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The impact of knowledge transfer performance on the artificial intelligence industry innovation network: An empirical study of Chinese firms

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  • Guofeng Shi
  • Zhiyun Ma
  • Jiao Feng
  • Fujin Zhu
  • Xu Bai
  • Bingxiu Gui

Abstract

As a core driving force of the most recent round of industrial transformation, artificial intelligence has triggered significant changes in the world economic structure, profoundly changed our life and way of thinking, and achieved an overall leap in social productivity. This paper aims to examine the effect of knowledge transfer performance on the artificial intelligence industry innovation network and the path artificial intelligence enterprises can take to promote sustainable development through knowledge transfer in the above context. First, we construct a theoretical hypothesis and conceptual model of the innovation network knowledge transfer mechanism within the artificial intelligence industry. Then, we collect data from questionnaires distributed to Chinese artificial intelligence enterprises that participate in the innovation network. Moreover, we empirically analyze the impact of innovation network characteristics, organizational distance, knowledge transfer characteristics, and knowledge receiver characteristics on knowledge transfer performance and verify the hypotheses proposed in the conceptual model. The results indicate that innovation network centrality and organizational culture distance have a significant effect on knowledge transfer performance, with influencing factors including network scale, implicit knowledge transfer, receiver’s willingness to receive, and receiver’s capacity to absorb knowledge. For sustainable knowledge transfer performance on promoting Chinese artificial intelligence enterprises innovation, this paper finally delivers valuable insights and suggestions.

Suggested Citation

  • Guofeng Shi & Zhiyun Ma & Jiao Feng & Fujin Zhu & Xu Bai & Bingxiu Gui, 2020. "The impact of knowledge transfer performance on the artificial intelligence industry innovation network: An empirical study of Chinese firms," PLOS ONE, Public Library of Science, vol. 15(5), pages 1-22, May.
    • Handle: RePEc:plo:pone00:0232658
    DOI: 10.1371/journal.pone.0232658
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    Cited by:

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    2. Jiyuan Dong & Xiumei Lai & Dali Zhao, 2022. "How incentive affects knowledge transfer performance in corporate user community: The mediating roles of users' transfer willingness and ability," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 43(6), pages 2249-2260, September.
    3. Cerchione, Roberto & Centobelli, Piera & Riccio, Emanuela & Abbate, Stefano & Oropallo, Eugenio, 2023. "Blockchain’s coming to hospital to digitalize healthcare services: Designing a distributed electronic health record ecosystem," Technovation, Elsevier, vol. 120(C).
    4. Bienvenido S. Cortes & Zhengyao Ooi, 2023. "Cultural Intelligence, Firm Capabilities, and Performance: The Case of German Subsidiaries in Malaysia," Businesses, MDPI, vol. 3(3), pages 1-15, September.
    5. Jiafu Su & Fengting Zhang & Xuefeng Zhang & Na Zhang & Cong Miao & Quanxin Gan & Yan Xiao, 2024. "Stability Analysis of Collaborative Product Innovation Network Exposed to Knowledge Resource Loss," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 15(1), pages 3380-3408, March.

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