Measuring the Social and Economic Impact of Universities' Entrepreneurial Activity: Introducing the BR-AFC Algorithm to Sort Alumni-Founded Companies
Autor(a) principal: | |
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Data de Publicação: | 2024 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Brazilian Journal of Operations & Production Management (Online) |
Texto Completo: | https://bjopm.org.br/bjopm/article/view/1808 |
Resumo: | Goal: This study introduces an algorithm to sort alumni-founded companies from the public Brazilian Internal Revenue Service (IRS) database. Design/Methodology/Approach: Departing from IRS data and student data from the university, sequential filters are applied to arrive at a final list of alumni-founded companies. Results: The main result of this study is the establishment of the algorithm itself, which emerged after cycles of iterations of analysis and rewriting. To test its reliability, a sample of 1625 alumni was used. The algorithm successfully identified 140 founders of 159 AFC. Founders were heterogeneously distributed throughout the decades analyzed. Companies belonged to different industry sectors and were classified according to their technological intensity, with predominance of middle-low and low intensity. Research limitations/implications: Although the BR-AFC algorithm is applicable to any Brazilian institution, generalization to other countries depends on access to country-specific databases containing data about companies and its partners. Additionally, the final result depends on the reliability of input data and of user decisions about the rigor of its premises. Practical implications: The BR-AFC algorithm can improve measurements of the socioeconomic impact of educational institutions. It points to the formation of entrepreneurs and, as a consequence, institutions can evaluate courses and educational programs and improve curricula. Policymakers and sponsoring institutions can measure return over investment, outcomes of policies to encourage entrepreneurship and rank universities according to novel criteria. Originality/Value: The main contribution to the literature is exploring novel approaches to measuring university-industry relationship. More specifically, it proposes an algorithm to identify the alumni-founded companies of a given university from large country-based databases. |
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ABEPRO |
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Brazilian Journal of Operations & Production Management (Online) |
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Measuring the Social and Economic Impact of Universities' Entrepreneurial Activity: Introducing the BR-AFC Algorithm to Sort Alumni-Founded Companiesalumni-founded companiesEntrepreneurshipUniversity Industry RelationshipInternal Revenue ServiceGoal: This study introduces an algorithm to sort alumni-founded companies from the public Brazilian Internal Revenue Service (IRS) database. Design/Methodology/Approach: Departing from IRS data and student data from the university, sequential filters are applied to arrive at a final list of alumni-founded companies. Results: The main result of this study is the establishment of the algorithm itself, which emerged after cycles of iterations of analysis and rewriting. To test its reliability, a sample of 1625 alumni was used. The algorithm successfully identified 140 founders of 159 AFC. Founders were heterogeneously distributed throughout the decades analyzed. Companies belonged to different industry sectors and were classified according to their technological intensity, with predominance of middle-low and low intensity. Research limitations/implications: Although the BR-AFC algorithm is applicable to any Brazilian institution, generalization to other countries depends on access to country-specific databases containing data about companies and its partners. Additionally, the final result depends on the reliability of input data and of user decisions about the rigor of its premises. Practical implications: The BR-AFC algorithm can improve measurements of the socioeconomic impact of educational institutions. It points to the formation of entrepreneurs and, as a consequence, institutions can evaluate courses and educational programs and improve curricula. Policymakers and sponsoring institutions can measure return over investment, outcomes of policies to encourage entrepreneurship and rank universities according to novel criteria. Originality/Value: The main contribution to the literature is exploring novel approaches to measuring university-industry relationship. More specifically, it proposes an algorithm to identify the alumni-founded companies of a given university from large country-based databases.Brazilian Association for Industrial Engineering and Operations Management (ABEPRO)2024-02-10info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionResearch paperapplication/pdfhttps://bjopm.org.br/bjopm/article/view/180810.14488/BJOPM.1808.2024Brazilian Journal of Operations & Production Management; Vol. 21 No. 1 (2024); 1808 2237-8960reponame:Brazilian Journal of Operations & Production Management (Online)instname:Associação Brasileira de Engenharia de Produção (ABEPRO)instacron:ABEPROenghttps://bjopm.org.br/bjopm/article/view/1808/1066Copyright (c) 2024 Daniela Uziel, Edison Renato Pereira da Silva, Humberto Henriques de Arrudahttp://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessUziel, DanielaSilva, Edison Renato Pereira daArruda, Humberto Henriques de2024-02-10T13:41:52Zoai:ojs.bjopm.org.br:article/1808Revistahttps://bjopm.org.br/bjopmONGhttps://bjopm.org.br/bjopm/oaibjopm.journal@gmail.com2237-89601679-8171opendoar:2024-02-10T13:41:52Brazilian Journal of Operations & Production Management (Online) - Associação Brasileira de Engenharia de Produção (ABEPRO)false |
