The Determinants of Design Applications in Europe

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%.

The Exports of Knowledge Intensive Services: A Complex Metric Approach

“…This relationship appears to be counterfactual. However, it seems to confirm the idea, as well as in the description of the previous variables, that the presence of elements of significant innovation at the country level is not necessarily associated with the export of knowledge-intensive services [26].…”

The Exports of Knowledge Intensive Services. A Complex Metric Approach

“…This relationship appears to be counterfactual. However, it seems to confirm the idea, as well as in the description of the previous variables, that the presence of elements of significant innovation at the country level is not necessarily associated with the export of knowledgeintensive services [26]. • Non-R&D innovation expenditure: considers the investment in technological innovation of companies net of Research and Development.…”

“…• Switzerland with an increased variation from an amount of 108.249 up to a variation of 109.250 or equal to a variation of 1.001 units equal to an amount of 0.925%; • Czech Republic with a diminutive variation from an amount of 56.920 units up to a value of 55.724 units or equal to a variation of -1.196 units equal to an amount of -2.101%; • Germany with a diminutive variation from an amount of 121.578 units up to a variation of 120.406 units or equal to an amount of -1.172 units equal to a value of -0.964%; • Greece with an increased variation from an amount of 76,653 units up to a value of 77,160 units or equal to an amount of 0.507 units equal to an amount of 0.661%; • Spain with a diminutive variation from an amount of 28,738 units up to a value of 26,868 units or equal to a variation of -1.870 units equal to a value of -6.507%; • France with an increased variation from an amount of 99,603 units up to a valor Edi 100,261 units or equal to an amount of 0.658 units equal to a value of 0.661%; • Hungary with a diminutive variation from an amount of 71,771 units up to a value of 70.517 units or equal to a value of -1.254 units equal to an amount of -1.747%; • Ireland with an increased variation from a value of 161,901 units up to a value of 162,676 units or open to a variation of 0.775 units equal to a variation of 0.479%; • Israel with a diminutive variation from an amount of 110,901 units up to a value of 108,280 units or equal to a variation of -2.6 units equal to an amount of -2.36%; • Malta with a diminutive variation from an amount of 37,409 units up to a value of 36.994 units or equal to a variation of -0.415 units equal to an amount of -1.109%; • Norway with a variation from an amount of 130.483 units up to a value of 132.708 units or equal to a variation of 2.225 units equal to an amount of 1.705%; • Slovenia with a diminutive variation from an amount of 36.935 units up to a value of 35,206 units or equal to a variation of -1.729 units equal to an amount of -4.681%. Overall, the value of the export of high -intensity services is predicted in decrease for the countries considered by an average value of 86.762 units up to a value of 86,338 units or equal to a variation of -0.424 units equal to a value of -12.54%.…”

K-Means Clusterization and Machine Learning Prediction of European Most Cited Scientific Publications

“…Furthermore, it is also very important that there is a first-level university system capable of generating scientific and intellectual products of excellence [35]. • Intellectual Assets: is a variable that considers the sum of three different sub-variables, namely: PCT patent Applications, Trademark Applications and Design Applications [36]. There is therefore a negative relationship between the value of intellectual assets and the presence of a set of scientific publications that are most cited internationally.…”