基于三阶段DEA的人工智能上市企业创新效率评价

Evaluation of Innovation Efficiency of Artificial Intelligence Listed Enterprises Based on Three-stage DEA

摘要:

以中国41家人工智能上市企业为样本,采用三阶段DEA模型对人工智能企业的创新效率进行有效测算。结果表明：企业规模、成立年限、政府补贴、地区对外开放水平和经济发展水平的提高有助于企业创新效率的提升,而股权性质、股权集中度和信息化水平则制约了企业创新效率的改进；清除环境影响和统计噪声后,多数企业的创新效率与纯技术效率上升,但仍处于较低水平；不同类型人工智能企业的创新效率存在差异,应用层企业最高,基础层企业次之,技术层企业最低。为了进一步提升人工智能产业的创新效率,政府应实施差异化战略,加大补贴力度,落实配套政策并加强知识产权保护；企业应扩大研发规模,完善管理机制,加强产学研合作,深化股权结构改革。

Abstract:

Taking 41 artificial intelligence listed companies in China as samples,this paper uses the three-stage DEA model to measure the innovation efficiency of artificial intelligence enterprises effectively. The results show that the improvement of enterprise scale,establishment period,government subsidies,regional opening-up level and economic development level contribute to the improvement of enterprise innovation efficiency,while the improvement of enterprise innovation efficiency is restricted by the nature of equity, ownership concentration and informatization level. After eliminating environmental effects and statistical noise,the innovation efficiency and pure technology efficiency of the vast majority of enterprises increase,but they are still at a lower level. There are differences in the innovation efficiency among different types of artificial intelligence enterprises. The application layer enterprises are the highest,the basic layer enterprises are the second,and the technology layer enterprises are the lowest.In order to further enhance the innovation efficiency of artificial intelligence industry,the government should implement differentiated strategies,increase subsidies,implement supporting policies and strengthen intellectual property protection. Enterprises should expand the scale of R D,improve management mechanisms,strengthen university-institute-industry collaboration,and deepen the reform of shareholding structure.