School of Management, University of Shanghai for Science and Technology

为了衡量智能制造企业公司治理能力,推动企业持续发展,文中选取2017—2021年84家百强上市智能制造企业作为研究对象,运用数据包络分析模型,测算其公司治理效率,并运用Tobit回归模型研究公司治理效率的影响因素,构建公司治理效率评价模型。结果表明:在静态分析层面上,只有少数智能制造企业达到DEA有效,规模效率成为制约智能制造企业公司治理效率的关键因素。在动态分析层面上,技术效率的提高,使智能制造企业全要素生产率上升0.3%。在影响因素分析中,共发现9个显著影响因素。

In order to measure the corporate governance ability of intelligent manufacturing enterprisesand promote their sustainable development, 84 top 100 listed intelligent manufacturing enterprises from2017 to 2021 are selected as research objects in this paper. The data enveloping analysis model is usedto measure their corporate governance efficiency, and the Tobit regression model is used to study the influencing factors of corporate governance efficiency, and the evaluation model of corporate governanceefficiency is built. The results show that at the static analysis level, only a few intelligent manufacturingenterprises achieve DEA efficiency in static analysis, and scale efficiency becomes the key factor restricting the corporate governance efficiency of intelligent manufacturing enterprises; and at the dynamicanalysis level, the total factor productivity of intelligent manufacturing enterprises rose 0. 3% . In the analysis of influencing factors, a total of 9 significant influencing factors were found.

国家自然科学基金项目(71871144)