by Simão Policarpo Nogueira
This website is an online version of the Dissertation submitted in partial fulfillment of requirements for the MSc in Business at the Universidade Católica Portuguesa, written under the supervision of Professor José Vasconcelos-Sousa.
The European startup and investment scene is quickly expanding: record levels of growth were identified in 2021, revealing this region lacks only in maturity of the ecosystem. Startup accelerators - the new generation of incubators - have picked up on this expansion and turned acceleration into a successful venture. As researchers continue to disagree on the conceptual and typological definitions of startup accelerators, their impact and ideal structure remain unclear. This dissertation aims to transpose a study conducted in the USA startup ecosystem to the European reality through intensive use of data and statistics. It looks into the relationships between the design variables of acceleration programs and the success - or lack thereof - of their graduated startups. Data was collected through a range of aggregators, news articles and websites and put through linear regression models in order to test the hypothesis that an accelerator’s structure impacts its startup’s chances of success. Our results reflect the positive effects of a large and diversified network of partners, sponsors, founders, and companies but struggled to explain the mechanisms behind startup success in full. Our data allowed us to distinguish between ill- and well-designed accelerators, establishing that the impact of the latter is stronger in both the short- and long-term, ultimately proposing the structure for an accelerator that maximises a startup’s chances of success.
This dissertation makes use of R and the R Markdown language. The entry point to the project is the file index.Rmd in the root of the repository. From there, all of the components of the statistical analysis, data, functions and content chapters are loaded.
The data used in this study has been anonymized and is made available upon request. There are two folders containing data: data-original, non-anonymized and whose access is restricted to the authors of the dissertation; and data-anonymized, for which you can request access. Access to the repository winning-data-anonymized (here used as the submodule data-anonymized) is required in order to replicate results.
Please reach the author at s-splnogueira@ucp.pt in order to request access to the data used in this study. You may be asked to provide details of the intended usage.
This project can be downloaded by cloning this git repository:
git clone git@github.com:simaonogueira101/winning-dissertation.git
or by downloading the zip file.
After cloning the repository, if you already have access to the data submodule(s), run the following command:
git submodule init
If you have access to the data-anonymized submodule, run:
git submodule update -f --remote data-anonymized
If you have access to the data-original submodule, run:
git submodule update -f --remote data-original
In order to run this code, you will require a local installation of R. It is recommended that you use RStudio.
Several packages were used to develop this project. In order to install all requirements, navigate to helpers/packages.R and uncomment all code and run the script. Afterwards, you may re-comment these lines to avoid re-installing the packages in the future.
Two external APIs were used. Google Cloud Platform and Crunchbase. If your goal is to simply replicate results, the only requirement is an API key from GCP with access to their Geocoding API and Maps Static API. The code expects an ENV_KEYS.R file with this key located in the helpers folder. An example file EXAMPLE_ENV_KEYS.R with the variable naming is available.
Reproduction of results can be achieved by opening the winning-dissertation.Rproj file and knitting the index.Rmd file in RStudio. Should this command be successful, the outputs will be available in the docs folder. The project outputs both PDF and HTML versions out of the box.
The format of data is R's .rds. You should be able to convert it to other formats (Excel, CSV, etc.) with simple scripts for analysis in other software.
For any questions, please reach the author at s-splnogueira@ucp.pt.
MIT License
Copyright (c) 2022 Simão Policarpo Nogueira
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.