PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
2023 | No. 59 | 111--130
Tytuł artykułu

Knowledge Input and Innovation in Visegrad Group (V4) Regions: A Spatial Econometric Approach

Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
This paper argues that one of the reasons why innovation in one country leaves another behind could be its spatial geography. Questions relevant to R&D development and technological change are raised on how knowledge inputs affect innovation in the Visegrad Group (V4) (Czech Republic, Hungary, Poland, and Slovakia) and how these factors are spatially dependent. The study results show that regional knowledge inputs (R&D expenditure and R&D personnel) play an essential role in innovation development in Visegrad Group (V4). The study findings also emphasize the importance of R&D funding support in the public sector and R&D personnel capabilities in promoting innovation. This paper intends to make an initial contribution to innovation studies taking regions of Visegrad Group (V4) as the analyzed object and suggests the development of spatial modeling using more up-to-date data to yield more reliable and in-depth results.(original abstract)
Rocznik
Numer
Strony
111--130
Opis fizyczny
Twórcy
  • University of Pécs, Pécs, Hungary
Bibliografia
  • Acs, Z.J., Anselin, L. & Varga, A. (2002). Patents and innovation counts as measures of regional production of new knowledge. Research Policy, 31(7): 1069-1085.
  • Agasisti, T., Barra, C. & Zotti, R. (2019). Research, knowledge transfer, and innovation: The effect of Italian universities' efficiency on local economic development 2006- 2012. Journal of Regional Science, 59(5): 819-849.
  • Ali, M.A. (2021). Modeling regional innovation in Egyptian governorates: Regional knowledge production function approach. Regional Science Policy & Practice. DOI: https://doi.org/10.1111/rsp3.12450.
  • Anselin, L. (1988). Spatial econometrics: methods and models (Vol. 4). Springer Science & Business Media.
  • Anselin, L. (2005). Exploring spatial data with GeoDaTM: a workbook. Center for Spatially Integrated Social Science, 165-223.
  • Anselin, L. & Florax, R. (2012). New directions in spatial econometrics. Springer Science & Business Media.
  • Anselin, L., Syabri, I., & Kho, Y. (2009). GeoDa: an introduction to spatial data analysis. In: Handbook of applied spatial analysis: Software tools, methods and applications, 73-89. Berlin: Springer Heidelberg.
  • Anselin, L., Varga, A. & Acs, Z. (1997). Local geographic spillovers between university research and high technology innovations. Journal of Urban Economics, 42(3): 422-448.
  • Archibugi, D. (1992). Patenting as an indicator of technological innovation: a review. Science and Public Policy, 19(6): 357-368.
  • Asheim, B.T., Grillitsch, M. & Trippl, M. (2016). Regional innovation systems: Past-present-future. Handbook on the Geographies. Available at: https://www.elgaronline.com/view/edcoll/9781784710767/9781784710767.00010.xml.
  • Autant-Bernard, C. (2001). Science and knowledge flows: evidence from the French case. Research Policy, 30(7): 1069-1078.
  • Bednář, P. & Halásková, M. (2018). Innovation performance and R&D expenditures in Western European regions: Divergence or convergence? Journal of International Studies, 11(1): 210-224. DOI: 10.14254/2071-8330.2018/11-1/16.
  • Benedetti, R., Palma, D. & Postiglione, P. (2020). Modeling the impact of technological innovation on environmental efficiency: a spatial panel data approach. Geographical Analysis, 52(2): 231-253. DOI: https://doi.org/10.1111/gean.12198.
  • Benneworth, P., Coenen, L., Moodysson, J. & Asheim, B. (2009). Exploring the multiple roles of Lund University in strengthening Scania's regional innovation system: towards institutional learning? European Planning Studies, 17(11): 1645-1664. DOI: https://doi.org/10.1080/09654310903230582.
  • Benneworth, P. & Hospers, G.-J. (2007). The new economic geography of old industrial regions: Universities as global-local pipelines. Environment and Planning C: Government and Policy, 25(6): 779-802.
  • Bivand, R.S. & Wong, D.W.S. (2018). Comparing implementations of global and local indicators of spatial association. Test, 27(3): 716-748. DOI: https://doi.org/10.1007/s11749-018-0599-x.
  • Blažek, J. & Csank, P. (2016). Can emerging regional innovation strategies in less developed European regions bridge the main gaps in the innovation process? Environment and Planning C: Government and Policy, 34(6): 1095-1114.
