IDEAS home Printed from https://ideas.repec.org/a/spr/scient/v121y2019i2d10.1007_s11192-019-03235-4.html
   My bibliography  Save this article

Dynamics of collaboration network community and exploratory innovation: the moderation of knowledge networks

Author

Listed:
  • Jingbei Wang

    (Northwestern Polytechnical University)

  • Naiding Yang

    (Northwestern Polytechnical University)

Abstract

Previous studies have explored the effects of network structures on organization’s exploratory innovation from different perspectives. However, few studies focus on the network community, and there still exists a possible tension on the relationship between network community and organization’s exploratory innovation. In an attempt to make theoretical and empirical contributions to the literature, this study addresses the above research gap by focusing on the dynamics of the network community, and developed a research model that explains how the dynamics of network community affect organization’s exploratory innovation. Furthermore, organizations are not only embedded in the collaboration network, but also in the knowledge network, and we further proposed that the configuration of organizational knowledge network has a moderating effect on the above relationships. We mainly focused on the network cohesion of organizational knowledge network and divided it into global cohesion and local cohesion. With the patent data of smartphone collaboration network from year 2004 to 2017, we empirically examined our hypotheses. The estimation results verified the inverted-U-shaped relationship between dynamics of network community and organization’s exploratory innovation. Furthermore, global cohesion of focal organization’s knowledge network moderates the process in the way that when it is at high level, organization’s exploratory innovation can benefit more from a moderate level of dynamics of network community. Nevertheless, local cohesion moderates the process in the way that when it is at low level, organization’s exploratory innovation can benefit more from a moderate level of dynamics of network community.

