 Big data application, factor allocation, and green innovation in Chinese manufacturing enterprisesQiang Gao, Changming Cheng and Guanglin Sun Technological Forecasting and Social Change, 2023, vol. 192, issue C 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. Keywords: Big data; Manufacture enterprises; Capital allocation; Labor allocation; Green innovation (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:192:y:2023:i:c:s0040162523002524 DOI: 10.1016/j.techfore.2023.122567 Access Statistics for this article Technological Forecasting and Social Change is currently edited by Fred Phillips More articles in Technological Forecasting and Social Change from Elsevier |
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