Research On The Evaluation Of Intelligent Level Of Listed Manufacturing Enterprises

Abstract

This paper takes the listed manufacturing enterprises in China's A-share market from 2010 to 2021 as the research sample. By constructing A multi-dimensional index system, it systematically calculates and analyzes the intelligence level and total factor productivity of manufacturing enterprises. The study selected 9,840 sample data from 820 listed manufacturing enterprises, based on principles such as objectivity and data availability. A level of intelligence has been established, which includes six indicators: hardware foundation, software foundation, talent foundation, capital foundation, R&D intensity, and innovation ability. The evaluation system uses principal component analysis to calculate the intelligence index. Meanwhile, the DEA-Malmquist index method is adopted. With the net value of fixed assets and the total number of employees as input indicators, and the return on main business assets, operating income and net profit as output indicators, the total factor productivity of the enterprise is calculated.

The research finds that from a regional perspective, there are significant differences in the intelligence levels among provinces. Beijing (0.519), Hubei (0.187), and Guangdong (0.177) have the highest intelligence indices, while Gansu (-0.620), Qinghai (-0.625), and Ningxia (-0.825) have the lowest. By region, the eastern region (-0.342) > the central region (-0.408) > the northeastern region (-0.506) > the western region (-0.524), and the overall intelligence index increased from -1.087 to 0.355 from 2010 to 2021.

In terms of total factor productivity, all provinces showed positive growth. Yunnan (11.1%), Xinjiang (11%), and Tianjin (9.7%) had the fastest growth rates, while Anhui (5.4%), Hainan (4.8%), and Qinghai (2.5%) had the slowest growth rates. At the regional level, the western region (8%) > the northeastern region (7%) > the eastern region (6.9%) ≈ the central region (6.8%). Due to the high base, the marginal growth rate of high-tech industries is lower than that of traditional industries. This study provides methodological and empirical support for quantifying the intelligence level of the manufacturing industry and analyzing regional and industry differences, and offers data references for precisely promoting intelligent upgrades.