  • Brown, P., Bocken, N. & Balkenende, R. (2020). How do companies collaborate for circular oriented innovation? Sustainability, 12(4): 1648. DOI: https://doi.org/10.3390/su12041648.
  • Buesa, M., Heijs, J. & Baumert, T. (2010). The determinants of regional innovation in Europe: A combined factorial and regression knowledge production function approach. Research Policy, 39(6): 722-735. DOI: https://doi.org/10.1016/j.respol.2010.02.016.
  • Cai, Y. & Hu, Z. (2022). Energy consumption in China: Spatial effects of industrial concentration, localization, and diversity. Science of the Total Environment, 852: 158568.
  • Caniëls, M.C.J. (2000). Knowledge spillovers and economic growth: regional growth differentials across Europe. Edward Elgar Publishing.
  • Capello, R. (2002). Spatial and sectoral characteristics of relational capital in innovation activity. European Planning Studies, 10(2): 177-200.
  • Cooke, P. (2001). Regional innovation systems, clusters, and the knowledge economy. Industrial and Corporate Change, 10(4): 945-974.
  • Dai, L., Derudder, B., Cao, Z. & Ji, Y.F. (n.d.). Examining the evolving structures of intercity knowledge networks: the case of scientific collaboration in China. International Journal of Urban Sciences. DOI: https://doi.org/10.1080/12265934.2022.2042365.
  • de La Tour, A., Glachant, M. & Ménière, Y. (2011). Innovation and international technology transfer: The case of the Chinese photovoltaic industry. Energy Policy, 39(2): 761-770.
  • Debarsy, N., Dossougoin, C., Ertur, C. & Gnabo, J.-Y. (2018). Measuring sovereign risk spillovers and assessing the role of transmission channels: A spatial econometrics approach. Journal of Economic Dynamics and Control, 87: 21-45.
  • Dosi, G. (1988). Sources, procedures, and microeconomic effects of innovation. Journal of Economic Literature, 26(3): 1120-1171.
  • Dubé, J. & Legros, D. (2014). Spatial autocorrelation. Spatial Econometrics Using Microdata, 59-91.
  • Espoir, D.K. & Ngepah, N. (2021). The effects of inequality on total factor productivity across districts in South Africa: a spatial econometric analysis. GeoJournal, 86(6): 2607-2638.
  • Filippetti, A. & Archibugi, D. (2011). Innovation in times of crisis: National Systems of Innovation, structure, and demand. Research Policy, 40(2): 179-192. https://www.sciencedirect.com/science/article/pii/S0048733310001794
  • Filippetti, A., Gkotsis, P., Vezzani, A. & Zinilli, A. (2020). Are innovative regions more resilient? Evidence from Europe in 2008-2016. Economia Politica, 37(3): 807-832.
  • Fischer, M.M. & Varga, A. (2003). Spatial knowledge spillovers and university research: Evidence from Austria. The Annals of Regional Science, 37(2): 303-322.
  • Fotheringham, A.S. (2009). "The problem of spatial autocorrelation" and local spatial statistics. Geographical Analysis, 41(4): 398-403.
  • Franchi, C., Cartabia, M., Risso, P., Mari, D., Tettamanti, M., Parabiaghi, A., Pasina, L., Djignefa Djade, C., Fortino, I. & Bortolotti, A. (2013). Geographical differences in the prevalence of chronic polypharmacy in older people: eleven years of the EPIFARM-Elderly Project. European Journal of Clinical Pharmacology, 69(7): 1477-1483.
  • Fritsch, M. (2002). Measuring the quality of regional innovation systems: A knowledge production function approach. International Regional Science Review, 25(1): 86-101.
  • Griliches, Z. (1979). Issues in Assessing the Contribution of Research and Development to. Bell Journal of Economics, 10: 92-116.
  • Gu, Y. & You, X. (2022). A spatial quantile regression model for driving mechanism of urban heat island by considering the spatial dependence and heterogeneity: An example of Beijing, China. Sustainable Cities and Society, 79: 103692.
  • Hájek, P. & Stejskal, J. (2018). R&D cooperation and knowledge spillover effects for sustainable business innovation in the chemical industry. Sustainability, 10(4): 1064.
  • Hong, J., Hong, S., Wang, L., Xu, Y. & Zhao, D. (2015). Government grants, private R&D funding and innovation efficiency in transition economy. Technology Analysis & Strategic Management, 27(9): 1068-1096.
  • Jaffe, A.B. (1989). Real effects of academic research. The American Economic Review, 79(5): 957-970.
