TY - JOUR
T1 - Unlocking the secrets of Spain's R&D subsidies
T2 - An advanced analysis of applicant companies
AU - Espinosa-Blasco, Mónica
AU - Penagos-Londoño, Gabriel I.
AU - Ruiz-Moreno, Felipe
AU - Vilaplana-Aparicio, María J.
N1 - Publisher Copyright:
© 2023 Mónica Espinosa-Blasco et al., published by Sciendo.
PY - 2023/7/1
Y1 - 2023/7/1
N2 - Innovation is crucial for companies to stay competitive, provide value to customers, and generate profits. Likewise, research and development (R&D) is critical for companies to sustain productivity growth. Spain has lagged behind other countries in terms of R&D investment, with only 1.4% of its GDP allocated to R&D, well below the European average. To improve this situation, the government offers subsidies to stimulate R&D in Spanish companies. This study examines the profile of subsidized companies in Spain. The aim is to provide insight into the support for companies that apply for innovation subsidies by analyzing the profile of subsidized companies and identifying key variables influencing the success of obtaining innovation grants. The study is based on advanced estimation methods. Natural language processing (NLP), artificial neural network (ANN) techniques, and clustering are used to perform rigorous and robust analysis of the profile of subsidized companies in Spain. The study thus contributes to knowledge in the field of innovation subsidies.
AB - Innovation is crucial for companies to stay competitive, provide value to customers, and generate profits. Likewise, research and development (R&D) is critical for companies to sustain productivity growth. Spain has lagged behind other countries in terms of R&D investment, with only 1.4% of its GDP allocated to R&D, well below the European average. To improve this situation, the government offers subsidies to stimulate R&D in Spanish companies. This study examines the profile of subsidized companies in Spain. The aim is to provide insight into the support for companies that apply for innovation subsidies by analyzing the profile of subsidized companies and identifying key variables influencing the success of obtaining innovation grants. The study is based on advanced estimation methods. Natural language processing (NLP), artificial neural network (ANN) techniques, and clustering are used to perform rigorous and robust analysis of the profile of subsidized companies in Spain. The study thus contributes to knowledge in the field of innovation subsidies.
KW - Innovation subsidies
KW - R&D
KW - finite mixture model
KW - innovation strategy
KW - natural language processing
KW - neural network
KW - public funds
UR - http://www.scopus.com/inward/record.url?scp=85176435779&partnerID=8YFLogxK
U2 - 10.2478/amns.2023.2.01144
DO - 10.2478/amns.2023.2.01144
M3 - Article
AN - SCOPUS:85176435779
SN - 2444-8656
VL - 8
SP - 3521
EP - 3544
JO - Applied Mathematics and Nonlinear Sciences
JF - Applied Mathematics and Nonlinear Sciences
IS - 2
ER -