Evolution and characteristics of Crossover Innovation Network of Emerging Technologies

a study based on patent data of the self-driving car technology

Authors

Keywords:

Emerging Technologies, Crossover Innovation Network, Social Network Analysis, Network Structure

Abstract

Along with the rapid development of a new round of scientific and technological revolution and industrial transformation, digital manufacturing, artificial intelligence, the Internet and other emerging fields marked by digitalization and intelligence are experiencing widespread penetration, cross-integration and constantly emerged crossover innovation. New technology development is a process of continuous increase of resource heterogeneity characterized by high fuzziness and uncertainty. However, it is difficult to meet the requirements of the development of emerging technologies only by the innovative resources and capabilities of the enterprises themselves. The limitation of innovation resources urges emerging technology enterprises to actively or passively exchange various innovation resources with other
innovation subjects across organizational, technological or industry boundaries, and promotes
the rapid rise of crossover innovation networks of emerging technologies, which has become an important way for emerging technology enterprises to avoid innovation risks and improve innovation
efficiency. To explore the crossover innovation network of emerging technologies and its evolution
path, this paper uses social network analysis to build the IPC co-occurrence network, patentee cooperative innovation network and patent citation network by stages by taking the invention patent data of self-driving car technology from 2006 to 2020 as samples, analyzes the subject cooperation, knowledge flow and technology convergence in the process of crossover innovation, explores the evolution process of crossover innovation network of emerging technologies and its characteristics in each stage, and then draws relevant enlightenment.

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References

Albert, R.; Barabási, A. Statistical mechanics of complex networks. Review of Modern Physics, v. 74, n. 1, p. 47-97, 2002.

Cantner, U.; Graf, H. The network of innovators in Jena: An application of social network analysis. Research Policy, v. 35, n. 4, p. 463-480, 2006.

Cantner, U.; Meder, A.; Wal, A. L. J. T. Innovator networks and regional knowledge base. Jena Economic Research Papers, v. 30, n. 9-10, p. 496 -507, 2010.

Choi, J., Sang-Hyun, A., Cha, M. S. The effects of network characteristics on performance of innovation clusters. Expert Systems with Applications, v. 40, n. 11, p. 4511-4518, 2013.

Choe, H. et al. Structural properties and inter- organizational knowledge flows of patent citation network: The case of organic solar cells. Renewable & Sustainable Energy Reviews, n. 55, p. 361-370, 2016.

Deeds, D. L.; Hill, C. W. L. An examination of opportunistic action within research alliances: Evidence from the biotechnology industry. Journal of Business Venturing, v. 14, n. 2, p. 141-163, 1999.

Day, G. S.; Schoemaker, P. J. H. Avoiding the pitfalls of emerging technologies. California Management Review, v. 42, n. 2, p. 8-33, 2000.

Dang, X. H.; Zheng, D. P. A further review on “The review of 17-year research literature on innovation network”: the definition, formation and classification of technological innovation network. R&D Management, v. 23, n. 3, p. 9-14, 2011.

Fitjar, R. D.; Rodríguezpose, A. When local interaction does not suffice: Sources of firm innovation in Urban Norway. Environment & Planning A, v. 43, n. 6, p. 1248-1267, 2011.

Feng, L. J. et al. The evolution path of latecomer firms’ value network from the perspective of disruptive innovation. Studies in Science of Science, n.1, p.175-183, 2019.

Freeman, L. C. Centrality in social networks conceptual clarification. Social Networks, v. 1, n. 3, p. 215-239, 1978.

Freeman, C. Networks of innovators: a synthesis of research issues. Policy Research, v. 20, n. 5, p. 499-514, 1991.

Guan, J. C.; Yam, R. C. M. Effects of government financial incentives on firms’ innovation performance in China: Evidences from Beijing in the 1990s. Research Policy, v. 44, n. 1, p. 273-282, 2015.

Hua, L.; Wang, W. P. Optimistic structures of collective innovation networks in different stages of industry life circle. Chinese Journal of Management Science, v. 21, n. 5, p. 129-140, 2013.

Karna, A.; Taübe, F.; Sonderegger, P. Evolution of innovation networks across geographical and organizational boundaries: a study of R&D subsidiaries in the Bangalore IT cluster. European Management Review, v. 10, n. 4, p. 211-226, 2013.

