Abstract
Bridging Technologies connect otherwise unrelated fields in regional knowledge spaces. By establishing new connections between technologies, they enable technological development through processes of recombinant innovation. In this paper, we develop a set of indicators that help us to characterise technologies in terms of their bridging function and study their evolution over time. We apply these tools to the regional and national levels in Germany. Our findings indicate that large patenting regions are not necessarily the ones that embed most new technologies in their knowledge space. For the German knowledge space we find that during the past two decades, it became less dependent on prominent technologies, such as transport, machinery and chemicals. Changes in the German knowledge space in terms of the development of new bridging technologies can be attributed to a regionally dispersed process rather than one driven by single regions.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data Availability
The data that support the findings of this study are available from the corresponding author upon request.
Notes
When we use the term “bridging technologies”, we are referring to technologies present in the KS that perform a bridging function.
Y10S and Y10T are special classes that include many different technological fields due to the harmonization between IPC and USPC classifications.
Since the regional KS is the difference between regional and world relatedness, it consists of edges with positive and negative weights. In the calculation of BC, we implement a linear transformation for the edge weights (\(\max (C_{ij}) - C_{ij} + 1\)) to have strictly positive weights.
In Appendix A, we display the heat map with betweenness based on the co-occurrence matrix. Since the results are quite similar, the larger turbulence is not caused by the method of reconstructing the KS but by the methodology used to identify BTs.
Appendix B shows the results for the KS of Jena using degree centrality. The results are similar in trends to BC even if some differences appear. Therefore, a node has a higher chance of being in the shortest path of two other nodes the more connections it has. Overall, these results reinforce the fact that our measures (BI and BC) are actually able to capture the bridging function of technologies.
The BioRegio contest was one of the first national innovation oriented cluster policies in Germany with the goal to identify and strengthen regions with strong capabilities in Biotechnology.
For further information a data visualization tool has been developed. This includes different methodologies to spot technologies with cross-fertilization potential in knowledge spaces. The tool is available at the following link: https://techspace.shinyapps.io/TechSpaceApp/
References
Abernathy W, Clark K (1985) Innovation: Mapping the winds of creative destruction. Res Policy 14(1):3–22. https://doi.org/10.1016/0048-7333(93)90040-o
Acharya VV, Hasan I, Saunders A (2006) Should banks be diversified? Evidence from individual bank loan portfolios. J Bus, 79(3):1355–1412. https://doi.org/10.1086/500679
Acs ZJ, Anselin L, Varga A (2002) Patents and innovation counts as measures of regional production of new knowledge. Res policy 31(7):1069–1085
Ahuja G, Lampert CM (2001) Entrepreneurship in the large corporation: A longitudinal study of how established firms create breakthrough inventions. Strat Manag J, 22(6–7):521–543
Akyildiz IF, Nie S, Lin SC, Chandrasekaran M (2016) 5G roadmap: 10 key enabling technologies. Comput Netw, 106:17–48. https://doi.org/10.1016/j.comnet.2016.06.010
Alstott J, Triulzi G, Yan B, Luo J (2017) Mapping technology space by normalizing patent networks. Scientometrics 110(1):443–479. https://doi.org/10.1007/s11192-016-2107-y
Arthur WB (1989) Competing technologies, increasing returns, and lock-in by historical events. Econ J, 99(394):116–131
Arthur WB, et al (1994) Increasing returns and path dependence in the economy. University of michigan Press
Arts S, Veugelers R (2015) Technology familiarity, recombinant novelty, and breakthrough invention. Ind Corp Chang, 24(6):1215–1246
