Methodology
This chapter is a part of the book “How to Building Thriving Start-up Ecosystems: Five Patterns for Success.”
Every book has a story within it, as well as the story of how the words came to the page. This book started as research for my senior Masters thesis at the University of Michigan, where I was simultaneously getting a Masters in Urban Planning and Design and a Masters in Information Science.
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 research question was initially simple: understand how start-up founders’ success was impacted by time spent at entrepreneurial hubs at the University of Michigan. I defined “founder success” as those students whose companies obtained repeated revenue, won a business competition, or were accepted into an accelerator.
The first part of this research was to find the entrepreneurial hubs on campus. To find these hubs I interviewed twenty-five self-reported entrepreneurs over the course of a year to understand where they studied, took meetings, had classes, and socialized. Based on these interviews I plotted a heat map that shows clusters of entrepreneurial activity. Red areas show where at least five people reported that they had conducted some activity related to entrepreneurship. The yellow regions are where over ten people reported that they had done some form of business-related activity.
The findings showed 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.
The second part of this research on how founder interaction with these physical hubs impacted their progress was far more complex. The twenty-five founders that I interviewed I took from points at the beginning, middle, and end of the entrepreneurship pipeline.
Beginning founders I defined as individuals that had only a cursory involvement with the university’s entrepreneurship ecosystem. Perhaps they had attended a Hackathon, participated in a business school entrepreneurship class, and while they expressed an interest in building a company, their start-up was still primarily an idea.
Mid-stage founders I defined as those who had taken measurable strides to build their company, either by participating in the university’s start-up incubator program 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 had developed their companies to a point where they had obtained some marker of “success” within the ecosystem, defined as either acceptance to an accelerator, winning a business plan competition, 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, utilized by tens of thousands of readers, or acquired by larger companies.
My interviews showed some early stage founders who 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. While late-stage founders did have a strong presence at these hubs there was certainly not a 1:1 correlation between time spent at physical entrepreneurial hubs of activity and founder success.
Brookings’ research pointed to Elfring and Hulsink’s idea of strong and weak ties as the primary theory that described the missing link between physical proximity and success. Founders could not just be in the right place; they needed to have access to both strong and weak interpersonal relationships.
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 information networks 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 impacted founder success. The strength or weakness of ties seemed to be one facet in a larger picture of how founders build mental maps and navigate through an entrepreneurship ecosystem.
My research question shifted from understanding the impact of where founders clustered physically to how they internally developed mental maps of the university’s entrepreneurship ecosystem. This seemed to be the missing link between why some founders were able to utilize these actively hubs effectively and progress with their company while others did not.
Getting a clear picture of how founders developed their mental maps they had of the entrepreneurial ecosystem proved difficult. One reason was that most people do not consciously understand that they have mental maps. The ways 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 truly understood the entrepreneurship ecosystem.
Then I had to distinguish between what about each founder’s experience was unique to them individually and in what ways they organized information that could be applicable to founders more broadly. To do this I first needed the language to interpret these findings. Here I drew heavily from Kevin Lynch’s ideas from his book “The Image of a City” (Lynch). Lynch classified five different elements of how people develop mental maps from their surroundings. I took many of his ideas, such as paths, edges, and nodes, and looked at how these ideas could be applicable to phenomena that might not have a direct physical correlate. I also pulled from the field of information ecology, specifically the ideas of information patterns and fitness.
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. A larger dataset would increase the surety of the location of the university’s entrepreneurial hubs and the universality of the information patterns discovered.
I endeavored to interview a diverse set of interviewees at several different stages of company formation. 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 founders (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 is needed to understand how founders build mental maps at teaching colleges, community colleges, or small liberal arts colleges.
This research was also confined to how information patterns and networks impact entrepreneurial success. There are likely many relevant different factors that impact a founder’s success, from the strength of a founder’s initial idea to their economic background. This research could only focus on one facet of founder success due to limitations of time, resources, and expertise.
While this research has both biases and limitations, I hope it is accurate enough to prove useful and actionable.
