Skip to main content
SpringerLink
Log in

Technology cluster coupling and invulnerability of industrial innovation networks: the role of centralized structure and technological turbulence

  • Published:
Scientometrics Aims and scope Submit manuscript

Abstract

The high failure rate of industrial innovation networks restrains organizations and industries from successfully developing innovation capacity and competitiveness. Given the trend of technology convergence, technology cluster coupling arguably makes a particularly important contribution to network invulnerability. This study examines how technology cluster coupling consolidates network invulnerability at the network level and examines the relevant dynamics under conditions of technological turbulence. Based on a longitudinal patent dataset from the renewable energy industry, we conduct patent network analysis and hierarchical regression analysis. The results show that centralized structure plays a partly negative mediating role in the positive relationship between technology cluster coupling and network invulnerability, and technological turbulence plays a negative moderating role in that relationship. This study responds to the appeal to explore the impact of community interaction on network-level outcomes and risk management in the innovation network, highlighting the critical role of centralized structure and shedding light on the moderating effect of technological turbulence. Our findings offer implications for industrial policymakers seeking to govern technology clusters aimed at strengthening the invulnerability of industrial innovation networks in environments with different degrees of technological turbulence.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  • Ahuja, G., Polidoro, F., Jr., & Mitchell, W. (2009). Structural homophily or social asymmetry? The formation of alliances by poorly embedded firms. Strategic Management Journal, 30(9), 941–958.

    Article  Google Scholar 

  • Anbumozhi, V., Gunjima, T., Ananth, A. P., & Visvanathan, C. (2010). An assessment of inter-firm networks in a wood biomass industrial cluster: Lessons for integrated policymaking. Clean Technologies and Environmental Policy, 12(4), 365–372.

    Article  Google Scholar 

  • Baldwin, R. E., & Robert-Nicoud, F. (2007). Entry and asymmetric lobbying: Why governments pick losers. Journal of the European Economic Association, 5(5), 1064–1093.

    Article  Google Scholar 

  • Balland, P., & Rigby, D. L. (2017). The geography of complex knowledge. Economic Geography, 93(1), 1–23.

    Article  Google Scholar 

  • Balland, P. A., Suire, R., & Vicente, J. (2013). Structural and geographical patterns of knowledge networks in emerging technological standards: Evidence from the European GNSS industry. Economics of Innovation and New Technology, 22(1), 47–72.

    Article  Google Scholar 

  • Barabási, A. L., & Albert, R. (1999). Emergence of scaling in random networks. Science, 286(5439), 509–512.

    Article  MathSciNet  MATH  Google Scholar 

  • Baron, R. M., & Kenny, D. A. (1986). The moderator–mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51(6), 1173.

    Article  Google Scholar 

  • Beckman, C. M., Haunschild, P. R., & Phillips, D. J. (2004). Friends or strangers? Firm-specific uncertainty, market uncertainty, and network partner selection. Organization Science, 15(3), 259–275.

    Article  Google Scholar 

  • Behfar, S. K., Turkina, E., & Burger-Helmchen, T. (2018). Knowledge management in OSS communities: Relationship between dense and sparse network structures. International Journal of Information Management, 38(1), 167–174.

    Article  Google Scholar 

  • Beekun, R. I., & Glick, W. H. (2001). Organization structure from a loose coupling perspective: A multidimensional approach. Decision Sciences, 32(2), 227–250.

    Article  Google Scholar 

  • Boschma, R. (2015). Towards an evolutionary perspective on regional resilience. Regional Studies, 49(5), 733–751.

    Article  Google Scholar 

  • Breiger, R. L., Boorman, S. A., & Arabie, P. (1975). An algorithm for blocking relational data, with applications to social network analysis and comparison with multidimensional scaling. Journal of Mathematical Psychology, 12, 328–383.

    Article  Google Scholar 

  • Buganza, T., Dell’Era, C., & Verganti, R. (2009). Exploring the relationships between product development and environmental turbulence: The case of mobile TLC services. Journal of Product Innovation Management, 26(3), 308–321.

