OSE in Africa Conceptual Model

The Observatory of Support for Entrepreneurship (OSE) in Africa Conceptual Model is based on scientific research published in peer-reviewed journals as well as popular publications such as Harvard Business Review France® by the authors of this report and is based on the capture of big data on the internet using automated scraping techniques. 

To that end, we will embark on an initial phase of identifying and qualifying key players in the targeted entrepreneurial ecosystem, using a variety of reference sources such as bilateral cooperation organizations, patronal conferences, professional associations, institutions, researchers, and universities.  We were able to identify almost 1500 entrepreneurs in 19 African countries, making this the most comprehensive and exhaustive repository to date.

To enable our Observatory to provide its full value, we qualified the entire database of over 1500 actors by specifying the kind and role of each actor, using a consistent model of distribution of these types and roles. The following types of actors are listed: Schools, Universities, Training Centers, Incubators, Accelerators, Social Economy Actor, Co-Working Spaces, Startup Nurseries, Third Places, FabLabs, Cultural Space for Startups, Media with an Entrepreneurial Focus, Startup Event, Clusters, Competitivity poles, Business Angel Clubs, Venture Capital, National Institutions, International Institutions, Local Institutions, Banks.

Following the hexagonal model, each of these categories of actors has been assigned a key role: schools and universities, incubators, accelerators, practice support, private equity investors (VCs and business angels), and government actors.

As presented below, OSE in Africa present a cutting edge complementary tool to leverage efficient strategy to stimulate Entrepreneurship in Africa.

GEM reports

OSE reports

Economic Datas
Organizational Datas
Managerial Datas
Systematic Annual Datas
Scientific Validity
Useful for understanding economic and managerial factors to guide long-term public policies Useful for understanding ecosystem organization and value chains, and implementing short & medium term strategies and tactics.

Method of Research

The research methodology used by the Observatory of Support for Entrepreneurship (OSE) in Africa is based on both network theory and natural language processing (NLP). This method is novel; it had never been scientifically applied to entrepreneurial ecosystems prior to the authors of this study publishing in peer-reviewed journals in 2021. Furthermore, she has never been deployed in Africa in such a long-term and systematic manner as is the case here. Finally, no other research institute has conducted longitudinal and systematic studies in Africa, making the Observatoire des Sudiens à l’Entrepreneurship the world’s leading authority on entrepreneurial ecosystems in Africa. 

The breadth and depth of our observations are unparalleled in the world, and the application of our method to these ecosystems creates the most accessible and mobilizable data set for analyzing the factors influencing entrepreneurship in Africa, as well as the most consistent set of recommendations in the world. In this section, we will go over the many components of these methods in order to make the results presented in this study more understandable.

Based on our observations of their activity, we identified the following elements to qualify the base: 

  1. Structure : public, private, associative, and public-private partnerships.
  2. Intervention Stage : Ideation, Conception, Debt financing, Scaling, Expansion.
  3. Demanded Contreparties: Fees (service), Shares, logo or label display, refund (loan).
  4. Supporting Topics: Generalist, FinTech, BioTech, EdTech, CleanTech, HealthTech, GreenTech, InsurTech, AdTech, RetailTech, AgriTech.
  5. Scale : International, National, Local Mono Site, Multi Site.
  6. Support function: Fundraising assistance (FundBoost), innovation assistance (InnovBoost), marketing strategy assistance (MarketBoost), business plan assistance (BuizPlanBoost), team formation assistance (HumanBoost), legal assistance (LawBoost), prototype assistance (ProtoBoost), large-scale testing assistance (TestoBoost), and commercialization assistance (SalesBoost).
  7. Implantation city : Abia, Abuja, Accra, Agadir, Akwa Ibom, Alger, Amersfoort, Baam, Bauchi, Bayelsa, Beni Mellal, Berlin, Bethesda, Borno, Bryanston, CapeTown, Casablanca, Delta, Driebergen-Rijsenburg, Dubai, Ekiti, El Jadida, Enugu, Essaouira, Fès, Frankfurt, Ifrane, Ikeja, Johannesburg, Kaduna, Kano, Kénitra, Kumasi, Kwara, Lagos, Londres, Luxembourg, Marrakech, Marseille, Meknès, Nairobi, Nassarawa, New York City, Ogun, Ogundana, Ondo, Osun, Ouagadougou, Oujda, Oyo, Paris, Plateau, Pretoria, Rabat, Rivers, Rome, Seattle, Settat, Tamale, Tanger, Temara, Thiais, Dakar, Mwanza, Arusha, Dar es Salaam, Tunis, Sfax, Sousse, Ariana, Douala, Sandton, Athlone, Cobham, Mohammadia, Bobo Dioulasso, Abidjan, kampala, Masindi, Mukono, Kigali, Lomé, Kpalimè, Yaoundé, Buea, Stellenbosch, Durban, Sandton, Volta, Berekuso, Cotonou, Kempton, Fransville, Arabie, Tshawne, Alton, Oslo, Nelspruit, Mpumalanga, Port Elizabeth, Midrand, Bohicon, Parakou, Abomey-Calavi, Lyon.
  8. Contacts Function: CEO, Program Manager, Associate, Researcher, Assistant, Project Manager, General Manager, Vice President, Logistics and External Relations Executive Director, General Manager, President, Technical Director, Co-Founder and Advisor, Advisor, Co-Founder, Marketing Director, Director, Regional Representative Manager, Administrative and Financial Director, COO, HR Director, Partnership Manager.

