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Research on the formation mechanism of big data technology cooperation networks: empirical evidence from China

Author

Listed:
  • Yonghong Ma

    (Harbin Engineering University)

  • Xiaomeng Yang

    (Harbin Engineering University)

  • Sen Qu

    (Harbin Engineering University)

  • Lingkai Kong

    (Harbin Engineering University)

Abstract

The purpose of the present paper is to investigate the formation mechanism of big data technology cooperation networks by considering the combined effect of three key factors, i.e., the individual characteristics, relationship characteristics, and cooperation characteristics of research and development (R&D) entities. The utilized research data come from Chinese big data technology cooperation patents granted during the years 2009–2018. In this paper, an exponential random graph model is applied to study the impact of different indicators on a big data technology cooperation network. The results show that R&D capability and structural holes in the individual characteristics of R&D entities have negative impacts on the formation of big data technology cooperation networks. In contrast, R&D entities with a high degree centrality are beneficial to the development of big data technology cooperation networks. Regarding relationship characteristics, a trend of geographical homogeneity is obvious in the formation process of the examined big data technology cooperation network, while the effect of organisational homogeneity is nonsignificant. In terms of cooperation characteristics, the network tends to facilitate convergent cooperation and transitive cooperation rather than intermediary cooperation. The present results provide a scientific reference for entities working to build effective cooperation relationships and promote the sustainable development of big data technology.

Suggested Citation

  • Yonghong Ma & Xiaomeng Yang & Sen Qu & Lingkai Kong, 2022. "Research on the formation mechanism of big data technology cooperation networks: empirical evidence from China," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(3), pages 1273-1294, March.
    • Handle: RePEc:spr:scient:v:127:y:2022:i:3:d:10.1007_s11192-022-04270-4
    DOI: 10.1007/s11192-022-04270-4
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    References listed on IDEAS

