人工智能企业创新效率评价及影响因素研究——基于Super DEA-Tobit模型

Research on Evaluation and Influencing Factors of Innovation Efficiency of Artificial Intelligence Enterprises:Based on Super DEA-Tobit Model

文章运用超效率DEA与Malmquist指数分别从静态和动态两个层面对人工智能上市企业创新效率进行测度,并进一步利用Tobit回归和脉冲响应对人工智能企业创新效率影响因素进行探究。研究发现:创新效率整体呈现波动下降趋势,且民营企业与国有企业创新效率存在差异性;创新效率进步得益于规模效率增长,而创新效率降低主要受到纯技术效率下降的影响;技术进步和纯技术效率下降分别是制约国有和民营人工智能企业创新效率提升的主要因素;政府支持对创新效率提升具有持续促进作用,而税收负担具有持续抑制作用。

The super-efficiency DEA and Malmquist index are used to measure the innovation efficiency of artificial intelligence listed companies from static and dynamic levels,and the Tobit regression and impulse response are further used to explore the factors affecting the innovation efficiency of artificial intelligence companies.The research found:The overall innovation efficiency shows a fluc-tuating downward trend,and there are differences in innovation efficiency between private enterprises and state-owned enterprises.The improvement of innovation efficiency benefits from the increase of scale efficiency,while the decrease of innovation efficiency is mainly affected by the decline of pure technical efficiency.Technological progress and decline in pure technical efficiency are respectively the main factors restricting the improvement of the innovation efficiency of state-owned and private artificial intelligence enterprises.Gover-nment support has a continuous effect on the improvement of innovation efficiency,and the tax burden has a continuous inhibitory effect.