Rachel Aliana
7 min readJan 25, 2024


Generated by DALL-E.

This chapter is a part of the book “Information Patterns for Successful Start-up Ecosystems.”

Before every book there is 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 getting a Masters in Urban Planning and Design and a Masters in Information Science simultaneously. My initial research was inspired by 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” that create outsized returns on productivity by spurring idea generation and accelerating commercialization (Katz, Wagner).

My research question was initially to understand how start-up founder success was impacted by time spent at entrepreneurial hubs at the University of Michigan. “Founder success” was defined for the purposes of this research 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 of business-related activity I interviewed twenty-five self-reported entrepreneurs over the course of a year to understand where they studied, took meetings, had classes, and socialized.

Below is a heat map that displays the clusters of entrepreneurial activity uncovered from this research. 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.

Map of entrepreneurial hubs on the University of Michigan campus. Generated by ArcGIS, 2017.

The findings showed three major hubs of 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 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 were defined as individuals that wanted to create a company but had little involvement with the university’s entrepreneurship ecosystem. Perhaps they had attended a Hackathon, or participated in a business school entrepreneurship class, but for most at this stage their company was an idea.

Mid-stage founders were 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.

Late-stage entrepreneurs have developed their companies to a point where they have obtained some form of “success” defined as either acceptance to an accelerator, winning a business plan competition, or demonstrating repeated revenue. 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. There was certainly not a 1:1 correlation between entrepreneurial hubs and founder success.

Brookings’ research pointed to Elfring and Hulsink’s idea of strong and weak ties as the primary theory that describes how a layer of spatial social networks impact founder success alongside physical proximity. 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 played into founder success. What it missed was an understanding of evolution and structure. Evolution in terms of how founders slowly built their understanding of the entrepreneurship ecosystem. Structure in terms of how founders gathered, organized, and understood information in either effective or ineffective schemas. 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 developed mental maps internally within the university 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 were unable to.

Understanding this picture of how founders developed mental maps of the entrepreneurial ecosystem proved difficult for a variety of reasons. One 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 even do it. I had to use a mixture of founders’ answers combined with tracking their actions over time to see how they truly understood the entrepreneurship ecosystem, as often their answers did not align with their actions.

Secondly, to gather overarching insights I had to distinguish between what about each founder’s experience was unique to their personal mental map and in what ways they organized information that could be extracted into information patterns applicable to founders more broadly. A mental map is a highly personalized internal representation of information, unique to each individual (Downs). An information pattern on the other hand is how information is structured, organized, and interpreted by people generally (Pirolli). To make this research applicable to founders across a wide array of different ecosystems, I sought specifically for insights that could lead to generalizable information patterns.

Thirdly, I needed a language to interpret this interview data that lay outside of information science. 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 they were applicable for specifically entrepreneurial mental maps. I then expanded on his list to include such elements as foundational selection, layers, and effective schemas to describe the phenomena I found. I also needed to rely on many ideas within ecology, as the idea of an ecosystem fit better to explain the importance of entrepreneurial productivity and the interlocking parts at play within the university than terminology from information science alone.

The structure of this research combines both quantitive research to plot physical entrepreneurial hubs, combined with more qualitative interviews to understand the actions and thinking of founders within these spaces. The initial twenty five interviews were structured, but were then followed by more unstructured interviews and longitudinal observation as I immersed myself into the university’s entrepreneurial ecosystem as a participant observer.

Biases, Limitations, and Further Exploration

This research has several biases and limitations that might impact the validity, reliability, and 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 veracity of the information patterns collected.

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.

As a white woman, there are likely many aspects of the entrepreneurial experience that I have not fully grasped and can never experience. As well as factors such as race and gender that impact the values and assumptions of this research’s design, data collection, and interpretation, more subtle biases such as my non-technical background at the time might have had an impact on interpretations of the data collected and what I found important to write about.

Most of my interviews were also conducted at the University of Michigan, which has a robust entrepreneurial ecosystem. The main challenge for students here was 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, but the majority of the research focused on students at the University of Michigan. Further exploration is needed to understand how founders build mental maps at teaching colleges, community colleges, or small liberal arts colleges to understand how various different ecosystems give rise to different patterns of interaction.

This research was also confined to how information patterns and networks impact entrepreneurial success within the university ecosystem. 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. Even though this research only focused on one lens, even here there are likely several more elements of founders’ mental maps that have yet to be uncovered. Further exploration to reveal other information patterns could help build even better entrepreneurship ecosystems and cities as a whole.

While this research has both biases and limitations, I hope it is accurate enough to prove useful and actionable.