Abstract: Understanding Interorganizational Collaboration through Network Analyses (Society for Social Work and Research 24th Annual Conference - Reducing Racial and Economic Inequality)

688P Understanding Interorganizational Collaboration through Network Analyses

Sunday, January 19, 2020
Marquis BR Salon 6 (ML 2) (Marriott Marquis Washington DC)
* noted as presenting author
Wonhyung Lee, PhD, Assistant Professor, State University of New York at Albany, Albany, NY
Charalampos Chelmis, PhD, Assistant Professor, State University of New York at Albany, Albany, NY
Miley Yao, Doctoral Student, State University of New York at Albany, Albany, NY
Background and Purpose: Human service organizations (HSOs) operate in an environment that is often considered difficult for collaboration, commonly described as being “siloed.” Despite the challenge, many HSOs do collaborate in various forms, the benefits of which have been conceptualized by prior research. Few studies, however, have visualized the collaboration network among HSOs using empirical data. This study aims to explain the characteristics of interorganizaitonal collaboration using network analyses of service coordination among local HSOs.

Methods: This study used mixed methods. First we conducted structured, in-person interviews with local human service organizations in Albany, NY. Second, two networks were constructed based on the questions about 1) how they coordinate services on a daily basis and 2) who they think is important for developing new technologies for better service coordination. Additionally, we asked a number of structured and open-ended questions to understand the characteristics of each organization and their experiences in service coordination in the region. To recruit interviewers, we used a snowball sampling approach. In total, 43 organizations participated in the interviews between April and October 2018. Interviews took on average one hour. Based on the results of the interviews, we constructed a network, where nodes denote the organizations and edges represent associations between those organizations. We analyzed the structure of this network to identify structurally important nodes and “communities,” (i.e., groups of organizations that share distinctive network relationships) and to study the characteristics of such relationships among HSOs.

Results: The network analysis identified several organizations that play a central role in connecting HSOs in the local service coordination efforts. However, the network of day-to-day interactions and the network of influence for technical advancement revealed some differences. The government agencies such as Department of Social Services and large multi-service organizations seem to play a more significant role in coordinating daily operations whereas grassroots, nonprofit organizations carry a relatively stronger weight in implementing novel coordination tools. The network, unlike other social networks, revealed a multipolar structure, which implies low connectivity among human service organizations. Qualitative insights regarding the factors that prevent organizations from coordinating services, such as staff egos, client privacy, competitive environment, and lack of resources or effective database, provide possible explanations for the multipolar structure of the network.

Conclusions and Implications: The multipolar network of HSOs enables us to visualize the effects of the environment that could be particularly challenging for service coordination. Based on the findings, organizations can target the key connectors and influencers to initiate service coordination or bring a new coordination model in the region. Future research can further investigate the characteristics of isolated network communities and whether it would be worthwhile to facilitate service coordination with other organizations in the region.