Connecting to Change the World: How to take a network's pulse
Assessing the performance of social-impact networks, such as the Urban Sustainability Directors Network, is emerging as a new science.
This is adapted from 'Connecting to Change the World: Harnessing the Power of Networks for Social Impact' by Peter Plastrik, Madeleine Taylor, and John Cleveland.
Evaluation in the social-change world had focused almost exclusively on the effort of an individual organization, not on collaborations of individuals or organizations.
In a network, performance is evaluated for the same reasons that an organization’s performance is evaluated: to measure impact and ensure accountability for use of resources, to plan and improve network processes and development, to generate knowledge about what works and doesn’t work.
Unlike an organization, a network is a decentralized, member-driven platform of relationships that evolves its capabilities and underlying structure of connectivity. Its success depends crucially on the degree to which it organizes connections among its members to produce unique, flexible capacity and network effects.
To assess a network, you have to examine closely its members’ multiple value propositions and web of relationships, their highly diffused decision-making processes, and the stage of the network’s evolution (connection, alignment, or production).
Due to these differences, when network builders come to the question of evaluation, whether for their own needs or those of a funder’s staff or board of directors, they are likely to want to know about things that an organization-centric evaluation might not consider important.
This was the case at Urban Sustainability Directors Network. “From day one,” says Parzen. As she and other co-founders of the network’ considered how to conduct continuous self-assessment, “We built a story about USDN around the idea of creating something based on the relationships.”
Framework for evaluating networks
Our framework for network assessment contains three major topics: connectivity, health, and impact.
It shouldn’t be a surprise that we emphasize connectivity among a network’s members. After all, if members are not connecting with each other, then no value can be created by the network. The connections made in a network result in connectivity structures that can be mapped, measured, and analyzed, with important implications for the network’s evolution.
Network health is about the other essentials for developing a network—members’ satisfaction and sense of shared purpose; the effectiveness of network infrastructure, including governance and communications; the network’s resources. In short, how is the network doing in creating the conditions crucial for its success and sustainability?
A network’s impact has two dimensions: the impact that individual members of the network have on their separate worlds as a result of participating in the network and the impact network members have collectively. Either way, the basic question is the same: how much is the network changing the world?
With these three topics framed, there’s also the challenge of figuring out how they interact. Network health depends to some extent on connectivity, and network impact depends to some extent on network health. And a network’s impact can affect both its health and its connectivity.
An advantage of this framework is that it can work for both an insider’s self-assessment and an outsider’s third-party evaluation. In fact, the evaluation of Reboot that the Jim Joseph Foundation paid for was also used by Rebooters to help guide the network’s development, and the continuous assessments that USDN conducts have been used by some of its funders to gauge the network’s progress.
Framing the assessment inquiry and its key questions is just a first step. For each topic in the framework there are challenges in data collection and analysis, and some tools to help network builders along.
From the beginning of USDN’s formation, the network developed maps and analyses of its members’ connections with each other. Parzen got the idea from consultants who had worked with the Barr Foundation, which required the networks it was funding to do mapping to develop strategies and benchmark connections.
“We decided that mapping was a way we could demonstrate the network’s performance in the early years,” she says. “We found that once we could show change in connectivity from year to year in those maps, it was very potent. It wasn’t just an evaluation tool; it was a planning tool for the network.” Before the network’s first meeting, members answered a survey about their connections to other members, and every year since then they have done the same.
By 2013, the network had amassed a large amount of longitudinal and recent data about member connections. Parzen reported in that year’s “State of the Network” that highly efficient connectivity had been achieved — the average member either knew every member or knew someone who knew every member — and the quality of the connections among members was “getting deeper” — while the average member’s number of stronger ties to other members had more than doubled in two years to 10.
Mapping reveals the structure of a network’s connectivity, and a method called “social network analysis” can analyze the efficiency of the connectivity. As the Barr Foundation invested in increasing the connections among organizations that provided after-school programs for students, it also measured the degree to which their connectivity was changing. Consultant Stephanie Lowell reports that data were collected from some 1,000 organizations in sports and arts programming, including their links to each other.
“Efficiency refers to the average number of steps it takes for any one node to reach another node in the network,” she explains. “A rule of thumb in the network research field is to strive for efficiency at or near three.” After Barr-supported weavers had been active connecting many of these organizations, the efficiency of connections among the sports organizations improved to 3.8 steps on average (from 4.6 steps) and that of the arts organizations improved to 3.2 average steps (from 6.0 steps).
As fascinating as network maps are, there’s a lot to know about why and when to invest in creating them.
Network maps, which are created with special software, present complex information in a way that makes it easier to “see” connections and their patterns. Who is connected to whom? Who has more connectivity or reach than others in the network? Which nodes are strongly connected and which are weakly connected? Which are becoming more connected, which are losing connectivity?
Network mapping creates value for network builders in three ways:
1. Maps reveal opportunities to build connections that can maximize the power and potential of your network.
2. Maps show how a network’s structure is evolving—and this can be used to assess the health of a network.
3. Network maps make members better networkers.
A simple network map with a small number of nodes can be drawn by hand. But the analysis and display of more complex network information are best achieved with special software that sorts, measures, and organizes data for easier interpretation. Although most of these software programs, having been developed for limited distribution by mathematicians, sociologists, or graph theorists, are difficult for the average user, the technical assistance and mapping tools themselves are increasingly being adapted to serve new lay markets.
Examples of low- or no-cost tools include NodeXL, a free, relatively easy-to-use software application that works within Microsoft Excel; Gephi, a more recent entrant into the field of no-cost network visualization tools; and InFlow, which can be purchased through orgnet.com and comes with coaching and support from its creator, Valdis Krebs. (One source for more information about mapping software is Patti Anklam’s website, listed in the Resources section.)
Copyright © 2014 Peter Plastrik. Reproduced by permission of Island Press, Washington, D.C.