Kim, H.; Song, J. S. Social network analysis of patent infringement lawsuits. Technological Forecasting & Social Change, v. 80, n. 5, p. 944-955, 2013.

Liu, Y.; Zhou, Y. F.; An, J. Analysis of core technology domain of electric vehicle based on patent co-citation. Journal of Information Technology, n. 3, p. 328-336, 2013.

Lee, C.; Park, G.; Kang, J. The impact of convergence between science and technology on innovation. Journal of Technology Transfer, v. 43, n. 2, p. 1-23, 2018.

Liu, Y. F. et al. Multi-domain knowledge convergence trajectory analysis of strategic emerging industries-based on citation network and text information. Strategic Study of CAE, v. 22, n.2, p.120-129, 2020.

Liu, Y. L.; Jian, L. R. Comparative study on the similarities and differences in the evolution of e-commerce data technology between China and the United Sates-from the perspective of dynamic patent directed network analysis. Journal of Intelligence, v. 41, n. 8, p. 201-207, 2022.

Ma, T. Q. Patent analysis: methods, chart interpretation and intelligence mining. Intellectual Property Press, 2015.

Mario, A. M.; Teodora, E. U.; Stefano, U. Treating patents as relational data: Knowledge transfers and spillovers across Italian Provinces. Industry & Innovation, 2011, v. 18, n. 1, p. 39-67, 2011.

Marra, A.; Antonelli, P.; Pozzi, C. Emerging green-tech specializations and clusters-a network analysis on technological innovation at the metropolitan level. Renewable & Sustainable Energy Reviews, v. 67, p. 1037-1046, 2017.

Muller, E. Delimiting disruption: Why Uber is disruptive, but Airbnb is not? International Journal of Research in Marketing, v. 37, n. 1, p.43-55, 2020.

Mao, J. Q. et al. Research on the relationship among inventor innovation network embeddedness, network stability and innovation performance. Scientific Decision Making, n. 3, p. 1-16, 2021.

Narula, R.; Santangelo, G. D. Location, collocation and R&D alliances in the European ICT industry. Research Policy, v. 38, n.2, p. 393-403, 2009.

Raffaelli, R. Technology reemergence: Creating new value for old technologies in Swiss mechanical watchmaking, 1970-2008. Administrative Science Quarterly, v. 64, n. 3, p. 576-618, 2019.

Stolpe, M. Determinants of knowledge diffusion as evidenced in patent data: the case of liquid crystal display technology. Research Policy, v. 31, n. 7, p. 1181-1198, 2002.

Shao, Y. F.; Dang, Y.; Wang, S. M. The mechanism of crossover innovation in fuzzy front end of breakthrough innovation. Science & Technology Progress and Policy, n. 22, p. 8-16, 2018.

Shi, W. P. et al. Network power, resource occupation and cooperative behavior: The moderating role of interorganizational

trust and benchmarking effect. Scientific Decision Making, n. 5, p. 25-42, 2020.

Safadi, H.; Johnson, S. L.; Faraj, S. Who contributes knowledge? Core-periphery tension in online innovation communities. Organization Science, v. 32, n. 3, p. 752-775, 2021.

Wang, D. The evolution mechanism of enterprise innovation network. Studies in Science of Science, v. 24, n. 5, p. 790-786, 2006.

Yan, A. M. Research on enterprise niche evaluation index and model construction. Science and Technology Progress and Countermeasures, v. 24, n. 7, p. 156-160, 2007.

Zhai, D. S. et al. Research on Dewinter Patent Information cleaning and labeling model. Chinese Journal of Information, v. 32, n. 8, p. 150-154, 2013.

Zhao, Y. X. et al. Realization mechanism of cross-industry expansion of focal firms based on the innovation ecosystem: A Iongitudinal case study from the perspective of social embeddedness. Nankai Business Review, v. 25, n.6, p.52-63, 2022.

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Published

2024-01-29

How to Cite

Jin, Y., Cao, X., & Ma, H. (2024). Evolution and characteristics of Crossover Innovation Network of Emerging Technologies: a study based on patent data of the self-driving car technology. Transinformação, 36, 1–19. Retrieved from https://puccampinas.emnuvens.com.br/transinfo/article/view/7316

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Section

Data and Information in Online Environments