  • Kirankabeş, M.C. & Erkul, A. (2019). Regional knowledge production in Central and East European countries: R&D factor productivity and changes in performances. Eastern Journal of European Studies, 10(1): 25-44.
  • Krammer, S.M.S. (2009). Drivers of national innovation in transition: Evidence from a panel of Eastern European countries. Research Policy, 38(5): 845-860.
  • Kravtsova, V. & Radosevic, S. (2012). Are systems of innovation in Eastern Europe efficient? Economic Systems, 36(1), 109-126. Available at: https://www.sciencedirect.com/science/article/pii/S0939362511000586.
  • LeSage, J. (2015). Spatial econometrics. In Handbook of research methods and applications in economic geography, 23-40. Edward Elgar Publishing.
  • Li, H., Calder, C.A. & Cressie, N. (2007). Beyond Moran's I: testing for spatial dependence based on the spatial autoregressive model. Geographical Analysis, 39(4): 357-375.
  • Liu, Y., Yan, Z., Cheng, Y.J. & Ye, X.T. (2018a). Exploring the Technological Collaboration Characteristics of the Global Integrated Circuit Manufacturing Industry. Sustainability, 10(1): 196. DOI: https://doi.org/10.3390/su10010196.
  • Liu, Y., Yan, Z., Cheng, Y.J. & Ye, X.T. (2018b). Exploring the Technological Collaboration Characteristics of the Global Integrated Circuit Manufacturing Industry. Sustainability, 10(1): 196. DOI: https://doi.org/10.3390/su10010196.
  • Loewen, B. & Schulz, S. (2019). Questioning the convergence of cohesion and innovation policies in Central and Eastern Europe. In: Regional and Local Development in Times of Polarisation, 121-148. Singapore: Palgrave Macmillan.
  • Lyke, A. (2018). Higher Education Hot Spots: Using Local Indicators of Spatial Association. New Directions for Institutional Research, 2018(180): 59-68.
  • Mansfield, E. (1995). Academic research underlying industrial innovations: sources, characteristics, and financing. The Review of Economics and Statistics, 77(1): 55-65.
  • Matkowski, Z., Prochniak, M. & Rapacki, R. (2016). Real income convergence between Central Eastern and Western Europe: Past, present, and prospects. Conference: 33rd CIRET Conference, September 14 - September 17, 2016. Copenhagen, Denmark.
  • McCann, P. & Ortega-Argilés, R. (2013). Transforming European regional policy: Smart specialisation and a results-driven agenda. Oxford Review of Economic Policy, 29(2): 405-431.
  • McCann, P. & Ortega-Argilés, R. (2014). Smart specialisation in European regions: Issues of strategy, institutions and implementation. European Journal of Innovation Management, 17(4): 409-427. DOI: https://doi.org/10.1108/EJIM-05-2014-0052.
  • Morisson, A. & Doussineau, M. (2019). Regional innovation governance and place-based policies: design, implementation and implications. Regional Studies, Regional Science, 6(1): 101-116.
  • Murzyn, D. (2020). Smart growth in less developed regions-the role of EU structural funds on the example of Poland. Innovation: The European Journal of Social Science Research, 33(1): 96-113.
  • Muscio, A., Reid, A. & Rivera Leon, L. (2015). An empirical test of the regional innovation paradox: can smart specialisation overcome the paradox in Central and Eastern Europe? Journal of Economic Policy Reform, 18(2): 153-171.
  • Naveed, A. & Ahmad, N. (2016). Technology spillovers and international borders: a spatial econometric analysis. Journal of Borderlands Studies, 31(4): 441-461.
  • Popescu, D.I., Ceptureanu, S.I. Alexandru, A.A.M. & Ceptureanu, E. (2019). Relationships between Knowledge Absorptive Capacity, Innovation Performance and Information Technology. Case study: the Romanian Creative Industries SMEs. Studies in Informatics and Control, 28(4): 463-476. DOI: https://doi.org/10.24846/v28i4y201910.
  • Popescu, G.H. (2014). FDI and economic growth in Central and Eastern Europe. Sustainability, 6(11): 8149-8163.
  • Qin, X., Du, D. & Kwan, M.-P. (2019). Spatial spillovers and value chain spillovers: evaluating regional R&D efficiency and its spillover effects in China. Scientometrics, 119(2): 721-747.
  • Radosevic, S. (1999). Transformation of science and technology systems into systems of innovation in central and eastern Europe: the emerging patterns and determinants. Structural Change and Economic Dynamics, 10(3-4): 277-320.
  • Radosevic, S. (2012). Innovation policy studies between theory and practice: a literature review based analysis. STI Policy Review, 3(1): 1-45.
