Below Daniel Evans & Louis Boguchwal, researchers from the Network Science Center at West Point (US), explain how they map networks of entrepreneurs across Africa to develop recommendations towards entrepreneurial success. Also see the previous post in this series, discussing the importance of facilitating entrepreneurial success in Africa and other developing regions.
We all live and work in a world made up of complex adaptive systems,each of which can be represented by various forms of networks. Our team has developed an innovative, yet simple, technique that allows us to develop quantifiable entrepreneur networks. This technique, known as the ‘Position Generator’, models the connections of roles in the local community. By analyzing the entrepreneur’s connections to prominent structural positions in the community or society, our team is able to develop powerful insights that inform policy recommendations.
Our model is a quantitatively derived network that enables us to accurately assess the local entrepreneurial ecosystem. This methodology identifies the most influential roles in the ecosystem, and allows us to compare and contrast different local communities.
The Position Generator Technique develops models of relationships between people and roles in the local environment.
The model below depicts the entrepreneurial network developed from data our team collected during visits with entrepreneurs operating in the Technology Sector in Kampala, Uganda. Even before a rigorous network analysis, an initial visual inspection yields some interesting insights:
- – The Military and Religion Roles have no impact on this ecosystem.
- – The Self, Social Network, and Professional Roles have a close relationship and are influential to the network.
- – There are two distinct sub-groups in the network.
- – Roles such as Commercial Banks, Government Business Development Programs, and Business Incubators are not as influential as one might expect.
Kampala Entrepreneurial Network Model. Developed after a data collection visit to Uganda.Nodes depicted in the network model are roles, or positions, in the local entrepreneurial ecosystem, and the links illustrate how roles are connected through individuals’ perceptions of where to find required resources. Each node is sized according to its influence, and is colored by a grouping algorithm. Each group shares specific characteristics.
As our project continues, our team will develop a “goal network” model. The constituent data will be collected from an environment that is considered to be especially conducive for successful SME establishment.
We will construct this network using the same methodology, and then determine which nodes in the “initial entrepreneurial network” are the “driver nodes.” By influencing these nodes, or their links to other nodes, we can encourage the “initial entrepreneurial network” to evolve towards the “goal network,” possessing similar mathematical characteristics. The resulting analysis will inform the development of specific policy recommendations for specific communities.
For example, the diagram below illustrates two simplified networks. Our goal is to influence the initial one on the left to evolve towards the one on the right. In this example, we can make three recommendations:
- Increase the influence of the center node in the network. This could be enacted through additional funding, resources, or oversight.
- Develop or establish relationships between the three outermost nodes on the left side of the network model. For instance, this might mean facilitating a relationship between commercial banks and business incubators where none currently exists.
- Eliminate the node in the lower right corner of the “initial entrepreneurial network” because this node adds no value. In practice, this could be a role that is actually detrimental to the entrepreneurial ecosystem. For example, perhaps a corrupt central government is involved in approving the funding of entrepreneurial efforts.
“Influencing” a network: Demonstration of network evolution to inform policy recommendations.
This work marks just the beginning of the Network Science Center’s frontier market initiatives. In 2014, we aim to expand our technical capabilities to include dynamic models and comparative analytics. Over the past months we have visited various cities across Africa. Our data collection trips allow us to track changes in the economic landscape and compare communities to one another. Thus, we can build enriched models that account for temporal changes in entrepreneur networks, which will provide deeper insight into how the local economy functions and the role of social capital in entrepreneurial success. Additionally, more powerful comparative methods will drive highly customized country-specific analysis. These detailed metrics can reveal that entrepreneur-economic policies are not “one size fits all.”
In our next blog posts we will discuss insights from data collection trips to entrepreneur ecosystems in various cities across Africa.