Suggested Citation

  • Jingbei Wang & Naiding Yang, 2019. "Dynamics of collaboration network community and exploratory innovation: the moderation of knowledge networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(2), pages 1067-1084, November.
    • Handle: RePEc:spr:scient:v:121:y:2019:i:2:d:10.1007_s11192-019-03235-4
    DOI: 10.1007/s11192-019-03235-4
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11192-019-03235-4
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11192-019-03235-4?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Guan, Jiancheng & Liu, Na, 2016. "Exploitative and exploratory innovations in knowledge network and collaboration network: A patent analysis in the technological field of nano-energy," Research Policy, Elsevier, vol. 45(1), pages 97-112.
    2. Wang, Chao-Hung & Hsu, Li-Chang, 2014. "Building exploration and exploitation in the high-tech industry: The role of relationship learning," Technological Forecasting and Social Change, Elsevier, vol. 81(C), pages 331-340.
    3. Liliana Arroyo Moliner & Eva Gallardo-Gallardo & Pedro Gallo de Puelles, 2017. "Understanding scientific communities: a social network approach to collaborations in Talent Management research," Scientometrics, Springer;Akadémiai Kiadó, vol. 113(3), pages 1439-1462, December.
    4. Corey C. Phelps & Ralph Heidl & Anu Wadhwa, 2012. "Networks, knowledge, and knowledge networks: A critical review and research agenda," Post-Print hal-00715591, HAL.
    5. Jo Thori Lind & Halvor Mehlum, 2010. "With or Without U? The Appropriate Test for a U‐Shaped Relationship," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 72(1), pages 109-118, February.
    6. Zhang, Gupeng & Duan, Hongbo & Zhou, Jianghua, 2017. "Network stability, connectivity and innovation output," Technological Forecasting and Social Change, Elsevier, vol. 114(C), pages 339-349.
    7. Isin Guler & Atul Nerkar, 2012. "The impact of global and local cohesion on innovation in the pharmaceutical industry," Strategic Management Journal, Wiley Blackwell, vol. 33(5), pages 535-549, May.
    8. Sanjay K. Arora & Alan L. Porter & Jan Youtie & Philip Shapira, 2013. "Capturing new developments in an emerging technology: an updated search strategy for identifying nanotechnology research outputs," Scientometrics, Springer;Akadémiai Kiadó, vol. 95(1), pages 351-370, April.
    9. Anindya Ghosh & Lori Rosenkopf, 2015. "PERSPECTIVE—Shrouded in Structure: Challenges and Opportunities for a Friction-Based View of Network Research," Organization Science, INFORMS, vol. 26(2), pages 622-631, April.
    10. Hanaki, Nobuyuki & Nakajima, Ryo & Ogura, Yoshiaki, 2010. "The dynamics of R&D network in the IT industry," Research Policy, Elsevier, vol. 39(3), pages 386-399, April.
    11. 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.
    12. Sanghoon Lee & Wonjoon Kim, 2017. "The knowledge network dynamics in a mobile ecosystem: a patent citation analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(2), pages 717-742, May.
    13. Yibo Lyu & Quanshan Liu & Binyuan He & Jingfei Nie, 2017. "Structural embeddedness and innovation diffusion: the moderating role of industrial technology grouping," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(2), pages 889-916, May.
    14. Melissa A. Schilling & Corey C. Phelps, 2007. "Interfirm Collaboration Networks: The Impact of Large-Scale Network Structure on Firm Innovation," Management Science, INFORMS, vol. 53(7), pages 1113-1126, July.
    15. Lionel Nesta & Vincent Mangematin, 1999. "What kind of Knowledge can a firm absorb?," Post-Print hal-03471555, HAL.
    16. Guiyang Zhang & Chaoying Tang, 2018. "How R&D partner diversity influences innovation performance: an empirical study in the nano-biopharmaceutical field," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(3), pages 1487-1512, September.
    17. Lee Fleming, 2001. "Recombinant Uncertainty in Technological Search," Management Science, INFORMS, vol. 47(1), pages 117-132, January.
    18. Yan Yan & Jiancheng Guan, 2018. "How multiple networks help in creating knowledge: evidence from alternative energy patents," Scientometrics, Springer;Akadémiai Kiadó, vol. 115(1), pages 51-77, April.
    19. Li, Eldon Y. & Liao, Chien Hsiang & Yen, Hsiuju Rebecca, 2013. "Co-authorship networks and research impact: A social capital perspective," Research Policy, Elsevier, vol. 42(9), pages 1515-1530.
    20. Michael J. Barber & Thomas Scherngell, 2013. "Is the European R&D Network Homogeneous? Distinguishing Relevant Network Communities Using Graph Theoretic and Spatial Interaction Modelling Approaches," Regional Studies, Taylor & Francis Journals, vol. 47(8), pages 1283-1298, September.