  • Rodríguez-Pose, A. & Crescenzi, R. (2008). Research and development, spillovers, innovation systems, and the genesis of regional growth in Europe. Regional Studies, 42(1): 51-67.
  • Romer, P.M. (1990). Endogenous technological change. Journal of Political Economy, 98(5, II): S71-S102.
  • Sannigrahi, S., Pilla, F., Basu, B., Basu, A.S. & Molter, A. (2020). Examining the association between sociodemographic composition and COVID-19 fatalities in the European region using spatial regression approach. Sustainable Cities and Society, 62: 102418.
  • Shen, N. & Peng, H. (2021). Can industrial agglomeration achieve the emission-reduction effect? Socio-Economic Planning Sciences, 75: 100867.
  • Song, W., Wang, C., Chen, W., Zhang, X., Li, H. & Li, J. (2020). Unlocking the spatial heterogeneous relationship between Per Capita GDP and nearby air quality using bivariate local indicator of spatial association. Resources, Conservation and Recycling, 160: 104880.
  • Stojcic, N. (2021). Collaborative innovation in emerging innovation systems: Evidence from Central and Eastern Europe. Journal of Technology Transfer, 46(2): 531-562. DOI: https://doi.org/10.1007/s10961-020-09792-8.
  • Tao, R. & Chen, Y. (2022). Applying Local Indicators of Spatial Association to Analyze Longitudinal Data: The Absolute Perspective. Geographical Analysis, 0: 1-14.
  • Theeranattapong, T., Pickernell, D. & Simms, C. (2021). Systematic literature review paper: The regional innovation system-university-science park nexus. The Journal of Technology Transfer, 46(6): 2017-2050.
  • Trippl, M., Zukauskaite, E. & Healy, A. (2019). Shaping smart specialization: the role of place-specific factors in advanced, intermediate and less-developed European regions. Regional Studies, 54(10): 1328-1340. DOI: https://doi.org/10.1080/00343404.2019.1582763.
  • Vallance, P., Blažek, J., Edwards, J. & Květoň, V. (2018). Smart specialisation in regions with less-developed research and innovation systems: A changing role for universities? Environment and Planning C: Politics and Space, 36(2): 219-238.
  • Varga, A. (1999). Time-space patterns of US innovation: Stability or change? In: Innovation, networks and localities, 215-234. Berlin: Springer.
  • Varga, A. (2007). Localised knowledge inputs and innovation: The role of spatially mediated knowledge spillovers in Hungary. Acta Oeconomica, 57(1): 1-20.
  • Wang, C., Zhang, X., Ghadimi, P., Liu, Q., Lim, M.K. & Stanley, H.E. (2019). The impact of regional financial development on economic growth in Beijing-Tianjin-Hebei region: A spatial econometric analysis. Physica A: Statistical Mechanics and its Applications, 521: 635-648.
  • Wei, X., Liu, X. & Sha, J. (2019). How does the entrepreneurship education influence the students' innovation? Testing on the multiple mediation model. Frontiers in Psychology, 10: 1557. DOI: https://doi.org/10.3389/FPSYG.2019.01557/BIBTEX.
  • Yan, D., Lei, Y., Shi, Y., Zhu, Q., Li, L. & Zhang, Z. (2018). Evolution of the spatiotemporal pattern of PM2. 5 concentrations in China-A case study from the BeijingTianjin-Hebei region. Atmospheric Environment, 183: 225-233.
  • Yu, C., Zhang, Z., Lin, C. & Wu, Y.J. (2017). Knowledge creation process and sustainable competitive advantage: The role of technological innovation capabilities. Sustainability, 9(12): 2280. DOI: https://doi.org/10.3390/su9122280.
  • Zhan, C., Xie, M., Liu, J., Wang, T., Xu, M., Chen, B., Li, S., Zhuang, B. & Li, M. (2021). Surface ozone in the Yangtze River Delta, China: a synthesis of basic features, meteorological driving factors, and health impacts. Journal of Geophysical Research: Atmospheres, 126(6): e2020JD033600.
  • Zhang, G., Jia, Y., Su, B. & Xiu, J. (2021). Environmental regulation, economic development and air pollution in the cities of China: Spatial econometric analysis based on policy scoring and satellite data. Journal of Cleaner Production, 328: 129496
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.ekon-element-000171663158

Zgłoszenie zostało wysłane

Zgłoszenie zostało wysłane

Musisz być zalogowany aby pisać komentarze.
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.