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.