scholarly journals The Intellectual Assets in Europe

2021 ◽  
Author(s):  
Costantiello Alberto ◽  
Laureti Lucio ◽  
Leogrande Angelo
Keyword(s):  
Fixed Effects ◽  
Government Procurement ◽  
Advanced Technology ◽  
High Tech ◽  
Dynamic Panel ◽  
Research Systems ◽  
Technology Products ◽  
Employment Share ◽  
Intellectual Assets ◽  
Knowledge Intensive

Abstract In this article we investigate the determinants of the Intellectual Assets in Europe. We use data from the European Innovation Scoreboard of the European Commission in the period 2000-2019 for 36 countries. Data are analyzed using Panel with Fixed Effects, Random Effects, WLS, Pooled OLS, Dynamic Panel at 1 Stage. Results show that the presence of Intellectual Assets in Europe is positively associated with “Enterprise Births”, “Top R&D Spending Enterprises per 10 mln Population”, “Employment Share Manufacturing”, “Share High and Medium high-tech Manufacturing”, “Attractive Research Systems”, “Finance and Support”, “Innovators”, “Sales Impact” and is negatively associated to “Government Procurement of Advanced Technology Products” and “Share Knowledge-Intensive Services”

scholarly journals The SMEs Innovation in Europe

2021 ◽  
Author(s):  
Alberto Costantiello ◽  
Laureti Lucio ◽  
Leogrande Angelo
Keyword(s):  
Panel Data ◽  
Population Density ◽  
Fixed Effects ◽  
Government Procurement ◽  
Advanced Technology ◽  
High Tech ◽  
Dynamic Panel ◽  
Technology Products ◽  
Firm Investments ◽  
Intellectual Assets

Abstract In this article we investigate the determinants of SMEs Innovation in Europe. We use data from the European Innovation Scoreboard of the European Commission in the period 2000-2019 for 36 countries. Data are analyzed through Panel Data with Fixed Effects, Random Effects, Dynamic Panel at 1 Stage and WLS. Results show that the presence of Innovators is positively associated with “Enterprise births”, “Government Procurement of Advanced Technology Products”, “Firm Investments”, “Intellectual Assets”, “Sales Impacts”, “Share High and Medium High-Tech Manufacturing” and negatively associated to “FDI Net Inflows” and “Population Density”.

scholarly journals The Broadband Penetration in Europe

2021 ◽  
Author(s):  
Angelo Leogrande ◽  
ALBERTO COSTANTIELLO ◽  
LUCIO LAURETI
Keyword(s):  
Panel Data ◽  
Fixed Effects ◽  
Polynomial Regression ◽  
Government Procurement ◽  
Machine Learning Algorithms ◽  
Advanced Technology ◽  
Technology Products ◽  
Firm Investments ◽  
Intellectual Assets ◽  
Knowledge Intensive

Abstract In this article we estimate the determinants of broadband penetration in Europe. We use data from the European Innovation Scoreboard of the European Commission for 37 countries in the period 2010-2019. We apply Panel Data with Fixed Effects, Panel Data with Random Effects, WLS, OLS and Dynamic Panel. We found that the level of “Broadband Penetration” in Europe is positively associated to “Enterprises Providing ICT Training”, “Innovative Sales Share”, “Intellectual Assets”, “Knowledge-Intensive Service Exports”, “Turnover Share SMEs”, “Innovation Friendly Environment” and negatively associated with “Government procurement of advanced technology products”, “Sales Impact”, “Firm Investments”, “Opportunity-Driven Entrepreneurship”, “Most Cited Publications”, “Rule of Law”. In adjunct we perform a clusterization with k-Means algorithm optimized with the Silhouette Coefficient and we find the presence of three different clusters. Finally, we apply eight machine learning algorithms to predict the level of “Broadband Penetration” in Europe and we find that the Polynomial Regression algorithm is the best predictor and that the level of the variable is expected to increase of 10,4%.

scholarly journals Human Resources in Europe. Estimation, Clusterization, Machine Learning and Prediction

2021 ◽  
Author(s):  
ANGELO LEOGRANDE ◽  
ALBERTO COSTANTIELLO
Keyword(s):  
Machine Learning ◽  
Human Resources ◽  
Fixed Effects ◽  
Tertiary Education ◽  
Government Procurement ◽  
Machine Learning Algorithms ◽  
Advanced Technology ◽  
High Tech ◽  
Technology Products ◽  
Using Data

