Welcome to Research Friday! This week we welcome Mary McGillicuddy, one of the researchers who worked with the ASU College of Public Programs on a project that analyzed and mapped the social network of downtown Phoenix nonprofit organizations.

I've heard that the ASU Lodestar Center gets a ton of calls from nonprofit organizations looking to partner with other organizations. The Center works like a hub in that way, connecting the dots between the sector. In fact, the Arizona sector works that way, too — we often connect to each other through hubs.

Contrary to popular belief, social networks and social network analysis (SNA) both existed long before Facebook. SNA is a visual, quantitative measurement tool that has been used across disciplines since the 1950's. Recently, ASU's College of Public Programs used SNA to assess its impact on nonprofit organizations in the downtown Phoenix area. This kind of analysis fascinates me, and I think it can really help others understand what's really going on in the Arizona nonprofit sector. But, before diving headfirst into the research findings, there are a few important terms that merit a little explanation.

Very simply, a social network depicts patterns of social interaction between entities (Tichy Tichy, Tushman, and Fombrun, 1979). These entities, also called "nodes," can be individuals, groups, and/or organizations. An identified connection between two nodes is called a "tie." A tie varies in its strength, direction, and content, all of which depend on many independent factors, such as reciprocity and intimacy within the relationship. The type of tie depicted in a social network map depends on a study's content and purpose (Knoke and Yang, 2008).

Our ASU study, led by former College of Public Programs Dean Debra Friedman, populated a social network map of Phoenix nonprofit organizations, including their relationship to ASU. Organizations were selected based on their proximity to the downtown Phoenix campus. Executive directors were contacted and asked to list the organization's five most important nonprofit partners within the last year; they were also asked to describe the nature of each partnership, such as resource sharing, along with the perceived importance of each partnership.

After receiving data from 157 organizations, ASU's nonprofit organization social network was created using UCINET software, which can be seen below. For confidentiality purposes, organizations were identified by their function, not by specific name.

There are several outcomes worth noting. At first glance, the network seems incomplete. There are many nodes (organizations) that are connected to ASU, but not each other. In the field of social network analysis, these are called "structural holes." These holes are actually advantageous for ASU, as ASU serves as the only organization that can potentially introduce and connect distant organizations to each other (Hanneman, 2005).

This process of facilitating new ties/relationships is called "brokering." The software computes "brokerage capacity," which calculates how many new ties (relationships) can be created by ASU in its network. ASU's brokerage capacity is 274, meaning ASU has the potential to create 274 new partnerships based on its current position and relationship to the responding nodes.

The majority of ASU's social network is maintained through weak ties; however, weak ties are essential to the dissemination of new information, formation of new partnerships, and connections to other social networks (Granvotter, 1979). Based on ASU's relatively new presence in the community, this is very encouraging information. With so many weak ties, ASU has the power to be a key informant, as well as receive diverse information from a multitude of nonprofit organizations.

How could a social network analysis help your organization? The possibilities are endless. For example, a donor network map could be created, allowing you to analyze relationships among donors and create strategies to increase interaction, strengthen existing relationships, and locate new prospects. A personnel map of the employees who are most sought-after for advice or assistance could be drafted to improve efficiency and camaraderie among co-workers. The data collection is simple, and the software is relatively inexpensive. For more information on social network theory and analysis software, see the references below.
 

Sources:
^ [1] Granovetter, M. (1973). The strength of weak ties. American Journal of Sociology. 78(6), 1360-1380. Retrieved from http://www.jstor.org/stable/2776392
^ [2] Hanneman, R. and Riddle, M. (2005). Introduction to social network methods. Retrieved from http://faculty.ucr.edu/~hanneman/nettext/
^ [3] Knoke, D. and Yang, S. (2008). Social network analysis: Second edition. Quantitative Applications in the Social Sciences. 154. Retrieved from http://www.sagepub.com/booksProdDesc.nav?prodId=Book228826
^ [4] Tichy, N. M., M. L. Tushman, and Fombrun, C. (1979). Social network analysis for organizations. Academy of Management Review. 4(4), 507-519. Retrieved from http://www.jstor.org/pss/257851
^ [5] UCINET. http://www.analytictech.com/ucinet/

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