dc.title.none.fl_str_mv |
Measuring the Social and Economic Impact of Universities' Entrepreneurial Activity: Introducing the BR-AFC Algorithm to Sort Alumni-Founded Companies |
title |
Measuring the Social and Economic Impact of Universities' Entrepreneurial Activity: Introducing the BR-AFC Algorithm to Sort Alumni-Founded Companies |
spellingShingle |
Measuring the Social and Economic Impact of Universities' Entrepreneurial Activity: Introducing the BR-AFC Algorithm to Sort Alumni-Founded Companies Uziel, Daniela alumni-founded companies Entrepreneurship University Industry Relationship Internal Revenue Service |
title_short |
Measuring the Social and Economic Impact of Universities' Entrepreneurial Activity: Introducing the BR-AFC Algorithm to Sort Alumni-Founded Companies |
title_full |
Measuring the Social and Economic Impact of Universities' Entrepreneurial Activity: Introducing the BR-AFC Algorithm to Sort Alumni-Founded Companies |
title_fullStr |
Measuring the Social and Economic Impact of Universities' Entrepreneurial Activity: Introducing the BR-AFC Algorithm to Sort Alumni-Founded Companies |
title_full_unstemmed |
Measuring the Social and Economic Impact of Universities' Entrepreneurial Activity: Introducing the BR-AFC Algorithm to Sort Alumni-Founded Companies |
title_sort |
Measuring the Social and Economic Impact of Universities' Entrepreneurial Activity: Introducing the BR-AFC Algorithm to Sort Alumni-Founded Companies |
author |
Uziel, Daniela |
author_facet |
Uziel, Daniela Silva, Edison Renato Pereira da Arruda, Humberto Henriques de |
author_role |
author |
author2 |
Silva, Edison Renato Pereira da Arruda, Humberto Henriques de |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Uziel, Daniela Silva, Edison Renato Pereira da Arruda, Humberto Henriques de |
dc.subject.por.fl_str_mv |
alumni-founded companies Entrepreneurship University Industry Relationship Internal Revenue Service |
topic |
alumni-founded companies Entrepreneurship University Industry Relationship Internal Revenue Service |
description |
Goal: This study introduces an algorithm to sort alumni-founded companies from the public Brazilian Internal Revenue Service (IRS) database. Design/Methodology/Approach: Departing from IRS data and student data from the university, sequential filters are applied to arrive at a final list of alumni-founded companies. Results: The main result of this study is the establishment of the algorithm itself, which emerged after cycles of iterations of analysis and rewriting. To test its reliability, a sample of 1625 alumni was used. The algorithm successfully identified 140 founders of 159 AFC. Founders were heterogeneously distributed throughout the decades analyzed. Companies belonged to different industry sectors and were classified according to their technological intensity, with predominance of middle-low and low intensity. Research limitations/implications: Although the BR-AFC algorithm is applicable to any Brazilian institution, generalization to other countries depends on access to country-specific databases containing data about companies and its partners. Additionally, the final result depends on the reliability of input data and of user decisions about the rigor of its premises. Practical implications: The BR-AFC algorithm can improve measurements of the socioeconomic impact of educational institutions. It points to the formation of entrepreneurs and, as a consequence, institutions can evaluate courses and educational programs and improve curricula. Policymakers and sponsoring institutions can measure return over investment, outcomes of policies to encourage entrepreneurship and rank universities according to novel criteria. Originality/Value: The main contribution to the literature is exploring novel approaches to measuring university-industry relationship. More specifically, it proposes an algorithm to identify the alumni-founded companies of a given university from large country-based databases. |
publishDate |
2024 |
dc.date.none.fl_str_mv |
2024-02-10 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Research paper |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://bjopm.org.br/bjopm/article/view/1808 10.14488/BJOPM.1808.2024 |
url |
https://bjopm.org.br/bjopm/article/view/1808 |
identifier_str_mv |
10.14488/BJOPM.1808.2024 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
https://bjopm.org.br/bjopm/article/view/1808/1066 |
dc.rights.driver.fl_str_mv |
Copyright (c) 2024 Daniela Uziel, Edison Renato Pereira da Silva, Humberto Henriques de Arruda http://creativecommons.org/licenses/by/4.0 info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2024 Daniela Uziel, Edison Renato Pereira da Silva, Humberto Henriques de Arruda http://creativecommons.org/licenses/by/4.0 |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Brazilian Association for Industrial Engineering and Operations Management (ABEPRO) |
publisher.none.fl_str_mv |
Brazilian Association for Industrial Engineering and Operations Management (ABEPRO) |
dc.source.none.fl_str_mv |
Brazilian Journal of Operations & Production Management; Vol. 21 No. 1 (2024); 1808 2237-8960 reponame:Brazilian Journal of Operations & Production Management (Online) instname:Associação Brasileira de Engenharia de Produção (ABEPRO) instacron:ABEPRO |
instname_str |
Associação Brasileira de Engenharia de Produção (ABEPRO) |
instacron_str |
ABEPRO |
institution |
ABEPRO |
reponame_str |
Brazilian Journal of Operations & Production Management (Online) |
collection |
Brazilian Journal of Operations & Production Management (Online) |
repository.name.fl_str_mv |
Brazilian Journal of Operations & Production Management (Online) - Associação Brasileira de Engenharia de Produção (ABEPRO) |
repository.mail.fl_str_mv |
bjopm.journal@gmail.com |
_version_ |
1797051459480584192 |