    Article  Google Scholar 

  • Cattani, G., & Ferriani, S. (2008). A core/periphery perspective on individual creative performance: Social networks and cinematic achievements in the Hollywood film industry. Organization Science, 19(6), 824–844.

    Article  Google Scholar 

  • Choe, H., Lee, D. H., Seo, I. W., & Kim, H. D. (2013). Patent citation network analysis for the domain of organic photovoltaic cells: Country, institution, and technology field. Renewable and Sustainable Energy Reviews, 26, 492–505.

    Article  Google Scholar 

  • Corsaro, D., Cantù, C., & Tunisini, A. (2012). Actors’ heterogeneity in innovation networks. Industrial Marketing Management, 41(5), 780–789.

    Article  Google Scholar 

  • Cowan, R., Jonard, N., & Zimmermann, J. B. (2007). Bilateral collaboration and the emergence of innovation networks. Management Science, 53(7), 1051–1067.

    Article  Google Scholar 

  • Crespo, J., Suire, R., & Vicente, J. (2014). Lock-in or lock-out? How structural properties of knowledge networks affect regional resilience. Journal of Economic Geography, 14(1), 199–219.

    Article  Google Scholar 

  • Crespo Cepas, J., Suire, R., & Vicente, J. (2016). Network structural properties for cluster long-run dynamics. Industrial and Corporate Change, 25(2), 261–282.

    Article  Google Scholar 

  • Crespo, J., Suire, R., & Vicente, J. (2015). Network structural properties for cluster long-run dynamics: Evidence from collaborative R&D networks in the European mobile phone industry. Industrial and Corporate Change, 25(2), 261–282.

    Article  Google Scholar 

  • Cummings, J. N., & Cross, R. (2003). Structural properties of work groups and their consequences for performance. Social Networks, 25(3), 197–210.

    Article  Google Scholar 

  • de Vaan, M. (2014). Interfirm networks in periods of technological turbulence and stability. Research Policy, 43(10), 1666–1680.

    Article  Google Scholar 

  • Dhanaraj, C., & Parkhe, A. (2006). Orchestrating innovation networks. Academy of Management Review, 31(3), 659–669.

    Article  Google Scholar 

  • Duranton, G. (2011). California dreamin’: The feeble case for cluster policies. Review of Economic Analysis, 3(1), 3–45.

    Google Scholar 

  • Eddleston, K. A., Otondo, R. F., & Kellermanns, F. W. (2008). Conflict, participative decision-making, and generational ownership dispersion: A multilevel analysis. Journal of Small Business Management, 46(3), 456–484.

    Article  Google Scholar 

  • Easterby-Smith, M., Lyles, M. A., & Tsang, E. W. (2008). Inter-organizational knowledge transfer: Current themes and future prospects. Journal of Management Studies, 45(4), 677–690.

    Article  Google Scholar 

  • Emden, Z., Calantone, R. J., & Droge, C. (2006). Collaborating for new product development: Selecting the partner with maximum potential to create value. Journal of Product Innovation Management, 23(4), 330–341.

    Article  Google Scholar 

  • Everett, M. G., & Borgatti, S. P. (1999). The centrality of groups and classes. The Journal of mathematical sociology, 23(3), 181–201.

    Article  MATH  Google Scholar 

  • Falck, O., Heblich, S., & Kipar, S. (2010). Industrial innovation: Direct evidence from a cluster-oriented policy. Regional Science and Urban Economics, 40(6), 574–582.

    Article  Google Scholar 

  • Fleming, L., & Waguespack, D. M. (2007). Brokerage, boundary spanning, and leadership in open innovation communities. Organization Science, 18(2), 165–180.

    Article  Google Scholar 

  • Foxon, T. J., Gross, R., Chase, A., Howes, J., Arnall, A., & Anderson, D. (2005). UK innovation systems for new and renewable energy technologies: Drivers, barriers and systems failures. Energy Policy, 33(16), 2123–2137.

