European Union Innovation Efficiency Assessment Based on Data Envelopment Analysis

Abstract

Though much attention is dedicated to the development of its research and innovation policy, the European Union constantly struggles to match the level of the strongest innovators in the world. Therefore, there is a necessity to analyze the individual efforts and conditions of the 27 member states that might determine their final innovative performance. The results of a scientific literature review showed that there is a growing interest in the usage of artificial intelligence when seeking to improve decision-making processes. Data envelopment analysis, as a branch of computational intelligence methods, has proved to be a reliable tool for innovation efficiency evaluation. Therefore, this paper aimed to apply DEA for the assessment of the European Union’s innovation efficiency from 2000 to 2020, when innovation was measured by patent, trademark, and design applications. The findings showed that the general EU innovation efficiency situation has improved over time, meaning that each programming period was more successful than the previous one. On the other hand, visible disparities were found across the member states, showing that Luxembourg is an absolute innovation efficiency leader, while Greece and Portugal achieved the lowest average efficiency scores. Both the application of the DEA method and the gathered results may act as viable guidelines on how to improve R&I policies and select future investment directions.

Proposal of Industry 5.0-Enabled Sustainability of Product–Service Systems and Its Quantitative Multi-Criteria Decision-Making Method

“…Wang et al [46] integrated economic and environmental factors within supply chains to create a sustainability indicator and proposed a supply chain greenness assessment method based on the multi-regional inputoutput model (MRIO) and DEA. Additionally, Andrijauskiene et al [47] utilized DEA to evaluate the European Union's innovation efficiency from 2000 to 2020. Notably, Kuo and Kusiak [48] demonstrated that data-driven production research has transitioned from analytical models to data-driven approaches, with manufacturing and DEA emerging as the most popular application areas for these methodologies.…”