    21. Hyunseok Park & Janghyeok Yoon, 2014. "Assessing coreness and intermediarity of technology sectors using patent co-classification analysis: the case of Korean national R&D," Scientometrics, Springer;Akadémiai Kiadó, vol. 98(2), pages 853-890, February.
    22. Yan, Yan & Guan, JianCheng, 2018. "Social capital, exploitative and exploratory innovations: The mediating roles of ego-network dynamics," Technological Forecasting and Social Change, Elsevier, vol. 126(C), pages 244-258.
    23. Lewis, Kyle & Belliveau, Maura & Herndon, Benjamin & Keller, Joshua, 2007. "Group cognition, membership change, and performance: Investigating the benefits and detriments of collective knowledge," Organizational Behavior and Human Decision Processes, Elsevier, vol. 103(2), pages 159-178, July.
    24. Powers, Joshua B. & McDougall, Patricia, 2005. "Policy orientation effects on performance with licensing to start-ups and small companies," Research Policy, Elsevier, vol. 34(7), pages 1028-1042, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Bing Feng & Kaiyang Sun & Ziqi Zhong & Min Chen, 2021. "The Internal Connection Analysis of Information Sharing and Investment Performance in the Venture Capital Network Community," IJERPH, MDPI, vol. 18(22), pages 1-16, November.
    2. Guiyang Zhang, 2021. "Employee co-invention network dynamics and firm exploratory innovation: the moderation of employee co-invention network centralization and knowledge-employee network equilibrium," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(9), pages 7811-7836, September.
    3. Ba, Zhichao & Mao, Jin & Ma, Yaxue & Liang, Zhentao, 2021. "Exploring the effect of city-level collaboration and knowledge networks on innovation: Evidence from energy conservation field," Journal of Informetrics, Elsevier, vol. 15(3).
    4. Wang, Wenjing & Lu, Shan, 2021. "University-industry innovation community dynamics and knowledge transfer: Evidence from China," Technovation, Elsevier, vol. 106(C).
    5. Liu, Weiwei & Song, Yifan & Bi, Kexin, 2021. "Exploring the patent collaboration network of China's wind energy industry: A study based on patent data from CNIPA," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    6. Wenjing Wang & Yiwei Liu, 2022. "Does University-industry innovation community affect firms’ inventions? The mediating role of technology transfer," The Journal of Technology Transfer, Springer, vol. 47(3), pages 906-935, June.
    7. Wenjing Wang & Yiwei Liu, 2021. "Community-level characteristics and member firms’ invention: evidence from university–industry innovation community in China," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(11), pages 8913-8934, November.
    8. Jiuling Xiao & Yuting Bao & Jiankang Wang, 2023. "Which neighbor is more conducive to innovation? The moderating effect of partners’ innovation," The Journal of Technology Transfer, Springer, vol. 48(1), pages 33-67, February.
    9. Runhui Lin & Biting Li & Yanhong Lu & Yalin Li, 2024. "Degree assortativity in collaboration networks and breakthrough innovation: the moderating role of knowledge networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(7), pages 3809-3839, July.
    10. Tian, Yunpei & Li, Gang & Mao, Jin, 2023. "Predicting the evolution of scientific communities by interpretable machine learning approaches," Journal of Informetrics, Elsevier, vol. 17(2).
    11. Jingbei Wang & Yafei Nie & Min Guo & Hui Liu, 2024. "Performance feedback as a determinant of ego-network stability in collaboration networks," Asia Pacific Journal of Management, Springer, vol. 41(2), pages 789-821, June.
    12. Feng, Bing & Sun, Kaiyang & Zhong, Ziqi & Chen, Min, 2021. "The internal connection analysis of information sharing and investment performance in the venture capital network community," LSE Research Online Documents on Economics 112731, London School of Economics and Political Science, LSE Library.
    13. Jingbei Wang & Yafei Nie & Min Guo & Hui Liu, 2024. "Digital Orientation and Innovation Outputs in Collaboration Networks: Inside the Black Box," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 15(1), pages 732-750, March.
    14. Chie Hoon Song, 2023. "Examining the Patent Landscape of E-Fuel Technology," Energies, MDPI, vol. 16(5), pages 1-19, February.
    15. Emilio Abad-Segura & Ana Batlles de la Fuente & Mariana-Daniela González-Zamar & Luis Jesús Belmonte-Ureña, 2020. "Effects of Circular Economy Policies on the Environment and Sustainable Growth: Worldwide Research," Sustainability, MDPI, vol. 12(14), pages 1-27, July.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Guan, Jiancheng & Liu, Na, 2016. "Exploitative and exploratory innovations in knowledge network and collaboration network: A patent analysis in the technological field of nano-energy," Research Policy, Elsevier, vol. 45(1), pages 97-112.