Abstract We estimate the relationships between innovation and human resources in Europe using the European Innovation Scoreboard of the European Commission for 36 countries for the period 2010-2019. We perform Panel Data with Fixed Effects, Random Effects, Pooled OLS, Dynamic Panel and WLS. We found that Human resources is positively associated to “Basic-school entrepreneurial education and training”, “Employment MHT manufacturing KIS services”, “Employment share Manufacturing (SD)”, “Lifelong learning”, “New doctorate graduates”, “R&D expenditure business sector”, “R&D expenditure public sector”, “Tertiary education”. Our results also show that “Human Resources” is negatively associated to “Government procurement of advanced technology products”, “Medium and high-tech product exports”, “SMEs innovating in-house”, “Venture capital”. In adjunct we perform a clusterization with k-Means algorithm and we find the presence of three clusters. Clusterization shows the presence of Central and Northern European countries that has higher levels of Human Resources, while Southern and Eastern Europe has very low degree of Human Resources. Finally, we use seven machine learning algorithms to predict the value of Human Resources in Europe Countries using data in the period 2014-2021 and we show that the linear regression algorithm performs at the highest level.

scholarly journals The Impact of Venture Capital Expenditures on Innovation in Europe

2021 ◽  
Author(s):  
Angelo Leogrande ◽  
ALBERTO COSTANTIELLO ◽  
LUCIO LAURETI
Keyword(s):  
Panel Data ◽  
Venture Capital ◽  
Fixed Effects ◽  
Government Procurement ◽  
Advanced Technology ◽  
Capital Expenditures ◽  
High Tech ◽  
Technology Products ◽  
The Impact ◽  
The Relationship

Abstract We investigate the relationship between “Venture Capital Expenditures” and innovation in Europe. Data are collected from the European Innovation Scoreboard for 36 countries in the period 2010-2019. We perform Panel Data with Fixed Effects, Panel Data with Random Effects, Pooled OLS, WLS, Dynamic Panel. Results show that the level of Venture Capitalist Expenditure is positively associated to “Foreign Doctorate Students” and “Innovation Index” and negatively related to “Government Procurement of Advanced Technology Products”, “Innovators”, “Medium and High-Tech Products Exports”, “Public-Private Co-Publications”. In adjunct, cluster analysis is realized with the algorithm k-Means and the Silhouette coefficient, and we found the presence of four different clusters for the level of “Venture Capital Expenditures”. Finally, we propose a confrontation among 8 different algorithms of machine learning to predict the level of “Venture Capital Expenditures” and we find that the linear regression generates the best results in terms of minimization of MAE, MSE, RMSE.

scholarly journals The Corporate Innovation in Europe

2021 ◽  
Author(s):  
Alberto Costantiello ◽  
Lucio Laureti ◽  
Leogrande Angelo
Keyword(s):  
Research And Development ◽  
Tertiary Education ◽  
Value Added ◽  
Entrepreneurial Activity ◽  
High Tech ◽  
High Tech Product ◽  
Firm Investments ◽  
Intellectual Assets ◽  
Education Design ◽  
Knowledge Intensive

Abstract In this article we investigate the political and industrial determinants of firm investment in Research and Development. We use data from the European Innovation Scoreboard of the European Commission for 36 countries in the period 2000-2019. We found that firm investments in Research and Development are positively associated with “Linkages”, “Innovation Index”, “International Co-publications”, “Medium and high-tech product exports”, “Non-R&D innovation expenditure”, “Turnover share large enterprises”, “Human Resources”, “Intellectual Assets”. Firm investments in Research and Development are negatively associated to “Foreign doctorate students”, “Knowledge-intensive services exports”, “Private co-funding of public R&D expenditures”, “Basic-school entrepreneurial education and training (SD)”, “New doctorate graduates”, “Trademark applications”, “Tertiary education” “Design applications”, “Lifelong Learning”, “Foreign-controlled enterprises – share of value added (SD)”, “Total Entrepreneurial Activity (TEA) (SD)”.

scholarly journals The Employment in Innovative Enterprises in Europe

2022 ◽  
Author(s):  
Lucio Laureti ◽  
Costantiello Alberto ◽  
Marco Maria Matarrese ◽  
Angelo Leogrande
Keyword(s):  
Machine Learning ◽  
Panel Data ◽  
Fixed Effects ◽  
Value Added ◽  
Machine Learning Algorithms ◽  
High Tech ◽  
Dynamic Panel ◽  
Silhouette Coefficient ◽  
High Tech Product ◽  
And Training

Abstract In this article we evaluate the determinants of the Employment in Innovative Enterprises in Europe. We use data from the European Innovation Scoreboard of the European Commission for 36 countries in the period 2000-2019 with Panel Data with Fixed Effects, Panel Data with Random Effects, Dynamic Panel, WLS and Pooled OLS. We found that the “Employment in Innovative Enterprises in Europe” is positively associated with “Broadband Penetration in Europe”, “Foreign Controlled Enterprises Share of Value Added”, “Innovation Index”, “Medium and High-Tech Product Exports” and negatively associated to “Basic School Entrepreneurial Education and Training”, “International Co-Publications”, and “Marketing or Organizational Innovators”. Secondly, we perform a cluster analysis with the k-Means algorithm optimized with the Silhouette Coefficient and we found the presence of four different clusters. Finally, we perform a comparison among eight different machine learning algorithms to predict the level of “Employment in Innovative Enterprises” in Europe and we found that the Linear Regression is the best predictor.

