Measuring the Social and Economic Impact of Universities’ Entrepreneurial Activity: Introducing the BR-AFC Algorithm to Sort Alumni-Founded Companies - Uziel, Daniela; Silva, Edison Renato Pereira da; Arruda, Humberto Henriques de (Brazilian Journal of Operations & Production Management, 2024)
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.