Balland PA (2016) Relatedness and the geography of innovation. Handbook on the geographies of innovation, p 127–141
Balland PA, Boschma R (2021) Complementary interregional linkages and smart specialisation: An empirical study on european regions. Reg Stud, 55(6):1059–1070
Balland PA, Boschma R, Crespo J, Rigby DL (2019) Smart specialization policy in the European Union: relatedness, knowledge complexity and regional diversification. Reg Stud, 53(9):1252–1268. https://doi.org/10.1080/00343404.2018.1437900
Basilico S, Cantner U, Graf H (2022) Policy influence in the knowledge space: A regional application. J Technol Transf
Bavelas A (1948) A mathematical model for group structures. Hum Organ, 7(3):16–30
Boschma R (2017) Relatedness as driver of regional diversification: A research agenda. Reg Stud, 51(3):351–364
Boschma R, Balland PA, Kogler DF (2014) Relatedness and technological change in cities : the rise and fall of technological knowledge in US metropolitan areas from 1981 to 2010. Ind Corp Chang, 24(1):223–250. https://doi.org/10.1093/icc/dtu012
Boschma R, Minondo A, Navarro M (2012) Related variety and regional growth in Spain. Pap Reg Sci, 91(2):241–256. https://doi.org/10.1111/j.1435-5957.2011.00387.x
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. Econ Geogr, 89(1):29–51. https://doi.org/10.1111/j.1944-8287.2012.01170.x
Breschi S, Lissoni F, Malerba F (2003) Knowledge-relatedness in firm technological diversification. Res Policy, 32(1):69–87. https://doi.org/10.1016/s0048-7333(02)00004-5
Bresnahan TF (2010) General Purpose Technologies. In Handbook of the Economics of Innovation, 2:761–791 Elsevier https://doi.org/10.1016/S0169-7218(10)02002-2
Bresnahan TF, Trajtenberg M (1995) General purpose technologies ‘Engines of growth’? J Econ, 65(1):83–108. https://doi.org/10.1016/0304-4076(94)01598-T
Cantner U, Graf H (2006) The network of innovators in jena: An application of social network analysis. Res Policy, 35(4):463–480
Cantner U, Kalthaus M, Yarullina I (2022) Outcomes of science-industry collaboration: Factors and interdependencies. Technical report, Jena Economic Research Papers
Cantner U, Vannuccini S (2017) Pervasive technologies and industrial linkages: Modeling acquired purposes. Struct Chang Econ Dyn
Corradini C, De Propris L (2017) Beyond local search: Bridging platforms and inter-sectoral technological integration. Res Policy, 46(1):196–206
Csardi G, Nepusz T (2006) The igraph software package for complex network research. InterJournal, Complex Systems, 1695. Available at http://igraph.org
Degroote B, Held P (2018) Analysis of the patent documentation coverage of the cpc in comparison with the ipc with a focus on asian documentation. World Patent Inf, 54:S78–S84
Dohse D (2000) Technology policy and the regions-the case of the BioRegio contest. Res Policy, 29(9):1111–1133. https://doi.org/10.1016/s0048-7333(99)00077-3
Dosi G (1982) Technological paradigms and technological trajectories. A suggested interpretation of the determinants and directions of technical change. Res Policy, 11(3):147–162 https://doi.org/10.1016/0048-7333(82)90016-6
Duranton G, Puga D (2000) Diversity and specialisation in cities: Why, where and when does it matter? Urban Stud, 37(3):533–555
European Commission (2003) Third European Report on Science & Technology Indicators: Towards a Knowledge-based Economy. Office for Official Publications of the European Communities, https://op.europa.eu/s/n27w
European Commission (2009) Preparing for our future: Developing a common strategy for key enabling technologies in the EU. https://eur-lex.europa.eu/legal-content/EN/ALL/?uri=CELEX%3A52009DC0512
Fleming L (2001) Recombinant uncertainty in technological search. Manag Sci, 47(1):117–132
Fleming L, Sorenson O (2001) Technology as a complex adaptive system: evidence from patent data. Res policy, 30(7):1019–1039
Freeman LC (1978) Centrality in social networks conceptual clarification. Soc Networks, 1(3):215–239
Fritsch M, Franke G (2004) Innovation, regional knowledge spillovers and r &d cooperation. Res policy, 33(2):245–255
