New Insights on the Allocation of Innovation Subsidies: A Machine Learning Approach

Mónica Espinosa-Blasco (), Gabriel I. Penagos-Londoño (), Felipe Ruiz-Moreno () and María J. Vilaplana-Aparicio ()
Additional contact information
Mónica Espinosa-Blasco: University of Alicante
Gabriel I. Penagos-Londoño: Pontificia Universidad Javeriana
Felipe Ruiz-Moreno: University of Alicante
María J. Vilaplana-Aparicio: University of Alicante

Journal of the Knowledge Economy, 2024, vol. 15, issue 1, No 111, 2704-2725

Abstract: Abstract Gaining more insights on how R&D&i subsidies are allocated is highly relevant for companies and policymakers. This article provides new evidence of the identification of some key drivers for companies participating in R&D&i project selection processes. It extends the existing literature by providing insight based on sophisticated, accurate methodology. A metaheuristic optimization algorithm is employed to select the most useful variables. Their importance is then ranked using a machine learning process, namely a random forest. A sample of 1252 cases of R&D&i subsidies is used for more than 800 companies based in Spain between 2014 and 2018. The study contributes by providing useful knowledge into how the value of received subsidies are associated with some firm characteristics. The findings allow the implementation of transparent public innovation policies and the reduction of the gap between the aspects that are considered important and those that actually determine the destination of these subsidies.

Keywords: Innovation; Subsidies; R&D&i strategy; Valencian companies; Random forest (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s13132-023-01295-9 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:jknowl:v:15:y:2024:i:1:d:10.1007_s13132-023-01295-9

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/13132

DOI: 10.1007/s13132-023-01295-9

Access Statistics for this article

Journal of the Knowledge Economy is currently edited by Elias G. Carayannis

More articles in Journal of the Knowledge Economy from Springer, Portland International Center for Management of Engineering and Technology (PICMET)
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().