    2. Xiaoxiao Shi & Qingpu Zhang, 2020. "Network inertia and inbound open innovation: is there a bidirectional relationship?," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(2), pages 791-815, February.
    3. Guan, Jiancheng & Yan, Yan & Zhang, Jing Jing, 2017. "The impact of collaboration and knowledge networks on citations," Journal of Informetrics, Elsevier, vol. 11(2), pages 407-422.
    4. Yan Yan & Jiancheng Guan, 2018. "How multiple networks help in creating knowledge: evidence from alternative energy patents," Scientometrics, Springer;Akadémiai Kiadó, vol. 115(1), pages 51-77, April.
    5. Jiuling Xiao & Yuting Bao & Jiankang Wang, 2023. "Which neighbor is more conducive to innovation? The moderating effect of partners’ innovation," The Journal of Technology Transfer, Springer, vol. 48(1), pages 33-67, February.
    6. Guiyang Zhang & Chaoying Tang, 2018. "How R&D partner diversity influences innovation performance: an empirical study in the nano-biopharmaceutical field," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(3), pages 1487-1512, September.
    7. Goossen, Martin C. & Paruchuri, Srikanth, 2022. "Measurement errors and estimation biases with incomplete social networks: replication studies on intra-firm inventor network analysis," Research Policy, Elsevier, vol. 51(1).
    8. Taiye Luo & Zhengang Zhang, 2021. "Multi-network embeddedness and innovation performance of R&D employees," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(9), pages 8091-8107, September.
    9. Jingbei Wang & Min Guo & Hui Liu & Yafei Nie, 2023. "Partners’ partners matter: the effect of partners’ centrality diversity on the focal organization’s innovation outputs," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(3), pages 1547-1565, March.
    10. Zhang, JingJing & Yan, Yan & Guan, JianCheng, 2019. "Recombinant distance, network governance and recombinant innovation," Technological Forecasting and Social Change, Elsevier, vol. 143(C), pages 260-272.
    11. Zhang, Zhengang & Luo, Taiye, 2020. "Network capital, exploitative and exploratory innovations——from the perspective of network dynamics," Technological Forecasting and Social Change, Elsevier, vol. 152(C).
    12. Guiyang Zhang, 2021. "Employee co-invention network dynamics and firm exploratory innovation: the moderation of employee co-invention network centralization and knowledge-employee network equilibrium," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(9), pages 7811-7836, September.
    13. Zakaryan, Arusyak, 2023. "Organizational knowledge networks, search and exploratory invention," Technovation, Elsevier, vol. 122(C).
    14. Jingbei Wang & Yafei Nie & Min Guo & Hui Liu, 2024. "Performance feedback as a determinant of ego-network stability in collaboration networks," Asia Pacific Journal of Management, Springer, vol. 41(2), pages 789-821, June.
    15. Wen, Jinyan & Qualls, William J. & Zeng, Deming, 2021. "To explore or exploit: The influence of inter-firm R&D network diversity and structural holes on innovation outcomes," Technovation, Elsevier, vol. 100(C).
    16. Yao, Li & Li, Jun & Li, Jian, 2020. "Urban innovation and intercity patent collaboration: A network analysis of China’s national innovation system," Technological Forecasting and Social Change, Elsevier, vol. 160(C).
    17. Wenjing Wang & Yiwei Liu, 2022. "Does University-industry innovation community affect firms’ inventions? The mediating role of technology transfer," The Journal of Technology Transfer, Springer, vol. 47(3), pages 906-935, June.
    18. Hongshu Chen & Xinna Song & Qianqian Jin & Ximeng Wang, 2022. "Network dynamics in university-industry collaboration: a collaboration-knowledge dual-layer network perspective," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(11), pages 6637-6660, November.
    19. Ba, Zhichao & Mao, Jin & Ma, Yaxue & Liang, Zhentao, 2021. "Exploring the effect of city-level collaboration and knowledge networks on innovation: Evidence from energy conservation field," Journal of Informetrics, Elsevier, vol. 15(3).
    20. Zhang, Ningning & You, Dingyi & Tang, Le & Wen, Ke, 2023. "Knowledge path dependence, external connection, and radical inventions: Evidence from Chinese Academy of Sciences," Research Policy, Elsevier, vol. 52(4).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:scient:v:121:y:2019:i:2:d:10.1007_s11192-019-03235-4. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.