Fritsch M, Graf H (2010) How National Conditions Affect Regional Innovation Systems–The Case of the Two Germanys. Jena Economic Research Papers 2010-054, Friedrich-Schiller-University Jena, Max-Planck-Institute of Economics
Fritsch M, Zoellner M (2019) The fluidity of inventor networks. J Technol Transf, 45(4):1063–1087
Graf H (2006) Networks in the Innovation Process: Local and Regional Interactions. Edward Elgar, Cheltenham, UK
Graf H (2012) Inventor networks in emerging key technologies: information technology vs. semiconductors. Journal of Evolutionary Economics, 22(3):459–480 https://doi.org/10.1007/s00191-011-0248-y
Graf H (2017) Regional innovator networks: A review and an application with R. Technical report, Jena Economic Research Papers. http://www2.wiwi.uni-jena.de/Papers/jerp2017/wp_2017_016.pdf
Graf H, Broekel T (2020) A shot in the dark? policy influence on cluster networks. Res Policy, 49(3):103920
Graf H, Henning T (2009) Public research in regional networks of innovators: A comparative study of Four East German regions. Reg Stud, 43(10):1349–1368 https://doi.org/10.1080/00343400802251460
Graf H, Menter M (2020) Public research and the quality of inventions: the role and impact of entrepreneurial universities and regional network embeddedness. Jena Economic Research Papers 2020-011, Friedrich Schiller University Jena
Griliches Z (1990) Patent Statistics as Economic Indicators: A Survey. J Econ Lit, 28(4):1661–1707 http://www.jstor.org/stable/2727442
Hausmann R, Hidalgo CA (2011) The network structure of economic output. J Econ Growth, 16(4):309–342
Hausmann R, Klinger B (2007) The structure of the product space and the evolution of comparative advantage. Technical Report 146, Center for International Development at Harvard University
Henderson RM, Clark KB (1990) Architectural innovation: The reconfiguration of existing product technologies and the failure of established firms. Adm Sci Q, p 9–30
Hidalgo CA, Klinger B, Barabasi AL, Hausmann R (2007) The Product Space Conditions the Development of Nations. Sci, 317(5837):482–487. https://doi.org/10.1126/science.1144581
Jaffe AB (1989) Characterizing the “technological position” of firms, with application to quantifying technological opportunity and research spillovers. Res Policy 18(2):87–97. https://doi.org/10.1016/0048-7333(89)90007-3
Joo SH, Kim Y (2010) Measuring relatedness between technological fields. Scientometrics 83(2):435–454. https://doi.org/10.1007/s11192-009-0108-9
Jürgens B, Herrero-Solana V (2017) Monitoring nanotechnology using patent classifications: an overview and comparison of nanotechnology classification schemes. J Nanoparticle Res, 19(4):1–7
Kay L, Newman N, Youtie J, Porter AL, Rafols I (2014) Patent Overlay Mapping: Visualizing Technological Distance. J Assoc Inf Sci Technol, 65(12):2432–2443. https://doi.org/10.1002/asi.23146
Khan M, Dernis H (2006). Global overview of innovative activities from the patent indicators perspective. https://doi.org/10.1787/674714465672
Kim DJ, Kogut B (1996) Technological platforms and diversification. Organ Sci, 7(3):283–301
Kodama F (1992) Technology fusion and the new r &d. Harvard business review, p 70–78
Kogler DF, Rigby DL, Tucker I (2013) Mapping knowledge space and technological relatedness in US cities. Eur Plan Stud, 21(9):1374–1391. https://doi.org/10.1080/09654313.2012.755832
Kogut B, Zander UB (2000) Did Socialism Fail to Innovate? A Natural Experiment of the Two Zeiss Companies American Sociological Review 65(2):169. https://doi.org/10.2307/2657436
Lee JS, Hsiang J (2020) Patent classification by fine-tuning bert language model. World Patent Inf, 61:101965
Leydesdorff L, Kogler DF, Yan B (2017) Mapping patent classifications: portfolio and statistical analysis, and the comparison of strengths and weaknesses. Scientometrics 112(3):1573–1591. https://doi.org/10.1007/s11192-017-2449-0
Leydesdorff L, Kushnir D, Rafols I (2014) Interactive overlay maps for US patent (USPTO) data based on International Patent Classification (IPC). Scientometrics 98:1583–1599. https://doi.org/10.1007/s11192-012-0923-2
Malerba F, Orsenigo L (1997) Technological Regimes and Sectoral Patterns of Innovative Activities. Ind Corp Chang, 6(1):83–118 https://doi.org/10.1093/icc/6.1.83