    Article  Google Scholar 

  • Gay, B., & Dousset, B. (2005). Innovation and network structural dynamics: Study of the alliance network of a major sector of the biotechnology industry. Research Policy, 34(10), 1457–1475.

    Article  Google Scholar 

  • Gilsing, V., Vanhaverbeke, W., & Pieters, M. (2014). Mind the gap: Balancing alliance network and technology portfolios during periods of technological uncertainty. Technological Forecasting and Social Change, 81, 351–362.

    Article  Google Scholar 

  • Goerzen, A. (2007). Alliance networks and firm performance: The impact of repeated partnerships. Strategic Management Journal, 28(5), 487–509.

    Article  Google Scholar 

  • Granovetter, M. S. (1973). The strength of weak ties. American Journal of Sociology, 78(6), 1360–1380.

    Article  Google Scholar 

  • Guan, J., Zhang, J., & Yan, Y. (2015). The impact of multilevel networks on innovation. Research Policy, 44(3), 545–559.

    Article  Google Scholar 

  • Gulati, R., & Sytch, M. (2008). Does familiarity breed trust? Revisiting the antecedents of trust. Managerial and Decision Economics, 29(2–3), 165–190.

    Article  Google Scholar 

  • Gulati, R., & Gargiulo, M. (1999). Where do interorganizational networks come from? American Journal of Sociology, 104(5), 1439–1493.

    Article  Google Scholar 

  • Graf, H., & Broekel, T. (2020). A shot in the dark? Policy influence on cluster networks. Research Policy, 49 (3), 103920.

  • Ha, S. H., Liu, W., Cho, H., & Kim, S. H. (2015). Technological advances in the fuel cell vehicle: Patent portfolio management. Technological Forecasting and Social Change, 100, 277–289.

    Article  Google Scholar 

  • Heidenreich, S., Landsperger, J., & Spieth, P. (2016). Are innovation networks in need of a conductor? Examining the contribution of network managers in low and high complexity settings. Long Range Planning, 49(1), 55–71.

    Article  Google Scholar 

  • Jaffe, A. B., Trajtenberg, M., & Henderson, R. (1993). Geographic localization of knowledge spillovers as evidenced by patent citations. Quarterly Journal of Economics, 108(3), 577–598.

    Article  Google Scholar 

  • Jenson, I., Leith, P., Doyle, R., West, J., & Miles, M. P. (2016). The root cause of innovation system problems: Formative measures and causal configurations. Journal of Business Research, 69(11), 5292–5298.

    Article  Google Scholar 

  • Jeong, S., Kim, J.-C., & Choi, J. Y. (2015). Technology convergence: What developmental stage are we in? Scientometrics, 104(3), 841–871.

    Article  Google Scholar 

  • John, C. H., & Pouder, R. W. (2006). Technology clusters versus industry clusters: Resources, networks, and regional advantages. Growth Change, 37(2), 141–171.

    Article  Google Scholar 

  • Kang, M. J., & Hwang, J. (2016). Structural dynamics of innovation networks funded by the European Union in the context of systemic innovation of the renewable energy sector. Energy Policy, 96, 471–490.

    Article  Google Scholar 

  • Karim, S., & Kaul, A. (2015). Structural recombination and innovation: Unlocking intraorganizational knowledge synergy through structural change. Organization Science, 26(2), 439–455.

    Article  Google Scholar 

  • Kenis, P., & Knoke, D. (2002). How organizational field networks shape interorganizational tie-formation rates. Academy of Management Review, 27(2), 275–293.

    Article  Google Scholar 

  • Klein Woolthuis, R., Lankhuizen, M. & Gilsing, V. (2005) A system failure framework for innovation policy design. Technovation, 25(6), 609–619.

