Version 1
: Received: 12 November 2022 / Approved: 14 November 2022 / Online: 14 November 2022 (08:26:39 CET)
How to cite:
LEOGRANDE, A.; Costantiello, A.; Laureti, L. The Impact of Patent Applications on Technological Innovation in European Countries. Preprints2022, 2022110245. https://doi.org/10.20944/preprints202211.0245.v1
LEOGRANDE, A.; Costantiello, A.; Laureti, L. The Impact of Patent Applications on Technological Innovation in European Countries. Preprints 2022, 2022110245. https://doi.org/10.20944/preprints202211.0245.v1
LEOGRANDE, A.; Costantiello, A.; Laureti, L. The Impact of Patent Applications on Technological Innovation in European Countries. Preprints2022, 2022110245. https://doi.org/10.20944/preprints202211.0245.v1
APA Style
LEOGRANDE, A., Costantiello, A., & Laureti, L. (2022). The Impact of Patent Applications on Technological Innovation in European Countries. Preprints. https://doi.org/10.20944/preprints202211.0245.v1
Chicago/Turabian Style
LEOGRANDE, A., Alberto Costantiello and Lucio Laureti. 2022 "The Impact of Patent Applications on Technological Innovation in European Countries" Preprints. https://doi.org/10.20944/preprints202211.0245.v1
Abstract
We investigate the innovational determinants of “Patent Applications” in Europe. We use data from the European Innovation Scoreboard-EIS of the European Commission for 36 countries in the period 2010-2019. We use Panel Data with Fixed Effects, Panel Data with Random Effects, Pooled OLS, WLS and Dynamic Panel. We found that the variables that have a deeper positive association with “Patent Applications” are “Human Resources” and “Intellectual Assets”, while the variables that show a more intense negative relation with Patent Applications are “Employment Share in Manufacturing” and “Total Entrepreneurial Activity”. A cluster analysis with the k-Means algorithm optimized with the Silhouette Coefficient has been realized. The results show the presence of two clusters. A network analysis with the distance of Manhattan has been performed and we find three different complex network structures. Finally, a comparison is made among eight machine learning algorithms for the prediction of the future value of the “Patent Applications”. We found that PNN-Probabilistic Neural Network is the best performing algorithm. Using PNN the results show that the mean future value of “Patent Applications” in the estimated countries is expected to decrease of -0.1%.
Keywords
Innovation, and Invention: Processes and Incentives; Management of Technological Innovation and R&D; Diffusion Processes; Open Innovation
Subject
Business, Economics and Management, Economics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
