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.