  • Kim, J., & Lee, S. (2015). Patent databases for innovation studies: A comparative analysis of USPTO, EPO, JPO and KIPO. Technological Forecasting and Social Change, 92, 332–345.

    Article  Google Scholar 

  • Latora, V., & Marchiori, M. (2001) Efficient behavior of small-world networks. Physical Review Letters, 87 (19), 198701.

  • Levén, P., Holmström, J., & Mathiassen, L. (2014). Managing research and innovation networks: Evidence from a government sponsored cross-industry program. Research Policy, 43(1), 156–168.

    Article  Google Scholar 

  • Li, Y., Hao, A., Zhang, X., & Xiong, X. (2018). Network topology and systemic risk in Peer-to-Peer lending market. Physica a: Statistical Mechanics and Its Applications, 508, 118–130.

    Article  Google Scholar 

  • Lucena-Piquero, D., & Vicente, J. (2019). The visible hand of cluster policy makers: An analysis of Aerospace Valley (2006–2015) using a place-based network methodology. Research Policy, 48(3), 830–842.

    Article  Google Scholar 

  • Lyu, Y., He, B., Zhu, Y., & Li, L. (2019). Network embeddedness and inbound open innovation practice: The moderating role of technology cluster. Technological Forecasting and Social Change, 144, 12–24.

    Article  Google Scholar 

  • Lyu, Y., Liu, Q., He, B., & Nie, J. (2017). Structural embeddedness and innovation diffusion: The moderating role of industrial technology grouping. Scientometrics, 111(2), 889–916.

    Article  Google Scholar 

  • Mans, P., Alkemade, F., van der Valk, T., & Hekkert, M. P. (2008). Is cluster policy useful for the energy sector? Assessing self-declared hydrogen clusters in the Netherlands. Energy Policy, 36(4), 1375–1385.

    Article  Google Scholar 

  • Mariotti, F., & Delbridge, R. (2012). Overcoming network overload and redundancy in interorganizational networks: The roles of potential and latent ties. Organization Science, 23(2), 511–528.

    Article  Google Scholar 

  • Marrone, J. A., Tesluk, P. E., & Carson, J. B. (2007). A multilevel investigation of antecedents and consequences of team member boundary-spanning behavior. Academy of Management Journal, 50(6), 1423–1439.

    Article  Google Scholar 

  • Mäs, M., Flache, A., Takács, K., & Jehn, K. A. (2013). In the short term we divide, in the long term we unite: Demographic crisscrossing and the effects of fault lines on subgroup polarization. Organization Science, 24(3), 716–736.

    Article  Google Scholar 

  • Negro, S. O., Alkemade, F., & Hekkert, M. P. (2012). Why does renewable energy diffuse so slowly? A review of innovation system problems. Renewable and Sustainable Energy Reviews, 16(6), 3836–3846.

    Article  Google Scholar 

  • Nishimura, J., & Okamuro, H. (2011). Subsidy and networking: The effects of direct and indirect support programs of the cluster policy. Research Policy, 40(5), 714–727.

    Article  Google Scholar 

  • Obstfeld, D. (2005). Social networks, the tertius iungens orientation, and involvement in innovation. Administrative Science Quarterly, 50(1), 100–130.

    Article  Google Scholar 

  • Olson, D. L., Birge, J. R., & Linton, J. (2014). Introduction to risk and uncertainty management in technological innovation. Technovation, 34(8), 395–398.

    Article  Google Scholar 

  • Østergaard, C. R. (2009). Knowledge flows through social networks in a cluster: Comparing university and industry links. Structural Change and Economic Dynamics, 20(3), 196–210.

    Article  Google Scholar 

  • Ozcan, S., & Islam, N. (2014). Collaborative networks and technology clusters: The case of nanowire. Technological Forecasting and Social Change, 82, 115–131.

    Article  Google Scholar 

  • Padula, G. (2008). Enhancing the innovation performance of firms by balancing cohesiveness and bridging ties. Long Range Planning, 41(4), 395–419.