The sum of this data constitutes the most comprehensive and complete data set for Africa. We then developed a specific software to collect all feasible information and observations for each country, which we then deduplicated and recorded. Using name co-occurrence approaches, we identified all forms of collaboration and partnership among the actors in these ecosystems, using a technique known as association dyadic identification. These data are graph matrices that can be used to identify networks. We can perform network studies that are powerful and significant when we use the other qualification data mentioned previously.

Furthermore, the data we’ve gathered allows us to use incredibly sophisticated « Natural Language Processing » tools, as well as « topic modeling. » The application of topic modeling to the Moroccan entrepreneurial ecosystem from 2008 to 2013 provides a number of benefits and significant insights for a comprehensive and nuanced knowledge of this time period. Here are some of the reasons why this method is useful:

  1. Emerging Themes Identification: Topic modeling automatically extracts the important themes covered in textual materials such as press stories, reports, blogs, and so on. This would aid in identifying emergent themes that have piqued the interest of Morocco’s entrepreneurial ecosystem throughout this time period.
  2. Trend analysis: Topic analysis can identify patterns that have influenced the entrepreneurial environment, such as rising company sectors, unique issues faced by entrepreneurs, or policy talks.
  3. Major Event Impact: Topic modeling can assist in understanding how important events such as the global financial crisis, governmental reforms, or other economic developments have affected discussions and concerns within the ecosystem.
  4. Actor perception: Topic analysis can illustrate how various ecosystem participants (entrepreneurs, investors, institutions, media, and so on) have viewed and reacted to changes and opportunities over time.
  5. Evolving Priorities: You may analyze changes in ecosystem priorities and examine how they have influenced entrepreneurs’ decisions and activities by examining how subjects have developed from year to year.
  6. Resource targeting: Topic modeling can help you more effectively target resources, support programs, and policies based on the real needs highlighted in ecosystem talks.
  7. Inspiration for Research: The outcomes of topic modeling can be used as a starting point for additional research into specific themes that emerge, leading to new ideas, discoveries, and analyses.
  8. Impact evaluation: By identifying the most discussed subjects and analyzing their evolution, you may determine the true impact of specific projects, changes, or events on the entrepreneurial environment.
  9. Recognition of Actors: Topic analysis can help to understand how various actors have contributed to conversations and projects within the ecosystem, providing a more nuanced view of their roles and contributions.

The combination of these data allows us to implement our general conceptual model, which is depicted below. Organizations such as the « Global Entrepreneurship Monitor » can conduct a « Macro-Level » analysis of the 10 economic components of entrepreneurial ecosystems. This very relevant analysis has the disadvantage of causing public policies that are costly and slow to implement, as well as slow to have meaningful results; we’re talking about a time scale of 15 to 20 years.

The Observatory of Support for Entrepreneurship (OSE) in Africa Model focuses on a « Meso-Level » analysis that allows for a configurational and processual examination of the organizational interdependence of entrepreneurs’ actors.

A complementary Economic Intelligence tool

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Call to Scientific Contribution

Scholars from all the world, and particular in Africa, are welcome to propose contributions to build OSE's National Reports, and also to build top level Research Papers.

Your are a Scholar ? You have knowledge in African Entrepreneurship and Entrepreneurial Ecosystems ? You have strong skills in systematic data collection using R Studio and Python ? Your have advanced skills in Data Science, Natural Language Processing or Topic Modeling ? Your are welcome, please contact us !

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