    as
    1. Xu Bai & Jinxi Wu & Yun Liu & Yihan Xu, 2020. "Research on the impact of global innovation network on 3D printing industry performance," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(2), pages 1015-1051, August.
    2. Ranjay Gulati, 1999. "Network location and learning: the influence of network resources and firm capabilities on alliance formation," Strategic Management Journal, Wiley Blackwell, vol. 20(5), pages 397-420, May.
    3. Cimenler, Oguz & Reeves, Kingsley A. & Skvoretz, John, 2015. "An evaluation of collaborative research in a college of engineering," Journal of Informetrics, Elsevier, vol. 9(3), pages 577-590.
    4. Peng, Tai-Quan, 2015. "Assortative mixing, preferential attachment, and triadic closure: A longitudinal study of tie-generative mechanisms in journal citation networks," Journal of Informetrics, Elsevier, vol. 9(2), pages 250-262.
    5. Kwon, Ohbyung & Lee, Namyeon & Shin, Bongsik, 2014. "Data quality management, data usage experience and acquisition intention of big data analytics," International Journal of Information Management, Elsevier, vol. 34(3), pages 387-394.
    6. Liming Zhao & Haihong Zhang & Wenqing Wu, 2019. "Cooperative knowledge creation in an uncertain network environment based on a dynamic knowledge supernetwork," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(2), pages 657-685, May.
    7. Guan, Jiancheng & Zhang, Jingjing & Yan, Yan, 2015. "The impact of multilevel networks on innovation," Research Policy, Elsevier, vol. 44(3), pages 545-559.
    8. Tarun Khanna, 1998. "The Scope of Alliances," Organization Science, INFORMS, vol. 9(3), pages 340-355, June.
    9. Tan, Kim Hua & Zhan, YuanZhu & Ji, Guojun & Ye, Fei & Chang, Chingter, 2015. "Harvesting big data to enhance supply chain innovation capabilities: An analytic infrastructure based on deduction graph," International Journal of Production Economics, Elsevier, vol. 165(C), pages 223-233.
    10. Gautam Ahuja, 2000. "The duality of collaboration: inducements and opportunities in the formation of interfirm linkages," Strategic Management Journal, Wiley Blackwell, vol. 21(3), pages 317-343, March.
    11. Steven Goodreau & James Kitts & Martina Morris, 2009. "Birds of a feather, or friend of a friend? using exponential random graph models to investigate adolescent social networks," Demography, Springer;Population Association of America (PAA), vol. 46(1), pages 103-125, February.
    12. Mazzola, Erica & Perrone, Giovanni & Kamuriwo, Dzidziso Samuel, 2015. "Network embeddedness and new product development in the biopharmaceutical industry: The moderating role of open innovation flow," International Journal of Production Economics, Elsevier, vol. 160(C), pages 106-119.
    13. Michael Song & Haili Zhang & Jinjin Heng, 2020. "Creating Sustainable Innovativeness through Big Data and Big Data Analytics Capability: From the Perspective of the Information Processing Theory," Sustainability, MDPI, vol. 12(5), pages 1-23, March.
    14. Agrawal, Ajay & Kapur, Devesh & McHale, John, 2008. "How do spatial and social proximity influence knowledge flows? Evidence from patent data," Journal of Urban Economics, Elsevier, vol. 64(2), pages 258-269, September.
    15. He, Xi-jun & Dong, Yan-bo & Wu, Yu-ying & Jiang, Guo-rui & Zheng, Yao, 2019. "Factors affecting evolution of the interprovincial technology patent trade networks in China based on exponential random graph models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 514(C), pages 443-457.
    16. Pierre-Alexandre Balland & José Antonio Belso-Martínez & Andrea Morrison, 2016. "The Dynamics of Technical and Business Knowledge Networks in Industrial Clusters: Embeddedness, Status, or Proximity?," Economic Geography, Taylor & Francis Journals, vol. 92(1), pages 35-60, January.
    17. Hunter, David R. & Handcock, Mark S. & Butts, Carter T. & Goodreau, Steven M. & Morris, Martina, 2008. "ergm: A Package to Fit, Simulate and Diagnose Exponential-Family Models for Networks," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 24(i03).
    18. Mitchell, Will & Singh, Kulwant, 1992. "Incumbents' use of pre-entry alliances before expansion into new technical subfields of an industry," Journal of Economic Behavior & Organization, Elsevier, vol. 18(3), pages 347-372, August.
    19. Morris, Martina & Handcock, Mark S. & Hunter, David R., 2008. "Specification of Exponential-Family Random Graph Models: Terms and Computational Aspects," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 24(i04).
    20. Geldes, Cristian & Felzensztein, Christian & Turkina, Ekaterina & Durand, Aurélia, 2015. "How does proximity affect interfirm marketing cooperation? A study of an agribusiness cluster," Journal of Business Research, Elsevier, vol. 68(2), pages 263-272.
    21. Ron Boschma, 2005. "Proximity and Innovation: A Critical Assessment," Regional Studies, Taylor & Francis Journals, vol. 39(1), pages 61-74.
    22. Handcock, Mark S. & Hunter, David R. & Butts, Carter T. & Goodreau, Steven M. & Morris, Martina, 2008. "statnet: Software Tools for the Representation, Visualization, Analysis and Simulation of Network Data," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 24(i01).
    23. Gnyawali, Devi R. & Park, Byung-Jin (Robert), 2011. "Co-opetition between giants: Collaboration with competitors for technological innovation," Research Policy, Elsevier, vol. 40(5), pages 650-663, June.
    24. Choe, Hochull & Lee, Duk Hee & Seo, Il Won & Kim, Hee Dae, 2013. "Patent citation network analysis for the domain of organic photovoltaic cells: Country, institution, and technology field," Renewable and Sustainable Energy Reviews, Elsevier, vol. 26(C), pages 492-505.
    25. Brennecke, Julia & Rank, Olaf, 2017. "The firm’s knowledge network and the transfer of advice among corporate inventors—A multilevel network study," Research Policy, Elsevier, vol. 46(4), pages 768-783.
    26. Sheng, Jie & Amankwah-Amoah, Joseph & Wang, Xiaojun, 2017. "A multidisciplinary perspective of big data in management research," International Journal of Production Economics, Elsevier, vol. 191(C), pages 97-112.
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