    Article  Google Scholar 

  • Peng, G.-S., & Wu, J. (2016). Optimal network topology for structural robustness based on natural connectivity. Physica a: Statistical Mechanics and Its Applications, 443, 212–220.

    Article  MathSciNet  Google Scholar 

  • Provan, K. G., Fish, A., & Sydow, J. (2007). Interorganizational networks at the network level: A review of the empirical literature on whole networks. Journal of Management, 33(3), 479–516.

    Article  Google Scholar 

  • Popp, D., Santen, N., Fisher-Vanden, K., & Webster, M. (2013). Technology variation vs. R&D uncertainty: What matters most for energy patent success? Resource and Energy Economics, 35(4), 505–533.

  • Porter, M. E. (2000). Location, competition, and economic development: Local clusters in a global economy. Economic Development Quarterly, 14(1), 15–34.

    Article  MathSciNet  Google Scholar 

  • Rampersad, G., Quester, P., & Troshani, I. (2010). Managing innovation networks: Exploratory evidence from ICT, biotechnology and nanotechnology networks. Industrial Marketing Management, 39(5), 793–805.

    Article  Google Scholar 

  • Rocha, H. O., & Sternberg, R. (2005). Entrepreneurship: The role of clusters theoretical perspectives and empirical evidence from Germany. Small Business Economics, 24(3), 267–292.

    Article  Google Scholar 

  • Schilling, M. A. (2015). Technology shocks, technological collaboration, and innovation outcomes. Organization Science, 26(3), 668–686.

    Article  Google Scholar 

  • Schleich, J., Walz, R., & Ragwitz, M. (2017). Effects of policies on patenting in wind-power technologies. Energy Policy, 108, 684–695.

    Article  Google Scholar 

  • Singh, J. (2005). Collaborative networks as determinants of knowledge diffusion patterns. Management Science, 51(5), 756–770.

    Article  MATH  Google Scholar 

  • Sorenson, O., & Waguespack, D. M. (2006). Social structure and exchange: Self-confirming dynamics in Hollywood. Administrative Science Quarterly, 51(4), 560–589.

    Article  Google Scholar 

  • Sorenson, O., Rivkin, J. W., & Fleming, L. (2006). Complexity, networks and knowledge flow. Research Policy, 35(7), 994–1017.

    Article  Google Scholar 

  • Stuck, J., Broekel, T., & Revilla Diez, J. (2016). Network structures in regional innovation systems. European Planning Studies, 24(3), 423–442.

    Article  Google Scholar 

  • Suire, R., & Vicente, J. (2014). Clusters for life or life cycles of clusters: In search of the critical factors of clusters’ resilience. Entrepreneurship & Regional Development, 26(1–2), 142–164.

    Article  Google Scholar 

  • Sytch, M., & Tatarynowicz, A. (2014). Exploring the locus of invention: The dynamics of network communities and firms’ invention productivity. Academy of Management Journal, 57(1), 249–279.

    Article  Google Scholar 

  • Sytch, M., Tatarynowicz, A., & Gulati, R. (2012). Toward a theory of extended contact: The incentives and opportunities for bridging across network communities. Organization Science, 23(6), 1658–1681.

    Article  Google Scholar 

  • Ter Wal, A. L., & Boschma, R. (2011). Co-evolution of firms, industries and networks in space. Regional Studies, 45(7), 919–933.

    Article  Google Scholar 

  • Thomas, B., Uwe, C., & Holger, G. (2011). Innovation networks: Measurement, performance and regional dimensions. Industry & Innovation, 18(1), 1–5.

    Article  Google Scholar 

  • van Aswegen, M. & Retief, F. P. (2020) The role of innovation and knowledge networks as a policy mechanism towards more resilient peripheral regions. Land Use Policy, 90, 104259.

  • van der Valk, T., Chappin, M. M. H., & Gijsbers, G. W. (2011). Evaluating innovation networks in emerging technologies. Technological Forecasting and Social Change, 78(1), 25–39.

