Methodology
This chapter is a part of the book “How to Building Thriving Start-up Ecosystems: Five Patterns for Success.”
Attached to every story is the underlying tale of how the words came to the page. This book started as research for my Masters thesis at the University of Michigan. My initial research drew from the Brookings Institution’s work on Innovation Districts. Brookings saw Innovation Districts as “geographic areas where leading-edge anchor institutions and companies cluster and connect with start-ups, business incubators and accelerators” (Katz, Wagner). These physical spaces create outsized returns on productivity by spurring idea generation and accelerating commercialization.
My goal was to mirror the work Brookings had done for cities in an educational context. My research question was simple: understand how start-up founders’ success at the University of Michigan was impacted by how much time they spent at physical entrepreneurial hubs.
The first step to answer this research question was to find the location of entrepreneurial hubs on campus. To do this I interviewed twenty-five self-reported entrepreneurs over the course of a year to understand where they studied, took meetings, had classes, and worked on their businesses. Based on these interviews I plotted a heat map that showed clusters of entrepreneurial activity.
In the map below Red areas show where at least five people reported that they had conducted entrepreneurship activities. Yellow regions show where over ten people reported that they had done business-related activities.
The map shows three major hubs of entrepreneurial activity focused around the university’s School of Information, the Business School, and the School of Computer Science and Engineering.
Now that these hubs had been identified, the second part of this research proved far more complex. To uncover how access to these physical entrepreneurial hubs impacted founder success I interviewed twenty-five founders from points at the beginning, middle, and end of the entrepreneurship pipeline.
Beginning founders: Individuals that had only a cursory involvement with the university’s entrepreneurship ecosystem. Perhaps they had attended a Hackathon, or participated in a business school entrepreneurship class. While these people might have expressed an interest in building a company, their start-up at this stage was still primarily an idea.
Mid-stage founders: those who had taken measurable strides to build their company, either by participating in the university’s start-up accelerator program called TechArb, participation in a business competition, or incorporation of their company. Founders at this stage might have found a co-founder, developed wireframes or an initial Minimum Viable Product (MVP) to test their idea, but did not have continuous paying customers.
Late-stage entrepreneurs: these founders had developed their companies to a point where they had obtained some marker of “success” within the ecosystem, defined as either winning a business plan competition, obtaining investment, or demonstrating repeated revenue from customers. These founders had developed products to a level of finesse that they could be bought on grocery store shelves, be utilized by tens of thousands of readers, or acquired by larger companies.
My interviews showed some early stage founders spent several hours each week at entrepreneurial hubs but never progressed with their ideas. Some late-stage founders spent almost all of their time at one of the entrepreneurial hubs; other late-stage founders spent little time in these locations. While it was clear that there were hubs of entrepreneurial activity, how students interacted with these hubs and how this interaction impacted their success was not straightforward.
Brookings’ research pointed to Elfring and Hulsink’s idea of strong and weak ties as the primary theory that describes the mechanism between physical proximity and success. Founders needed to not just be in the right place to succeed; they needed to build both strong and weak interpersonal relationships within these spaces.
Strong ties occur between people or firms with high levels of trust, often from previously working together. These close ties can help founders with joint problem solving and shared technical information. Strong ties often are developed within similar fields, and the shared information often includes useful workshops, industry-specific conferences, and industry-specific blogs (Katz, Wagner).
Weak ties on the other hand occur between people or firms that have infrequent contact and work in different contexts or economic sectors. Weak ties provide access to new information, new contacts and business leads outside of existing networks (Katz, Wagner).
While the theory of weak and strong ties was one way to understand the information that passed between people in these spaces, my interviews showed this theory alone was an overly simplistic explanation of how physical space played into founder success.
This theory missed an understanding of both the evolution of how founders built their mental maps of the entrepreneurial ecosystems they inhabited and the highly structured nature of some of the effective or ineffective schemas that founders possessed. The strength or weakness of ties was one facet in a larger picture of how founders built mental maps and navigated through the entrepreneurship ecosystem.
My research shifted from looking at the impact of physical spaces on founder success to understanding how founders developed mental maps of the university’s entrepreneurship ecosystem. These mental maps seemed to be the missing link between why some founders progressed with their companies while others did not.
Getting a clear picture of how founders developed their mental maps proved difficult. This was because most people do not consciously understand that they have mental maps. How people find and organize information is often so fluid and natural that many are not aware that they do it. I ultimately used a mixture of founders’ answers in initial interviews combined with observation of their actions over time to see how they understood and navigated through the university’s entrepreneurship ecosystem.
Then I had to distinguish what about each founder’s experience was unique to them and in what ways they organized information that was applicable to founders more broadly. To do this I needed a language to interpret my findings. I developed many terms myself from the disciplines of urban design and information ecology. From urban theorist Kevin Lynch’s book “The Image of a City” he classified five different elements of how people developed mental maps from cities, such as paths, edges, and nodes, landmarks, and boundaries. Together with the idea of legibility these terms became the foundation for information patterns such as Connected Nodes and Potential Achievable Action Maps. I also pulled from the field of information ecology, specifically the ideas of structural clutter and efficiency.
Biases, Limitations, and Further Exploration
This research has biases and limitations that might impact the generalizability of its findings. The sample size of the structured founder interviews was only twenty-five students and eight other “entrepreneurial leaders” within the university ecosystem that included business and information science school professors, leaders of local accelerators, and the heads of different entrepreneurship organizations at the university. A larger dataset would increase the surety of the universality of the information patterns uncovered.
I endeavored to interview a diverse set of founders at several different stages of company development. However, as students progressed through the entrepreneurship pipeline, the demographics increasingly leaned toward white males with technical backgrounds. While there are likely racial, gender, and economic elements as to how entrepreneurs experience the University of Michigan’s entrepreneurship ecosystem, this book does not have the capacity to give such considerations the attention they deserve.
My interviews were also conducted at the University of Michigan, which has consistently ranked for the last decade in the top ten universities worldwide for entrepreneurship (Trevor). The main challenge for students at Michigan was how to navigate through a wealth of resources rather than to access any resources at all. To mitigate this bias, I conducted several unstructured interviews with students from Wayne State and Stony Brook University. However, further exploration should be done to understand how founders build mental maps differently at community colleges and small liberal arts colleges.
This research was also confined to how information structures impact entrepreneurial success. There are many relevant different factors that impact a founder’s success, from the strength of a founder’s initial idea, to their economic background, and their unique skills. This research could only focus on one element of founder success due to limitations of time and expertise. While this research has both biases and limitations, I hope it is accurate enough to prove useful and actionable.
