29 research outputs found

    Robustness test estimation results.

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    This study evaluated 72 universities’ performance innovation during 2011 to 2019 of panel data, using the data envelopment analysis–Malmquist method. The study used benchmark regression to analyse the relationship between digital finance and the universities’ innovation performance. The aim was to improve innovation performance and promote national innovation across countries. According to the results of the empirical analysis, digital finance positively affects innovation performance. That finding was confirmed through advanced robustness test evaluation, such as limited information maximum likelihood, two-stage least squares, and interactive fixed effects. Moreover, based on information theory, the digital finance influence mechanism improves credit demand and financial efficiency. Additionally, innovation performance survived spatial overflow effects. Lastly, the paper concludes with some implications for improving digital financial coverage and constructing innovation networks among universities.</div

    Spatial overflow analysis.

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    This study evaluated 72 universities’ performance innovation during 2011 to 2019 of panel data, using the data envelopment analysis–Malmquist method. The study used benchmark regression to analyse the relationship between digital finance and the universities’ innovation performance. The aim was to improve innovation performance and promote national innovation across countries. According to the results of the empirical analysis, digital finance positively affects innovation performance. That finding was confirmed through advanced robustness test evaluation, such as limited information maximum likelihood, two-stage least squares, and interactive fixed effects. Moreover, based on information theory, the digital finance influence mechanism improves credit demand and financial efficiency. Additionally, innovation performance survived spatial overflow effects. Lastly, the paper concludes with some implications for improving digital financial coverage and constructing innovation networks among universities.</div

    S1 File -

    No full text
    This study evaluated 72 universities’ performance innovation during 2011 to 2019 of panel data, using the data envelopment analysis–Malmquist method. The study used benchmark regression to analyse the relationship between digital finance and the universities’ innovation performance. The aim was to improve innovation performance and promote national innovation across countries. According to the results of the empirical analysis, digital finance positively affects innovation performance. That finding was confirmed through advanced robustness test evaluation, such as limited information maximum likelihood, two-stage least squares, and interactive fixed effects. Moreover, based on information theory, the digital finance influence mechanism improves credit demand and financial efficiency. Additionally, innovation performance survived spatial overflow effects. Lastly, the paper concludes with some implications for improving digital financial coverage and constructing innovation networks among universities.</div

    Mechanism discussion of credit supply and financial efficiency.

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    Mechanism discussion of credit supply and financial efficiency.</p

    Malmquist index results of innovation in universities over 2010 to 2019.

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    Malmquist index results of innovation in universities over 2010 to 2019.</p

    Estimation results of instrumental variables.

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    This study evaluated 72 universities’ performance innovation during 2011 to 2019 of panel data, using the data envelopment analysis–Malmquist method. The study used benchmark regression to analyse the relationship between digital finance and the universities’ innovation performance. The aim was to improve innovation performance and promote national innovation across countries. According to the results of the empirical analysis, digital finance positively affects innovation performance. That finding was confirmed through advanced robustness test evaluation, such as limited information maximum likelihood, two-stage least squares, and interactive fixed effects. Moreover, based on information theory, the digital finance influence mechanism improves credit demand and financial efficiency. Additionally, innovation performance survived spatial overflow effects. Lastly, the paper concludes with some implications for improving digital financial coverage and constructing innovation networks among universities.</div

    Digital finance conceptual model.

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    This study evaluated 72 universities’ performance innovation during 2011 to 2019 of panel data, using the data envelopment analysis–Malmquist method. The study used benchmark regression to analyse the relationship between digital finance and the universities’ innovation performance. The aim was to improve innovation performance and promote national innovation across countries. According to the results of the empirical analysis, digital finance positively affects innovation performance. That finding was confirmed through advanced robustness test evaluation, such as limited information maximum likelihood, two-stage least squares, and interactive fixed effects. Moreover, based on information theory, the digital finance influence mechanism improves credit demand and financial efficiency. Additionally, innovation performance survived spatial overflow effects. Lastly, the paper concludes with some implications for improving digital financial coverage and constructing innovation networks among universities.</div

    Construction of innovation performance index system in universities.

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    Construction of innovation performance index system in universities.</p

    Baseline regression results of the impact of digital finance on the performance of innovation.

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    Baseline regression results of the impact of digital finance on the performance of innovation.</p

    The risk of stroke for DWI positive patients (TSI) and DWI negative patients.

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    <p>Stroke risk for TSI patients was substantially higher than DWI negative patients (P<0.01).</p><p>CI indicates confidence interval.</p><p>The risk of stroke for DWI positive patients (TSI) and DWI negative patients.</p
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