    Article  Google Scholar 

  • Vicente, J. (2014) 'Don't Throw the Baby Out with the Bath Water': Network Failures and Policy Challenges for Cluster Long Run Dynamics. Papers in Evolutionary Economic Geography.

  • Vicente, J. (2018) Economics of clusters. Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-78870-8.

  • Vicente, J., Balland, P., & Brossard, O. (2011). Getting into Networks and Clusters: Evidence from the Midi-Pyrenean Global Navigation Satellite Systems (GNSS) Collaboration Network. Regional Studies, 45(8), 1059–1078.

    Article  Google Scholar 

  • Voudouris, I., Lioukas, S., Iatrelli, M., & Caloghirou, Y. (2012). Effectiveness of technology investment: Impact of internal technological capability, networking and investment’s strategic importance. Technovation, 32(6), 400–414.

    Article  Google Scholar 

  • Wal, A. L. J. T., & Boschma, R. (2009). Applying social network analysis in economic geography: Framing some key analytic issues. Annals of Regional Science, 43(3), 739–756.

    Article  Google Scholar 

  • Wong, C., Hu, M., & Shiu, J. (2015). Governing the economic transition: How Taiwan transformed its industrial system to attain virtuous cycle development. Review of Policy Research, 32(3), 365–387.

    Article  Google Scholar 

  • Yin, R.-R., Liu, B., Liu, H.-R., & Li, Y.-Q. (2016). Research on invulnerability of the random scale-free network against cascading failure. Physica A: Statistical Mechanics and Its Applications, 444, 458–465.

    Article  Google Scholar 

  • Yun, S., Lee, J., & Lee, S. (2019). Technology development strategies and policy support for the solar energy industry under technological turbulence. Energy Policy, 124, 206–214.

    Article  Google Scholar 

  • Yunming, W., Si, C., Chengsheng, P., & Bo, C. (2018). Measure of invulnerability for command and control network based on mission link. Information Sciences, 426, 148–159.

    Article  Google Scholar 

  • Zhang, Y., & Li, H. (2010). Innovation search of new ventures in a technology cluster: The role of ties with service intermediaries. Strategic Management Journal, 31(1), 88–109.

    Article  MathSciNet  Google Scholar 

  • Zhang, Y., & Yang, N. (2013). Research on robustness of R&D network under cascading propagation of risk with gray attack information. Reliability Engineering & System Safety, 117, 1–8.

    Article  Google Scholar 

Download references

Acknowledgements

We would like to thank the anonymous reviewers for their comments on theoretical foundations and policy design. We are also very grateful to the two doctoral supervisors for their guidance on the conception and design of this paper, and Lining Bao for her discussion of data acquisition.

Funding

This study was funded by grants from the National Natural Science Foundation of China (Nos. 71572026, 71632004 and 71872026); and the Youth Top Talent Project to revitalize Liaoning province in China (No. XLYC1907125); and the Doctoral Fund of Zhengzhou University of Light Industry under Grant number (2021BSJJ050).

Author information

Authors and Affiliations

  1. College of Economics and Management, Zhengzhou University of Light Industry, Zhengzhou, China

    Li Li

  2. School of Economics and Management, Ganjingzi District, Dalian University of Technology, No. 2 Linggong Road, Dalian City, Liaoning Province, China

    Haifen Lin

  3. Jiangnan University, Wuxi, China

    Yibo Lyu

Authors
  1. Li Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Haifen Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yibo Lyu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yibo Lyu.

Ethics declarations

Conflict of interest

The authors declare that there are no conflicts of interest.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, L., Lin, H. & Lyu, Y. Technology cluster coupling and invulnerability of industrial innovation networks: the role of centralized structure and technological turbulence. Scientometrics 127, 1209–1231 (2022). https://doi.org/10.1007/s11192-022-04269-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11192-022-04269-x

Keywords

Mathematics subject classification

JEL classification

Search

Navigation