Background and purpose: The transformation to Industry 4.0 increases the number of robots installed within industries, which brings great shifts in industrial ecosystems. For this reason, our research goal was to analyze the key performance indicators to investigate the economic and social sustainability of the changes in production.Methodology: The combination of official (World Bank, U.S. Bureau of Labor Statistics) and publicly available (Federal Reserve Economic Data, Industrial Federation of Robotics) data was used for statistical data processing, including comparison, correlation, cross-correlation and vector autoregression analysis, to present the past developments and also to predict future trends within the U.S. manufacturing sector.Results: In contrast to robust industry robotization observed in the 2008–2018 period, the share of manufacturing output and employment declined. Nonetheless, the vector autoregression model forecast shows, that the U.S. manufacturing sector has arrived at a turning point, after which robotization can increase employment and labor productivity of workers, while also stimulating further growth of their education levels.Conclusion: The transition to Industry 4.0 has a major impact on increasing demands for new knowledge and skills for increased productivity. Accordingly, forecasted growths of analyzed manufacturing indicators suggest that negative impacts of robotization in the recent past were only temporary, due to the entrance to the Industry 4.0 era. Nonetheless, additional policies to support sustainable industry development are required.
In 2004, the European Commission implemented the Decision No 1608/2003/EC of the European Parliament and of the Council concerning the production and development of Community statistics on innovation. This triggered the awareness of the role of innovation and R&D on national and European level and thus the opportunity to step towards in-depth monitoring innovation performance through various indicators. The paper aims to investigate the trends in the selected innovation indicators (i.e., public funding, expenditures and innovation activities, types of innovation and products introduced, hampered innovation activities) to outline the development direction on the enterprise level using the Community innovation survey data for the 2002–2016 period. Using the basic time series analysis, the paper evaluates the progress according to the European Strategy on research and innovation. Furthermore, using the autocorrelation and autoregression methods, the paper also outlines the future direction in innovation performance on European level.
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