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中国智能制造企业四维要素资本配置效率研究。

Research on capital allocation efficiencies with four-dimensional factor capitals from China's intelligent manufacturing enterprises.

机构信息

Aliyun School of Big Data, Changzhou University, Changzhou, Jiangsu, China.

School of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu, China.

出版信息

PLoS One. 2022 Jul 21;17(7):e0270588. doi: 10.1371/journal.pone.0270588. eCollection 2022.

Abstract

Compared with traditional manufacturing enterprises, intelligent manufacturing enterprises pay more attention to the investment of knowledge capital and technological capital. Taking 258 intelligent manufacturing listed companies in China from 2015 to 2020 as research samples, the paper selects the material capital, human capital, knowledge capital and technological capital of enterprises as the input variables of Cobb-Douglas production function. Considering that enterprises are often affected by spatial correlation, stochastic frontier panel model, spatial lag stochastic frontier panel model and dynamic spatial lag stochastic frontier panel model are constructed to measure capital allocation efficiencies of enterprises. The results show that all the factor capitals in the three models have a significant positive impact on enterprises' performance, and the dual lag effect of time and space is significant. Moreover, it is more reasonable to use the dynamic spatial lag stochastic frontier panel model to estimate the parameters and measure capital allocation efficiencies. The development of intelligent manufacturing industry has significant space-time spillover effect among provinces. About 52.98% of intelligent manufacturing enterprises have high capital allocation efficiencies, but 12.04% still need to further optimize capital allocation. The gap between the actual performance of the sample enterprises and efficiency frontier is mainly due to technical ineffectiveness. From a regional perspective, the top ten enterprises with high capital allocation efficiencies are all in the eastern region, but the average of capital allocation efficiency is the highest in the western region, followed by the eastern and central regions.

摘要

与传统制造企业相比,智能制造企业更加注重知识资本和技术资本的投入。本文选取 2015-2020 年中国 258 家智能制造上市企业为研究样本,选取企业的物质资本、人力资本、知识资本和技术资本作为柯布-道格拉斯生产函数的投入变量。考虑到企业往往受到空间相关性的影响,构建了随机前沿面板模型、空间滞后随机前沿面板模型和动态空间滞后随机前沿面板模型来衡量企业的资本配置效率。结果表明,三个模型中的所有要素资本对企业绩效均有显著的正向影响,且时间和空间的双重滞后效应显著。此外,使用动态空间滞后随机前沿面板模型来估计参数和衡量资本配置效率更为合理。智能制造产业在省际间具有显著的时空溢出效应。约 52.98%的智能制造企业具有较高的资本配置效率,但仍有 12.04%需要进一步优化资本配置。样本企业的实际表现与效率前沿之间的差距主要是由于技术无效。从区域角度来看,资本配置效率较高的前十家企业均位于东部地区,但资本配置效率的平均值最高的是西部地区,其次是东部和中部地区。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d937/9578723/2e4ddb09c1cf/pone.0270588.g001.jpg

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