Resource Limitations
Resource constraints. Despite makers’ social, knowledge, and
technical resources (Browder et al., 2019), their enthusiastic efforts
to harness these resources soon confronted immediate challenges
regarding materials, equipment, processes, expertise, and staffing.
First, many informants identified material shortages as a
significant obstacle. Makers looked to new and existing suppliers for
large quantities of materials such as elastic or plastic just as major
disruptions became apparent in global supply chains. They found
themselves competing with producers around the globe for the limited
supplies available.
Second, makers faced equipment constraints as they attempted
manufacturing with tools intended for prototyping or small-scale
fabrication. People working with personal sewing machines to sew cloth
masks, for example, were limited in terms of both speed and scale. A
maker in Triport experienced similar challenges with 3D printers, “I
learned about how to produce mass quantities of things on a machine
built for prototyping.” A few informants reported being able to
“retool” or change equipment as needed, such as the use of die cutters
or injection molding. However, limited access to such specialized
equipment meant that most makers were left to maximize their output with
the available tools (Baker & Nelson, 2005).
Third, makers encountered process constraints as they developed
new production and delivery systems, ramped them up for scale, and
navigated the social distancing, business shutdowns, and stay-at-home
restrictions unique to the pandemic. These process constraints had
logistical and physical implications as people were forced to organize
many activities without adequate facilities in terms of size and
sterilization for dealing with medical grade purposes. Further, for
people accustomed to the proximate collaboration and spontaneous
creativity they enjoyed in maker spaces (Furnari, 2014), the shift to
virtual interaction and working in isolation violated their expectations
concerning sociability in making.
Fourth, most makers lacked knowledge and expertise about
servicing clinical settings where PPE was needed and lacked experience
in establishing supply chain operations from scratch. The leader of one
organization in Edgeville described getting involved in developing
demand generation and fulfillment functions because it was needed, not
because they had experience doing it, “Folks were struggling and
figuring out what the heck was in a supply chain and buying blindly.”
One leader of a well-connected Midburg maker space expressed frustration
over a lack of information and competence for making things at an
industrial scale, “There’s a big difference between how you make things
at a small scale, like prototyping and flexible manufacturing, and
how… things that we use are genuinely made… I’m interested
in it, and I still find it hard to understand.”
Fifth, staffing challenges arose as makers self-organized their
human resources. Even in Midburg and Edgeville, where some organizations
chose to sell PPE, most of the activities were performed by volunteers.
Although often motivated by altruism or a sense of civic duty, the
volunteer amateur labor pool was generally insufficient for the demand
and poorly matched to the specialized circumstances (Barker & Gump,
1964). Such staffing problems are associated with low productivity and,
when performed by undercompensated volunteers, often lead to burnout and
pressure to take on excessive responsibilities (Oliver, 1984).
Coordination. Different network formation choices affected the
extent of coordination within and between clusters in a network. First,
makers within organizations or clusters of organizations typically made
decisions early on about how to fund PPE activities. As indicated by our
initial research design, Midburg and Edgeville included organizations
that chose to sell PPE, typically selling it at cost to break even and
minimize losses. A few organizations, such as an institution-based maker
space in Triport, relied on philanthropy to provide needed funding.
Perhaps the most popular choice, however, was to coordinate crowdfunding
campaigns through websites such as GoFundMe and to promote them via
social media.
These funding decisions indicate how different groups formed clusters
based on their willingness to pool resources. In Stilton, for example,
the cluster that formed around four maker spaces relied on the one
organization that was already set up as an independent non-profit to
lead a crowdfunding campaign. In this case, this choice to coordinate
efforts proved to provide financial “runway” during the network
evolution stage. In Edgeville, the decision to sell PPE required
coordinated efforts to educate and incentivize volunteers about the
rationale for the decision, which primarily pertained to creating a
self-sustaining initiative. For others, such as an independent maker
space in Midburg, the decision to sell PPE at cost created complications
that would persist into the network evolution stage, leading one
informant to regret not attempting crowdfunding at the outset.
Second, coordination levels between the clusters in our cases indicate
the limits to trust and cooperation in emergent networks (Burt et al.,
2021). As emergent groups (Stallings & Quarantelli, 1985), multiple
clusters in the same city often began describing themselves as
“networks.” Because we collected data on the very early phases of
network emergence for each case, we captured the dilemmas groups faced
in deciding whether to coordinate with each other and the extent to
which their activities would be open or closed to outside groups. The
cooperative coalition between three organizations in Edgeville
contrasted with the extensive independent and parallel efforts observed
in Midburg and Stilton. For example, a volunteer leader of an
independent maker space in Midburg described how other PPE maker groups
with university affiliations were “pushing” a different philosophy
regarding the disposability or reusability of PPE. Thus, divergent
understandings of needs and priorities during crisis onset influenced
decisions about whether to coordinate with specific other nodes.
Limited coordination between clusters in a network also surfaced in the
different types of sub-communities we found actively forming networks in
each city. Not only do these provide another indication of parallel
efforts, but they also illustrate clustering around organizational
fields, such as industries, professional groups, or craft specialties.
In multiple cases, we found clusters around not only maker spaces, but
also around sewing communities, engineering groups, entrepreneurial
support organizations, the creative arts sector, as well as the clinical
community. As people scrambled to self-organize entirely new PPE
activities, their coordination still favored homophily regarding
skillsets, projects, and temporary organizing.