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A Study on the Spatial–Temporal Evolution of Innovation Efficiency in Chinese Universities in the Context of the Digital Economy

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  • Qi Gao

    (School of Business Administration and Customs Affairs, Shanghai Customs College, Shanghai 201204, China)

  • Qiang Wang

    (School of Business Administration and Customs Affairs, Shanghai Customs College, Shanghai 201204, China)

Abstract

With the rapid development of knowledge and the digital economy, it is a crucial to understand the role of the digital economy in improving the innovation efficiency of universities. Using the panel data of universities in 31 Chinese provinces from 2013 to 2020, this paper measures the regional innovation efficiency of Chinese universities and examines the impact of the digital economy on universities’ innovation by employing the super-efficiency DEA model along with the Malmquist index, kernel density estimation and Theil index. The analysis shows the following: (1) The digital economy has a significant positive impact on the efficiency of university innovation, but there is still much room for improvement. (2) In terms of the dynamic evolution of innovation efficiency, Chinese university performance shows a trend toward declining innovation efficiency, and the issue of inadequate investment in technical innovation is discovered, which urgently needs to be addressed. The findings of this paper offer empirical support for understanding the relationship between digital economy growth and university innovation productivity with important ramifications for the innovative expansion of higher education institutions in emerging nations.

Suggested Citation

  • Qi Gao & Qiang Wang, 2022. "A Study on the Spatial–Temporal Evolution of Innovation Efficiency in Chinese Universities in the Context of the Digital Economy," Sustainability, MDPI, vol. 15(1), pages 1-14, December.
    • Handle: RePEc:gam:jsusta:v:15:y:2022:i:1:p:39-:d:1009055
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    References listed on IDEAS

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    1. Montresor, Sandro & Vezzani, Antonio, 2015. "The production function of top R&D investors: Accounting for size and sector heterogeneity with quantile estimations," Research Policy, Elsevier, vol. 44(2), pages 381-393.
    2. Andrea Szalavetz, 2020. "Digital transformation – enabling factory economy actors’ entrepreneurial integration in global value chains?," Post-Communist Economies, Taylor & Francis Journals, vol. 32(6), pages 771-792, July.
    3. Jill Johnes, 2006. "Measuring Efficiency: A Comparison of Multilevel Modelling and Data Envelopment Analysis in the Context of Higher Education," Bulletin of Economic Research, Wiley Blackwell, vol. 58(2), pages 75-104, April.
    4. Song, Yi & Zhang, Yangxueying & Zhang, Yijun, 2022. "Economic and environmental influences of resource tax: Firm-level evidence from China," Resources Policy, Elsevier, vol. 77(C).
    5. Zhang, Yijun & Song, Yi, 2022. "Tax rebates, technological innovation and sustainable development: Evidence from Chinese micro-level data," Technological Forecasting and Social Change, Elsevier, vol. 176(C).
    6. Watanabe, Chihiro & Naveed, Nasir & Neittaanmäki, Pekka, 2018. "Digital solutions transform the forest-based bioeconomy into a digital platform industry - A suggestion for a disruptive business model in the digital economy," Technology in Society, Elsevier, vol. 54(C), pages 168-188.
    7. Cristian Barra & Roberto Zotti, 2018. "The contribution of university, private and public sector resources to Italian regional innovation system (in)efficiency," The Journal of Technology Transfer, Springer, vol. 43(2), pages 432-457, April.
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