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Big data application, factor allocation, and green innovation in Chinese manufacturing enterprises

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  • Gao, Qiang
  • Cheng, Changming
  • Sun, Guanglin

Abstract

Green innovation is key to promoting the manufacturing industry's green development and transformation. One way to promote green innovation in manufacturing enterprises can be the deep integration of big data. Using data on listed Chinese manufacturing enterprises from 2014 to 2019, we examine the impacts of big data on green innovation and the mechanisms underlying this relationship. Using a panel fixed effects regression model, we find that big data significantly and positively affects green innovation. Internal mechanism analyses reveal that big data improves the manufacturing industry's green innovation by improving the factor allocation efficiency for both labor and capital. The heterogeneity analysis indicates that the promotional effect of big data on green innovation is more prominent in private enterprises than in state-owned enterprises. The government should formulate big data application policies, and provide incentives for the deep integration of big data in manufacturing enterprises to accelerate green innovation.

Suggested Citation

  • Gao, Qiang & Cheng, Changming & Sun, Guanglin, 2023. "Big data application, factor allocation, and green innovation in Chinese manufacturing enterprises," Technological Forecasting and Social Change, Elsevier, vol. 192(C).
    • Handle: RePEc:eee:tefoso:v:192:y:2023:i:c:s0040162523002524
    DOI: 10.1016/j.techfore.2023.122567
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    Citations

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    Cited by:

    1. Hongyi Mao & Jiang Lu, 2023. "Big Data Management Capabilities and Green Innovation: A Dynamic Capabilities View," Sustainability, MDPI, vol. 15(19), pages 1-27, October.
    2. Xu, Chao & Sun, Guanglin & Kong, Tao, 2024. "The impact of digital transformation on enterprise green innovation," International Review of Economics & Finance, Elsevier, vol. 90(C), pages 1-12.
    3. Panpan Liu & Guanghui Han & Haichao Yang & Xiaobo Li, 2024. "A Sustainable Development Study on Innovation Factor Allocation Efficiency and Spatial Correlation Based on Regions along the Belt and Road in China," Sustainability, MDPI, vol. 16(7), pages 1-22, April.
    4. Liu, Feng & Wang, Rongping & Fang, Mingjie, 2024. "Mapping green innovation with machine learning: Evidence from China," Technological Forecasting and Social Change, Elsevier, vol. 200(C).
    5. Sun, Guanglin & Fang, Jiming & Li, Jinning & Wang, Xiaolin, 2024. "Research on the impact of the integration of digital economy and real economy on enterprise green innovation," Technological Forecasting and Social Change, Elsevier, vol. 200(C).
    6. Di Luan & Hongjun Cao & Tongkun Qu, 2023. "How Does Corporate Green Innovation Strategy Translate into Green Innovation Performance Based on Chain Mediation?," Sustainability, MDPI, vol. 15(16), pages 1-20, August.
    7. Sun, Guanglin & Fang, Jiming & Li, Ting & Ai, Yongfang, 2024. "Effects of climate policy uncertainty on green innovation in Chinese enterprises," International Review of Financial Analysis, Elsevier, vol. 91(C).
    8. Wanying Rao & Pingfeng Liu, 2024. "Can Digital Innovation Improve Green Total Factor Productivity: Evidence from Digital Patents of China," Sustainability, MDPI, vol. 16(10), pages 1-21, May.
    9. Liu, Hongda & Zhao, Haifeng & Li, Shiyuan, 2023. "Future social change of manufacturing and service industries: Service-oriented manufacturing under the integration of innovation-flows drive," Technological Forecasting and Social Change, Elsevier, vol. 196(C).

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