Recently, VSC has been using a predictive analytics product developed by UniteUs to identify veterans and military-connected individuals with high social needs scores who do not currently have a relationship with social services or a medical home.
Research questions include: 1) What services do clients identified through predictive analytics request and receive? 2) How similar are these services to clients who enter NCServes through other means? 3) What combination of services improves social needs scores for clients identified through predictive analytics? 4) How similar are the social needs scores of clients who enter NCServes through other means?
Improving Veterans Referrals by Optimizing Network Design in Response to COVID-19
AmericaServes is the United States’ first coordinated system of public, private, and nonprofit organizations working together in communities to serve veterans, transitioning service members, and their families. We will analyze data collected in their shared referral management and analytics platform. First, the study will examine how referral networks adapt to a substantial increase in the number of cases as a result of the secondary impacts of COVID-19 (i.e., economic downturn, disruption of service delivery mechanisms). Second, this research will utilize high-level analytics to understand how different network structures perform in the face of a surge of cases.
