Regional Innovation System in the Podkarpackie against selected Polish and EU regions

Abstract

Innovation and innovativeness should be seen as a vast and internally complex set of measures carried out to improve management efficiency, build strong competitive positions and obtain economic benefits from enterprises, national economies and societies. A possibility of achieving these benefits is the category of competitiveness, hence, relative position of a region is measured using various indicators. The aim of the study is to analyze the level of the selected RIS indicators of the Podkarpackie against selected EU regions. The study will make it possible to understand in which area Podkarpackie performs better and in which worse? The analysis is carried out at the regional level NUTS 2. The most important approach in which quantitative indicators have been used comprehensively is EIS, which makes it possible to compare the Podkarpackie with the selected regions of Poland and the EU. Seven regions were selected for our analysis: 1 in Denmark, Germany, the United Kingdom, Czech Republic and 3 in Poland. When it comes to innovativeness, the Podkarpackie region is a Moderate Innovator. The results of our research show that the relative strengths in the regional innovation system are design applications, sales new-tomarket/firm innovations, R&D expenditures business sector, non-R&D innovation expenditures, and tertiary education. Relative weaknesses are: marketing or organizational innovations, and also life-long learning.

Analyzing the root of regional innovation performance in the European territory

“…Thus, it is necessary to investigate which variables have the greatest impact on the classification of regions, with regard to the performance of innovation and not only build rankings of innovation level (Szopik-Depczyńska et al , 2020). Being so, the results of the present paper are complementary to other researches, such as the ones by Arbolino et al (2019); Delgado-Márquez and García-Velasco (2018); Garcia-Bernabeu et al (2020); Lewandowska and Švihlíková (2020); Lopes et al (2018b, 2019a); Saftescu et al (2016); and Szopik-Depczyńska et al (2020).…”

“…For moderate regions to increase their innovative performance, and possibly becoming strong regions, the results suggest that they should implement measures in the EPO patent applications and innovative SMEs collaborating with other variables, as there is a great disparity in these variables comparing the two regions in particular. Despite the efforts of regional policymakers to strengthen public funds for innovation, they are still not enough, it is recommended that they should be strengthened (Lewandowska and Švihlíková, 2020; Stanculescu, 2015). Being so, for moderate regions, it is important to reinforce tailored consultancy, which can be done by higher education institutions, or by specialized companies, to increase financial support from regional, specialized and public organizations.…”

The Role of Non-R&D Expenditures in Promoting Innovation in Europe

“…Finally, in the breakdown of the effects that promote non-R&D innovation, the author verifies the predominant role of human capital and remittances from abroad. [9] verify the existence of a positive role of non-R & D innovation expenditures in promoting innovation in the European regions of NUTS2. [10] compares the performance and characteristics of technological innovation of firms investing in R&D versus firms investing in non-R&D innovation.…”

Data-Driven Coupling Coordination Development of Regional Innovation EROB Composite System: An Integrated Model Perspective

“…T is the comprehensive development coefficient of the four subsystems, and a, b, c, d are the regulation coefficients. Some scholars [34][35][36] pointed out that the innovation environment, innovation resource, innovation output, and innovation benefit are equally important to the development of RISs, so we let a = b = c = d = 0.25. D is the coupling coordination scheduling, which indicates the coupling coordination development degree of the four subsystems, and the value is within the interval [0, 1].…”

“…Zhang et al [34] divided RIS into innovation demand and innovation supply subsystems. Lewandowska and Švihlíková [35] constructed an RIS from five dimensions: population with tertiary education, R&D expenditure in the public sector, R&D expenditure in the business sector, EPO pattern applications, and employment in knowledge-intensive activities. Weck et al [36] built an RIS framework based on knowledge collaboration, knowledge sharing, and knowledge management.…”