Network Emergence Stage
In all four cases, multiple actors and organizations sprung to action during March 2020. Individuals learned of voids (Drabek & McEntire, 2003) and began sharing ideas about how makers could help – often through social media – without waiting for requests from local institutions, such as hospital administrators and city government officials (Stallings & Quarantelli, 1985). Within a week, however, a major hospital in Midburg acknowledged its desperate need for supplies, specifically calling on people and organizations with access to 3D printers. Large Zoom meetings quickly attracted individual makers as well as people representing prestigious universities and leaders of small businesses, maker spaces, and community organizations across the city. In these instances, many curious and well-intentioned actors sought to identify potential connections, learn what was needed, learn what others intended to do, and inform their own decisions about how to get involved. This illustrates multiple classes of nodes (Casciaro, 2020) coalescing around potential linkages in the early phases of organizing for temporary action (H. E. Aldrich & Whetten, 1981).
Network formation. As the networks in our four cases emerged, they did not develop randomly; rather, they exhibited characteristics of small world networks with varying degrees of clusters and inter-connections (H. E. Aldrich & Kim, 2007). Still, our data indicates people were not only operating within pre-existing homophilic relationships. There was movement within networks as people formed new clusters and expanded or re-configured existing ones. In Midburg, for example, interview results increased our list of local organizations involved with PPE by 33%. Informants in Midburg reported collaborating with an average of 6.1 new organizations because of their PPE efforts. As networks formed, we found varying numbers of institutional ties and differing approaches to organizing activities and resources across the cases.
In Midburg, our entry point to the field was a single maker space that centralized its PPE production and distribution activities at its facility. Yet, we uncovered multiple other local clusters – including university and hospital led PPE production groups – that were aware of each other and communicated initially, but ultimately organized their activities separately. Similarly, in Stilton we identified a cluster of three independent maker spaces and one university maker space working separately from several other clusters in the city. Activities in Edgeville, however, were more tightly clustered with connections to one or more of the three main nodes, indicating a potentially higher degree of power and coordination of activities spread across the network. These nodes were a three-way partnership between established organizations in the community, including an independent maker space that played the central organizing role. These organizations acted as hubs for PPE production activities that were broadly distributed among more than 350 individual parts contributors, such as people with 3D printers, local schools, small businesses, and other arts and maker organizations.
In Triport, makers with institutional ties helped form the two main clusters we found. A small cluster formed with direct ties to the city’s entrepreneurial ecosystem for technology startups. The second cluster is unique in our sample because it began with strong university administration support in terms of fundraising, use of equipment, and permission for employee participation. This cluster developed into a broadly distributed set of nodes connecting more than 200 people, but with a greater degree of decentralization and redundancy of activities to “balance the load.” One of the primary organizers characterized it as “an illicit drug network that operated on trust” and in which people “were behaving in a very mob-like manner… because it was all based on relationships.” An implication of this for our study is that, when we observed network connections, we captured the strength of participants’ social ties (i.e., frequency of interaction before and during COVID-19) rather than just transactional ties between organizations.