The determinants of “International Scientific Co-Publications” in Europe are analyzed in the following article. Data from the European Innovation Scoreboard-EIS of the European Union for 36 countries in the period 2010-2019 were used. The data were analyzed using Panel Data with Fixed Effects, Panel Data with Random Effects, Pooled OLS, WLS, Dynamic Panel. The results show that the variable “International Scientific Co-Publication” is negatively associated with “Employment Share Manufacturing”, “Intellectual Assets”, “Turnover Share Large Enterprises”, “Linkages” and positively associated with “SMEs innovating in house”, “Trademark Applications”, “Human Resources”, “Public-private co-publications”, “Attractive Research Systems”, “Government procurement of advanced technology products”, “Turnover Share SMEs”. Then, a clustering analysis is realized with the algorithm k-Means. The Silhouette Coefficient and the Elbow Method are confronted to optimize the k-Means algorithm. The results show that the Elbow method is more efficient than the Silhouette coefficient in identifying the optimal number of clusters corresponding to k = 4 with the k-Means algorithm. A network analysis was then carried out using the “Manhattan Distance”. The analysis shows the presence of 9 network structures of which 5 are complex i.e., with a number of linkages greater than 3, and 4 are basic i.e. consist of a single link between two countries. Furthermore, we confront eight different machine learning algorithms to predict the future level of “International Scientific Co-Publications”. We found that the best algorithm in performing prediction with original data is the Tree Ensemble Regression. The predicted value of “International Scientific Co-Publication” is expected to growth by 0.61%.
The following article investigates the determinants that lead innovative SMEs to collaborate. Data from 36 European countries is analyzed using Panel Data with Fixed Effects, Panel Data with Random Effects, Pooled OLS, WLS and Dynamic Panel models. The analysis shows that the ability of innovative SMEs to collaborate is positively associated with the following variables: "Linkages", "Share High and Medium high-tech manufacturing", "Finance and Support", "Broadband Penetration", "Non-R&D Innovation Expenditure" and negatively to the following variables: "New Doctorate graduates", "Venture Capital", "Foreign Controlled Enterprises Share of Value Added", "Public-Private Co-Publications", "Population Size", "Private co-funding of Public R&D expenditures". A clustering with k-Means algorithm optimized by the Silhouette coefficient was then performed and four clusters were found. A network analysis was then carried out and the result shows the presence of three composite structures of links between some European countries. Furthermore, a comparison was made between eight different predictive machine learning algorithms and the result shows that the Random Forest Regression algorithm performs better and predicts a reduction in the ability of innovative SMEs to collaborate equal to an average of 4.4%. Later a further comparison is made with augmented data. The results confirm that the best predictive algorithm is Random Forest Regression, the statistical errors of the prediction decrease on average by 73.5%, and the ability of innovative SMEs to collaborate is predicted to growth by 9.2%.
The determinants of enterprises providing ICT training in Europe are analyzed in this article. Data are collected from the European Innovation Scoreboard-EIS of the European Commission for 36 European countries in the period 2000-2019. Data are analyzed with Panel Data with Fixed Effects, Panel Data with Random Effects, Dynamic Panel, WLS and Pooled OLS. Results show that the number of enterprises providing ICT training in Europe is positively associate with “Innovation Index”, “Innovators”, “New Doctorate Graduates”, “Tertiary Education” and negatively associated with “Government Procurement of Advanced Technology Products”, “Human Resources”, and “Marketing or Organisational Innovators”. In adjunct a cluster analysis is performed by using k-Means algorithm optimized with the Silhouette Coefficient and we find the presence of four clusters. Finally, we use eight different machine learning algorithms to predict the value of the enterprises providing ICT training in Europe. We found that the Simple Tree Regression is the best predictor and that the number of enterprises providing ICT training in Europe is expected to growth of the 5,02%.
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