March JG (1991) Exploration and exploitation in organizational learning. Organ Sci, 2(1):71–87
Montresor S, Quatraro F (2017) Regional branching and Key enabling technologies: Evidence from European patent data. Econ Geogr, 93(4):367–396. https://doi.org/10.1080/00130095.2017.1326810
Neffke F, Henning M, Boschma R (2011) How Do Regions Diversify over Time? Industry Relatedness and the Development of New Growth Paths in Regions. Econ Geogr, 87(3):237–265. https://doi.org/10.1111/j.1944-8287.2011.01121.x
Neffke F, Henning MS (2008) Revealed Relatedness: Mapping Industry Space. Papers in Evolutionary Economic Geography (PEEG) 0819, Utrecht University, Department of Human Geography and Spatial Planning, Group Economic Geography. https://ideas.repec.org/p/egu/wpaper/0819.html
Nelson RR, Winter SG (1982) An evolutionary theory of economic change. Harvard University, Cambridge
Ochiai A (1957) Zoogeographical studies on the soleoid fishes found in japan and its neighbouring regions-i. Bull Jpn Soc Scient Fish, 22:522–525
Pavitt K (1984) Sectoral patterns of technical change: Towards a taxonomy and a theory. Res Policy, 13(6):343–373. https://doi.org/10.1016/0048-7333(84)90018-0
Pavitt K (1985) Patent statistics as indicators of innovative activities: Possibilities and problems. Scientometrics 7(1–2):77–99. https://doi.org/10.1007/BF02020142
Posada J, Toro C, Barandiaran I, Oyarzun D, Stricker D, de Amicis R, Pinto EB, Eisert, P, Döllner J, Vallarino I (2015) Visual Computing as a Key Enabling Technology for Industrie 4.0 and Industrial Internet. IEEE Comput Graph Appl, 35(2):26–40 https://doi.org/10.1109/mcg.2015.45
Quatraro F (2010) Knowledge coherence, variety and economic growth: Manufacturing evidence from Italian regions. Res, Policy 39(10):1289–1302. https://doi.org/10.1016/j.respol.2010.09.005
R Core Team (2018) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. Available online at https://www.R-project.org/
Rhoades SA (1993) The herfindahl-hirschman index. Federal Reserve Bulletin, 79(3):188–189 https://heinonline.org/HOL/Page?handle=hein.journals/fedred79 &div=37 &g_sent=1 &casa_token= &collection=journals#
Scheu M, Veefkind V, Verbandt Y, Galan EM, Absalom R, Förster W (2006) Mapping nanotechnology patents: The EPO approach. World Patent Inf, 28(3):204–211. https://doi.org/10.1016/j.wpi.2006.03.005
Schmoch U (2008) Concept of a technology classification for country comparisons. Final report to the world intellectual property organisation (wipo), WIPO. http://publica.fraunhofer.de/documents/N-114385.html
Schoenmakers W, Duysters G (2010) The technological origins of radical inventions. Res Policy, 39(8):1051–1059
Schumpeter JA (1934) The Theory of Economic Development. Harvard University Press
Scott AJ (2006) Entrepreneurship, innovation and industrial development: Geography and the creative field revisited. Small Bus Econ, 26(1):1–24. https://doi.org/10.1007/s11187-004-6493-9
Shaw ME (1954) Group structure and the behavior of individuals in small groups. J Psychol, 38(1):139–149
Soete L (1987) The impact of technological innovation on international trade patterns: The evidence reconsidered. Res Policy, 16(2):101–130. https://doi.org/10.1016/0048-7333(87)90026-6
Squicciarini M, Dernis H, Criscuolo C (2013) Measuring Patent Quality: Indicators of Technological and Economic Value. Technical report, OECD Publishing, Paris. https://doi.org/10.1787/5k4522wkw1r8-en
Stiroh KJ (2004) Do community banks benefit from diversification? J Financ Serv Res, 25(2–3):135–160. https://doi.org/10.1023/b:fina.0000020657.59334.76
Teece DJ, Rumelt R, Dosi G, Winter S (1994) Understanding corporate coherence: Theory and evidence. J Econ Behav & Organ 23(1):1–30. https://doi.org/10.1016/0167-2681(94)90094-9
Veefkind V, Hurtado-Albir J, Angelucci S, Karachalios K, Thumm N (2012) A new EPO classification scheme for climate change mitigation technologies. World Patent Inf, 34(2):106–111. https://doi.org/10.1016/j.wpi.2011.12.004
Verbeek A, Debackere K, Luwel M, Andries P, Zimmermann E, Deleus F (2002) Linking science to technology: Using bibliographic references in patents to build linkage schemes. Scientometrics 54(3):399–420. https://doi.org/10.1023/A:1016034516731
