Version 1
: Received: 12 September 2022 / Approved: 13 September 2022 / Online: 13 September 2022 (09:50:32 CEST)
How to cite:
Leogrande, A.; Costantiello, A.; Laureti, L. The Role of Non-R&D Expenditures in Promoting Innovation in Europe. Preprints2022, 2022090166. https://doi.org/10.20944/preprints202209.0166.v1
Leogrande, A.; Costantiello, A.; Laureti, L. The Role of Non-R&D Expenditures in Promoting Innovation in Europe. Preprints 2022, 2022090166. https://doi.org/10.20944/preprints202209.0166.v1
Leogrande, A.; Costantiello, A.; Laureti, L. The Role of Non-R&D Expenditures in Promoting Innovation in Europe. Preprints2022, 2022090166. https://doi.org/10.20944/preprints202209.0166.v1
APA Style
Leogrande, A., Costantiello, A., & Laureti, L. (2022). The Role of Non-R&D Expenditures in Promoting Innovation in Europe. Preprints. https://doi.org/10.20944/preprints202209.0166.v1
Chicago/Turabian Style
Leogrande, A., Albertoc Costantiello and Lucio Laureti. 2022 "The Role of Non-R&D Expenditures in Promoting Innovation in Europe" Preprints. https://doi.org/10.20944/preprints202209.0166.v1
Abstract
In this article we estimate the value of “Non-R&D Innovation Expenditures” in Europe. We use data from the European Innovation Scoreboard-EIS of the European Commission from the period 2010-2019. We test data with the following econometric models i.e.: Pooled OLS, Dynamic Panel, Panel Data with Fixed Effects, Panel Data with Random Effects, WLS. We found that “Non-R&D Innovation Expenditures” is positively associated among others to “Innovation Index” and “Firm Investments” and negatively associated among others to “Human Resources” and “Government Procurement of Advanced Technology Products”. We use the k-Means algorithm with either the Silhouette Coefficient and the Elbow Method in a confrontation with the network analysis optimized with the Distance of Manhattan and we find that the optimal number of clusters is four. Furthermore, we propose a confrontation among eight machine learning algorithms to predict the level of “Non-R&D Innovation Expenditures” either with Original Data-OD either with Augmented Data-AD. We found that Gradient Boost Trees Regression is the best predictor for OD while Tree Ensemble Regression is the best Predictor for AD. Finally, we verify that the prediction with AD is more efficient of that with OD with a reduction in the average value of statistical errors equal to 40,50%.
Keywords
innovation and invention: processes and incentives; management of technological innovation and R&D; diffusion orocesses; 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.