scholarly journals The Determinants of Design Applications in Europe

2021 ◽  
Author(s):  
Angelo Leogrande ◽  
Alberto Costantiello ◽  
Lucio Laureti ◽  
Domenico Leogrande
Keyword(s):  
Machine Learning ◽  
Panel Data ◽  
Fixed Effects ◽  
Econometric Models ◽  
Machine Learning Algorithms ◽  
Dynamic Panel ◽  
A Value ◽  
Silhouette Coefficient ◽  
Firm Investments ◽  
Intellectual Assets

Abstract In this article we estimate the level of “Design Application” in 37 European Countries in the period 2010-2019. We use data from the European Innovation Scoreboard-EIS of the European Commission. We perform four econometric models i.e., Pooled OLS, Panel Data with Random Effects, Panel Data with Fixed Effects, Dynamic Panel. We found that the level of Design Applications is negatively associated to “Enterprise Births”, “Finance and Support”, “Firm Investments” and positively associated with “Venture Capital”, “Turnover share large enterprises”, “R&D expenditure public sector”, “Intellectual Assets”. In adjunct we perform a cluster analysis with the application of the k-Means algorithm optimized with the Silhouette Coefficient and we found three different clusters. Finally, we confront eight different machine learning algorithms to predict the level of “Design Application” and we found that the Tree Ensemble is the best predictor with a value for the 30% of the dataset analyzed that is expected to decrease in mean of -12,86%.

scholarly journals Does the Technological Content of Government Demand Matter for Private R&D? Evidence from US States

2016 ◽  
Vol 8 (2) ◽  
pp. 45-84 ◽  
Author(s):  
Viktor Slavtchev ◽  
Simon Wiederhold
Keyword(s):  
Instrumental Variable ◽  
Fixed Effects ◽  
Government Procurement ◽  
High Tech ◽  
Aggregate Level ◽  
Us States ◽  
Unique Data ◽  
Panel Fixed Effects ◽  
Federal Procurement

Governments purchase everything from airplanes to zucchini. This paper investigates the role of the technological content of government procurement in innovation. In a theoretical model, we first show that a shift in the composition of public purchases toward high-tech products translates into higher economy-wide returns to innovation, leading to an increase in the aggregate level of private R&D. Using unique data on federal procurement in US states and performing panel fixed-effects estimations, we find support for the model's prediction of a positive R&D effect of the technological content of government procurement. Instrumental-variable estimations suggest a causal interpretation of our findings. (JEL H57, H76, O31, O32, O38)

High-tech business in crisis conditions: development trends of large, medium and small enterprises

2020 ◽  
Vol 18 (4) ◽  
pp. 48-58
Author(s):  
Vladislav V. Spitsyn ◽  
Alexander A. Mikhal'chuk ◽  
Anastasia A. Bulykina ◽  
Svetlana N. Popova ◽  
Irina E. Nikulina
Keyword(s):  
Small Businesses ◽  
Negative Impact ◽  
Service Industries ◽  
High Tech ◽  
Small Enterprises ◽  
Mathematical Methods ◽  
Crisis Period ◽  
Knowledge Intensive ◽  
Crisis Conditions ◽  
Medium And Small Enterprises

Leading world countries view innovative development and high-tech business as an opportunity to overcome economic stagnation and decline in economic growth. One of the modern trends in the analysis of high-tech development is the study of high-tech knowledge-intensive service industries and their development in times of crisis. The purpose of the paper is to identify patterns of development of large, medium and small enterprises in high-tech service industries in Russia during periods of crisis. Economic and economic-mathematical methods of analysis are applied to the formed samples of enterprises. The research period is 2013-2017. The financial indicators of enterprises were adjusted for the level of accumulated inflation in relation to 2013. According to results, large and medium-sized enterprises showed insignificant or weak significant positive dynamics of revenue during all years of the crisis period. The crisis period did not lead to a decrease in the revenue of these groups of enterprises. The acute phase of the crisis (2014-2015) had a pronounced negative impact on the group of small enterprises in all studied industries, but they successfully recovered in 2016-2017 and reached the pre-crisis level of revenue. The total revenue by industries and groups of enterprises in 2017 became higher than in 2013, and its growth rates were significant for many groups of enterprises, which indicates a successful overcoming of the crisis period and signs of growth in high-tech service industries. Our study shows the need for state support for small businesses in high-tech service industries in crisis conditions, and identifies the possibilities of adaptation of enterprises in these industries to an unfavorable external environment. Our results may be useful for the purposes of government stimulation of economic development in the current environment.

U.S. Exports of Advanced Technology Products (ATP) to China

2009 ◽  
Author(s):  
Alexander B. Hammer ◽  
Robert B. Koopman ◽  
Andrew Martinez
Keyword(s):  
Advanced Technology ◽  
Technology Products
Sign in / Sign up

Export Citation Format

Share Document