Wassermann S, Faust K (1994) Social Network Analysis: Methods and Applications. Cambridge University Press, Cambridge. https://doi.org/10.1017/CBO9780511815478
Weitzman ML (1998) Recombinant growth. Q J Econ, 113(2):331–360
Yan B, Luo J (2017) Measuring Technological Distance for Patent Mapping. J Assoc Inf Sci Technol, 68(2):423–437 https://doi.org/10.1002/asi.23664
Acknowledgements
The authors would like to thank the participants of the 2nd Rethinking Clusters workshop in Padua (Italy) and the 2019 EMAEE conference in Brighton (UK) for useful comments. Furthermore, the authors are glad for helpful comments and discussions with Uwe Cantner, Martin Kalthaus, Simone Vannuccini, Philip Dörr, Indira Yarullina and the TechSpace project members on earlier versions of this paper. All remaining errors are our own.
Funding
The research leading to these results received funding from the German Federal Ministry of Education and Research (BMBF), under Grant Agreement No. 16IFI017.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflicts of interest
The authors have no competing interests to declare that are relevant to the content of this article.
Ethical Conduct
This article does not contain any studies involving human participants performed by any of the authors.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Appendices
A Betweenness based on co-occurrences
In this appendix, we present our results for Betweenness Centrality if we use the simple Co-occurrence matrix instead of Revealed Relatedness. Overall, the results are quite similar despite some small differences in the technology rankings.
Node Betweenness Centrality on Co-occurrence matrix ranking in Jena (1990-2015)
B Degree centrality
In this appendix, we present our results for simple Degree Centrality if we use Revealed Relatedness. Overall, the results are quite similar to the ones for the Bridging Index despite some small differences in the technology rankings.
Degree Centrality on Revealed Relatedness matrix ranking in Jena (1990-2015)
C Schmoch Classification
The work of Schmoch (2008) on the classification of industrial activities is based on the IPC classification. To make it suitable for the CPC classification, it is necessary to make some assumptions. First, we created a new technological class denoted Miscellaneous in which all CPC4 classes not present in the IPC classification are subsumed. These are mainly the ones of the Y class. Considering CPCs at a lower level than 4 digits, it is possible to identify some classes that are present in some different technological classifications. In this case, we opted to select the Schmoch technological field that is more represented (has the highest number of patents) in that specific CPC4 class. A61K is mostly in field 16 Pharmaceuticals, but A61K-008 is in 14 Organic fine chemistry, H04N is mainly in class 2 Audio-visual technology, but also in 3 Telecommunications and 4 Digital communication, G01N is mainly in 10 Measurement but also with G01N-033 in 11 Analysis of biological materials, finally B01D is both in 23 Chemical engineering and 24 Environmental technology. We decided to keep all CPC4 classes in one technological field, the one that had the highest presence of patents worldwide. So, A61K was assigned to Pharmaceuticals, H04N to Audio-visual technology, G01N to Measurement and B01D to Chemical engineering. Another factor to take into consideration is that the 4 digit CPC class is also identified in IPC but the correspondence at a lower level of classification (6 or 10 digits) tends to differ. In these cases, we assume that CPC4 is exactly the same as IPC4 to simplify calculations. Since the intention is to have some indications on the dominant technologies and their evolution, the slight differences when passing from IPC to CPC are not taken into account.
D LMR Abbreviations
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Basilico, S., Graf, H. Bridging technologies in the regional knowledge space: measurement and evolution. J Evol Econ 33, 1085–1124 (2023). https://doi.org/10.1007/s00191-023-00832-8
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00191-023-00832-8