We are pleased to share that AUC Geographica was awarded an Impact Factor of 0.5 in the 2023 Journal Citation Reports™ released by Clarivate in June 2024. AUC Geographica ranks in Q3 in the field of Geography.
AUC Geographica (Acta Universitatis Carolinae Geographica) is a scholarly academic journal continuously published since 1966 that publishes research in the broadly defined field of geography: physical geography, geo-ecology, regional, social, political and economic geography, regional development, cartography, geoinformatics, demography and geo-demography.
AUC Geographica also publishes articles that contribute to advances in geographic theory and methodology and address the questions of regional, socio-economic and population policy-making in Czechia.
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Impact factor (JCR®): 0.5
Journal Citation Indicator (JCI): 0.20
Rank (JCI): Q3 in Geography
Scopus
Cite Score: 1.2
Rank (ASJC): Q3 in Geography, Planning and Development; Q3 in General Earth and Planetary Sciences
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AUC GEOGRAPHICA, Vol 58 No 2 (2023), 200–213
Related variety and state-sponsored R&D collaboration: a geographical and industrial analysis in Czechia
Petr Horák
DOI: https://doi.org/10.14712/23361980.2023.15
published online: 21. 11. 2023
abstract
This paper aims to explore the influence of related variety on direct state-supported R&D cooperation across various geographical levels to understand regional performance differentiation and economic base restructuring in Czechia by employing Frenken et al.’s (2007) methodological approach to calculate a related and unrelated variety for all NACE and NACE C-Manufacturing. Findings indicate that the city of Prague has the highest unrelated and related variety, followed by the cities of Brno, Ostrava, and Pilsen. Calculation just for C-Manufacturing changes the ordering significantly. Furthermore, intra-regional and extra-regional pairwise R&D cooperation in joint projects is calculated. The cluster analysis of Czech microregional data (SO ORP) reveals patterns such as emerging collaborators and collaboration powerhouses. Linear regression analyses established a strong positive association between R&D collaboration intensity and related variety, while a negative link was observed with unrelated variety. Similar relationships were observed in the manufacturing sector (NACE-C).
keywords: related variety; unrelated variety; cluster analysis; Czech microregional data (SO ORP); state-supported R&D collaboration
references (22)
1. Bathelt, H., Storper, M. (2023): Related Variety and Regional Development: A Critique, Economic Geography. CrossRef
2. Bishop, P., Gripaios, P. (2010): Spatial Externalities, Relatedness and Sector Employment Growth in Great Britain. Regional Studies 44(4), 443-454. CrossRef
3. Blažek, J., Marek, D., Květoň, V. (2016): The Variety of Related Variety Studies: Opening the Black Box of Technological Relatedness via Analysis of Inter-firm R&D Cooperative Projects. Papers in Evolutionary Economic Geography (online), http://econ.geo.uu.nl/peeg/peeg1611.pdf.
4. Bond-Smith, S., McCann, P. (2019): A Multi-sector Model of Relatedness, Growth and Industry Clustering. Journal of Economic Geography 5(20), 1145-1163. CrossRef
5. Boschma, R., Iammarino, S. (2009): Related Variety, Trade Linkages, and Regional Growth in Italy. Economic Geography 85(3), 289-311. CrossRef
6. Boschma, R., Minondo, A., Navarro, M. (2013): The Emergence of New Industries at the Regional Level in Spain: A Proximity Approach Based on Product Relatedness. Economic Geography 89(1), 29-51. CrossRef
7. Castaldi, C., Frenken, K., Los, B. (2015): Related Variety, Unrelated Variety and Technological Breakthroughs: An analysis of US State-Level Patenting. Regional Studies 49(5), 767-781. CrossRef
8. Corradini, C., Vanino, C. (2022): Path dependency, regional variety and the dynamics of new firm creation in rooted and pioneering industries, Journal of Economic Geography 22(3), 631-651. CrossRef
9. Ebersberger, B., Herstad, S. J., Koller, C. (2014): Does the composition of regional knowledge bases influence extra-regional collaboration for innovation? Applied Economics Letters 21(3), 201-204. CrossRef
10. Frenken, K., Boschma, R. (2007): A theoretical framework for Evolutionary Economic Geography: Industrial dynamics and urban growth as a branching process. Journal of Economic Geography 7(5), 635-649. CrossRef
11. Frenken, K., van Oort, F., Verburg, T. (2007): Related variety, unrelated variety and regional economic growth. Regional Studies 41(5), 685-697. CrossRef
12. Grillitsch, M., Asheim, B., Trippl, M. (2018): Unrelated knowledge combinations: the unexplored potential for regional industrial path development. Cambridge Journal of Regions, Economy and Society 11(2), 257-274. CrossRef
13. Hartigan, J. A., Wong, M. A. (1979): Algorithm AS 136: A K-Means Clustering Algorithm. Journal of the Royal Statistical Society. Series C (Applied Statistics) 28(1), 100-108. CrossRef
14. Hartog, M., Boschma, R., Sotarauta, M. (2012): The Impact of Related Variety on Regional Employment Growth in Finland 1993-2006: High-Tech versus Medium/Low-Tech. Industry and Innovation 19 (6), 459-476. CrossRef
15. Ismkhan, H. (2017): An initialization method for the k-means using the concept of useful nearest centers. Journal of Advanced Engineering Research and Science. CrossRef
16. Květoň, V., Novnotný, J., Blažek, J., Marek, D. (2022): The role of geographic and cognitive proximity in knowledge networks: The case of joint R&D projects. Papers in Regional Science 101(2), 351-372. CrossRef
17. Květoň, V., Šafr, K. (2019): Regional embeddedness, relatedness and inter-regional linkages among less developed regions in Central Europe. European Planning Studies 27(5), 862-884. CrossRef
18. MacQueen, J. (1967): Some methods for classification and analysis of multivariate observations. In Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, eds L. M. Le Cam, J. Neyman 1, 281-297. Berkeley, CA: University of California Press.
19. Nooteboom, B. (2000): Learning by Interaction: Absorptive Capacity, Cognitive Distance and Governance. Journal of Management and Governance 4, 69-92. CrossRef
20. Virmani, D., Taneja, S., Malhotra, G. (2015): Normalization based K means Clustering Algorithm. Journal of Advanced Engineering Research and Science. CrossRef
21. Yeon, J., Jun, B. (2022). The spillover effect of neighboring port on regional industrial diversification and regional economic resilience. General Economics. CrossRef
22. Yeung, H. (2020): Regional worlds: from related variety in regional diversification to strategic coupling in global production networks. Regional Studies 55(6), 989-1010. CrossRef
Related variety and state-sponsored R&D collaboration: a geographical and industrial analysis in Czechia is licensed under a Creative Commons Attribution 4.0 International License.
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ISSN: 0300-5402
E-ISSN: 2336-1980
