Version 1
: Received: 11 June 2022 / Approved: 13 June 2022 / Online: 13 June 2022 (05:21:48 CEST)
How to cite:
Leogrande, A.; Costantiello, A.; Laureti, L. The Exports of Knowledge Intensive Services: A Complex Metric Approach. Preprints2022, 2022060169. https://doi.org/10.20944/preprints202206.0169.v1
Leogrande, A.; Costantiello, A.; Laureti, L. The Exports of Knowledge Intensive Services: A Complex Metric Approach. Preprints 2022, 2022060169. https://doi.org/10.20944/preprints202206.0169.v1
Leogrande, A.; Costantiello, A.; Laureti, L. The Exports of Knowledge Intensive Services: A Complex Metric Approach. Preprints2022, 2022060169. https://doi.org/10.20944/preprints202206.0169.v1
APA Style
Leogrande, A., Costantiello, A., & Laureti, L. (2022). The Exports of Knowledge Intensive Services: A Complex Metric Approach. Preprints. https://doi.org/10.20944/preprints202206.0169.v1
Chicago/Turabian Style
Leogrande, A., Alberto Costantiello and Lucio Laureti. 2022 "The Exports of Knowledge Intensive Services: A Complex Metric Approach" Preprints. https://doi.org/10.20944/preprints202206.0169.v1
Abstract
In the following article, the value of the "Knowledge Intensive Services Exports in Europe" in 36 European countries is estimated. The data were analyzed through a set of econometric models or: Poled OLS, Dynamic Panel, Panel Data with Fixed Effects, Panel Data with Random Effects, WLS. The results show that “Knowledge Intensive Services Exports” is negatively associated, among others, with "Buyer Sophistication", "Government Procurement of Advanced Technology Products", and positively associated with the following variables i.e. "Innovation Index", "Sales Impacts" and "Total Entrepreneurial Activity". Then a clusterization with k-Means algorithm was made with the Elbow method. The results show the presence of 3 clusters. A network analysis was later built and 4 complex network structures and three structures with simplified networks were detected. To predict the future trend of the variable, a comparison was made with eight different machine learning algorithms. The results show that prediction with Augmented Data-AD is more efficient than prediction with Original Data-AD with a reduction of the mean of statistical errors equal to 55,94%.
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.
